{ "schema_version": "1.0", "slug": "chanticleer", "name": "Chanticleer", "subtitle": "Security and threat-watching", "description": "Security, threat detection, and the protection and reliable operation of critical infrastructure.", "note": "Cases include cybersecurity, defense, public and financial security, natural hazards, and infrastructure. 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The claimed detection is that purpose." }, { "label": "AI contribution", "value": "The only statement that AI detection played a role is a Microsoft-published feature (Microsoft funds ALERTCalifornia) quoting its founder Neal Driscoll. His “nearly 4,000 acres” counterfactual is his estimate. The quoted deputy fire chief is also ALERTCalifornia's deputy director of operations. Cal Fire's incident record and KBAK's same-day report say nothing about how the fire was detected. KBAK attributes the start to an electrical strike." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0313", "title": "ALERTCalifornia cameras and AI may have helped crews rapidly detect and contain Kern County's 52-acre Trotter Fire (August 2023)", "summary": "Trotter Fire, Bear Valley Springs near Tehachapi, 14–17 August 2023, 52 acres; the AI-detection claim and “nearly 4,000 acres” counterfactual come only from ALERTCalifornia's founder via Microsoft.", "date_observed": "2023-08-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Cal Fire's incident record lists the Trotter fire at Trotter Dr and Skyline Drive, west of Tehachapi, Kern County. It started 14 August 2023 at 18:10 UTC under the Kern County Fire Department (not a Cal Fire incident). It burned 52 acres and was 100% contained and extinguished 17 August 2023. It says nothing about how the fire was detected or reported. KBAK (Bakersfield, 14 August 2023) reported a mandatory evacuation for Skyline Drive between Saddleback Drive and Cumberland Road in Bear Valley Springs. It gave an estimated 40 acres, forward progress stopped by 2:17 p.m. and the evacuation lifted by 3:12 p.m. The Kern County Fire Department said the fire “was started by an electrical strike”. A KGET headline the same day blamed lightning and said no homes were damaged. Microsoft's “Unlocked” feature “The first five minutes” (Bruna Mori, 27 August 2026) was published after Microsoft's $5 million contribution to ALERTCalifornia. It included a $3 million Azure grant (announced by ALERTCalifornia on 3 February 2026). The feature says in 2023 ALERTCalifornia's cameras and AI helped crews rapidly detect and contain the Trotter Fire at 52 acres. Without AI-powered imaging, ALERTCalifornia founder Neal Driscoll estimates it may have spread to nearly 4,000 acres. It quotes Zachary Wells, deputy chief of the Kern County Fire Department and deputy director of operations at ALERTCalifornia. The claim is found only in Microsoft and ALERTCalifornia material. No fire-agency, regulator or independent news account of the fire mentions AI detection. None says which agency received the alert or its timing relative to 911 calls.", "category": "protective response", "unexpected": false, "purpose": "Built to detect smoke in camera feeds and alert fire agencies. The claimed detection is that purpose.", "system": "ALERTCalifornia camera network with AI smoke detection (the Microsoft feature does not name the detection software; AIBD-0007 identifies DigitalPath)", "model_version": null, "developer": "ALERTCalifornia, University of California San Diego (camera network); AI detection software supplier not named in the sources", "deployer": "Kern County Fire Department (incident agency); which agency received the AI alert is not stated", "location": "Bear Valley Springs, west of Tehachapi, Kern County, California, United States", "domain": "Wildfire detection and response", "segment": "Public safety / fire services", "benefit_type": "Reduced Risk", "taxonomy_note": "The AI detection and the “nearly 4,000 acres” counterfactual come only from ALERTCalifornia's founder via Microsoft.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 3, "reason": "The fire was held to 52 acres and the evacuation lifted the same afternoon (Cal Fire record; KBAK), a risk removed for the neighborhood. The claim that the camera network detected it first comes only from ALERTCalifornia's founder via Microsoft." }, { "group": "organizational", "depth": 2, "reason": "An early alert to the Kern County Fire Department is described only in the vendor's account. The department's own account mentions no camera alert." } ], "ai_role": { "status": "unassessed", "statement": "The only statement that AI detection played a role is a Microsoft-published feature (Microsoft funds ALERTCalifornia) quoting its founder Neal Driscoll. His “nearly 4,000 acres” counterfactual is his estimate. The quoted deputy fire chief is also ALERTCalifornia's deputy director of operations. Cal Fire's incident record and KBAK's same-day report say nothing about how the fire was detected. KBAK attributes the start to an electrical strike." }, "evidence": [ { "kind": "report", "citation": "California Department of Forestry and Fire Protection (2023). Trotter fire, Kern County, incident record (started 14 August 2023; the incident page is the public view).", "title": "Trotter Fire", "url": "https://www.fire.ca.gov/incidents/2023/8/14/trotter/", "date": "2023-08-17", "written_by": "independent", "quote": "Trotter Dr and Skyline Drive, West of Tehachapi" }, { "kind": "news report", "citation": "KBAK / Bakersfield Now (2023). Evacuation order lifted in Bear Valley Springs following Trotter Fire. 14 August 2023.", "title": "Evacuation order lifted in Bear Valley Springs following Trotter Fire", "url": "https://bakersfieldnow.com/news/local/kern-county-fire-department-orders-evacuation-in-bear-valley-springs-due-to-vegetation-fires-kern-county", "date": "2023-08-14", "written_by": "independent", "quote": "The Kern County Fire Department said that the Trotter Fire was started by an electrical strike." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (Microsoft funds ALERTCalifornia; the source of the AI-detection claim).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "In 2023, ALERTCalifornia's cameras and AI helped crews rapidly detect and contain Kern County's Trotter Fire at 52 acres." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (the counterfactual estimate).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "Without AI-powered imaging, Driscoll estimates it may have spread to nearly 4,000 acres." }, { "kind": "press release", "citation": "ALERTCalifornia (2026). New collaboration expands capabilities and enhances natural disaster resilience. 3 February 2026 (the Microsoft funding announcement; the page does not mention the Trotter Fire).", "title": "New collaboration expands capabilities and enhances natural disaster resilience", "url": "https://alertcalifornia.org/new-collaboration-expands-capabilities-and-enhances-natural-disaster-resilience/", "date": "2026-02-03", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "ALERTCalifornia's camera network with AI smoke detection covering the Tehachapi area", "A fire agency able to act on an early detection. Which agency received this alert is not stated", "A lightning or electrical-strike ignition on 14 August 2023 that crews stopped at 52 acres" ], "must_hold": [ "An alert reaching dispatch before or alongside 911 calls, which no cited source documents for this fire", "A counterfactual spread that depends on fuels, wind and response time. The 4,000-acre figure is an estimate by the network's founder" ], "echoes": [ { "date": "2024-07-06", "setting": "ALERTCalifornia AI camera alert at 2:33 a.m. sends Cal Fire engines to a fire near Auburn before any 911 call; held to a 30 x 30 ft area (AIBD-0007)", "model_version": null, "outcome": "recurred", "source": "AIBD-0007" }, { "date": "2026-05-04", "setting": "Cal Fire battalion chief Brent Pascua, quoted by AP, says responses have started before 911 was called and in a few cases fires were put out with no 911 call ever received (aggregate statement)", "model_version": null, "outcome": "recurred", "source": "Associated Press, 4 May 2026, https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" } ] }, "paired_incident": null, "headline": "ALERTCalifornia AI may have helped contain the Trotter Fire", "metric": "contain the Trotter Fire at 52 acres", "comparator": { "against": "the fire's estimated spread without AI-powered imaging, as estimated by ALERTCalifornia founder Neal Driscoll", "before": "nearly 4,000 acres", "after": "52 acres", "basis": "assumed", "quote": "In 2023, ALERTCalifornia’s cameras and AI helped crews rapidly detect and contain Kern County’s Trotter Fire at 52 acres. Without AI-powered imaging, Driscoll estimates it may have spread to nearly 4,000 acres.", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/" }, "later": null, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 3 for societal and 2 for organizational on the documented outcome, with the reason stated on each rating.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://alertcalifornia.org/about/; https://www.kerncountyfire.org/about-us/contact-us/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report, one independent news report, one independent press release and the makers' press release. 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His “nearly 4,000 acres” counterfactual is his estimate. The quoted deputy fire chief is also ALERTCalifornia's deputy director of operations. Cal Fire's incident record and KBAK's same-day report say nothing about how the fire was detected. KBAK attributes the start to an electrical strike." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "The fire was held to 52 acres and the evacuation lifted the same afternoon (Cal Fire record; KBAK), a risk removed for the neighborhood. 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"record_fields", "fields": { "account": "Cal Fire's incident record lists the Trotter fire at Trotter Dr and Skyline Drive, west of Tehachapi, Kern County. 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The feature says in 2023 ALERTCalifornia's cameras and AI helped crews rapidly detect and contain the Trotter Fire at 52 acres. Without AI-powered imaging, ALERTCalifornia founder Neal Driscoll estimates it may have spread to nearly 4,000 acres. It quotes Zachary Wells, deputy chief of the Kern County Fire Department and deputy director of operations at ALERTCalifornia. The claim is found only in Microsoft and ALERTCalifornia material. No fire-agency, regulator or independent news account of the fire mentions AI detection. None says which agency received the alert or its timing relative to 911 calls.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "The fire was held to 52 acres and the evacuation lifted the same afternoon (Cal Fire record; KBAK), a risk removed for the neighborhood. The claim that the camera network detected it first comes only from ALERTCalifornia's founder via Microsoft." }, { "group": "organizational", "depth": 2, "reason": "An early alert to the Kern County Fire Department is described only in the vendor's account. The department's own account mentions no camera alert." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect smoke in camera feeds and alert fire agencies. 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Which agency received this alert is not stated", "A lightning or electrical-strike ignition on 14 August 2023 that crews stopped at 52 acres" ], "must_hold": [ "An alert reaching dispatch before or alongside 911 calls, which no cited source documents for this fire", "A counterfactual spread that depends on fuels, wind and response time. The 4,000-acre figure is an estimate by the network's founder" ], "echoes": [ { "date": "2024-07-06", "setting": "ALERTCalifornia AI camera alert at 2:33 a.m. sends Cal Fire engines to a fire near Auburn before any 911 call; held to a 30 x 30 ft area (AIBD-0007)", "outcome": "recurred", "source": "AIBD-0007" }, { "date": "2026-05-04", "setting": "Cal Fire battalion chief Brent Pascua, quoted by AP, says responses have started before 911 was called and in a few cases fires were put out with no 911 call ever received (aggregate statement)", "outcome": "recurred", "source": "Associated Press, 4 May 2026, https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "ALERTCalifornia's camera network with AI smoke detection covering the Tehachapi area", "A fire agency able to act on an early detection. 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Trotter fire, Kern County, incident record (started 14 August 2023; the incident page is the public view).", "title": "Trotter Fire", "url": "https://www.fire.ca.gov/incidents/2023/8/14/trotter/", "date": "2023-08-17", "written_by": "independent", "quote": "Trotter Dr and Skyline Drive, West of Tehachapi" }, { "kind": "news report", "citation": "KBAK / Bakersfield Now (2023). Evacuation order lifted in Bear Valley Springs following Trotter Fire. 14 August 2023.", "title": "Evacuation order lifted in Bear Valley Springs following Trotter Fire", "url": "https://bakersfieldnow.com/news/local/kern-county-fire-department-orders-evacuation-in-bear-valley-springs-due-to-vegetation-fires-kern-county", "date": "2023-08-14", "written_by": "independent", "quote": "The Kern County Fire Department said that the Trotter Fire was started by an electrical strike." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (Microsoft funds ALERTCalifornia; the source of the AI-detection claim).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "In 2023, ALERTCalifornia's cameras and AI helped crews rapidly detect and contain Kern County's Trotter Fire at 52 acres." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (the counterfactual estimate).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "Without AI-powered imaging, Driscoll estimates it may have spread to nearly 4,000 acres." }, { "kind": "press release", "citation": "ALERTCalifornia (2026). New collaboration expands capabilities and enhances natural disaster resilience. 3 February 2026 (the Microsoft funding announcement; the page does not mention the Trotter Fire).", "title": "New collaboration expands capabilities and enhances natural disaster resilience", "url": "https://alertcalifornia.org/new-collaboration-expands-capabilities-and-enhances-natural-disaster-resilience/", "date": "2026-02-03", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0313-source-0001", "AIBD-0313-source-0002", "AIBD-0313-source-0003", "AIBD-0313-source-0004", "AIBD-0313-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "California Department of Forestry and Fire Protection (2023). Trotter fire, Kern County, incident record (started 14 August 2023; the incident page is the public view).", "title": "Trotter Fire", "url": "https://www.fire.ca.gov/incidents/2023/8/14/trotter/", "date": "2023-08-17", "written_by": "independent", "quote": "Trotter Dr and Skyline Drive, West of Tehachapi" }, { "kind": "news report", "citation": "KBAK / Bakersfield Now (2023). Evacuation order lifted in Bear Valley Springs following Trotter Fire. 14 August 2023.", "title": "Evacuation order lifted in Bear Valley Springs following Trotter Fire", "url": "https://bakersfieldnow.com/news/local/kern-county-fire-department-orders-evacuation-in-bear-valley-springs-due-to-vegetation-fires-kern-county", "date": "2023-08-14", "written_by": "independent", "quote": "The Kern County Fire Department said that the Trotter Fire was started by an electrical strike." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (Microsoft funds ALERTCalifornia; the source of the AI-detection claim).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "In 2023, ALERTCalifornia's cameras and AI helped crews rapidly detect and contain Kern County's Trotter Fire at 52 acres." }, { "kind": "press release", "citation": "Mori B (2026). The first five minutes. Microsoft Unlocked, 27 August 2026 (the counterfactual estimate).", "title": "The first five minutes", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/", "date": "2026-08-27", "written_by": "independent", "quote": "Without AI-powered imaging, Driscoll estimates it may have spread to nearly 4,000 acres." }, { "kind": "press release", "citation": "ALERTCalifornia (2026). New collaboration expands capabilities and enhances natural disaster resilience. 3 February 2026 (the Microsoft funding announcement; the page does not mention the Trotter Fire).", "title": "New collaboration expands capabilities and enhances natural disaster resilience", "url": "https://alertcalifornia.org/new-collaboration-expands-capabilities-and-enhances-natural-disaster-resilience/", "date": "2026-02-03", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Trotter Fire, Bear Valley Springs near Tehachapi, 14–17 August 2023, 52 acres; the AI-detection claim and “nearly 4,000 acres” counterfactual come only from ALERTCalifornia's founder via Microsoft.", "account": "Cal Fire's incident record lists the Trotter fire at Trotter Dr and Skyline Drive, west of Tehachapi, Kern County. It started 14 August 2023 at 18:10 UTC under the Kern County Fire Department (not a Cal Fire incident). It burned 52 acres and was 100% contained and extinguished 17 August 2023. It says nothing about how the fire was detected or reported. KBAK (Bakersfield, 14 August 2023) reported a mandatory evacuation for Skyline Drive between Saddleback Drive and Cumberland Road in Bear Valley Springs. It gave an estimated 40 acres, forward progress stopped by 2:17 p.m. and the evacuation lifted by 3:12 p.m. The Kern County Fire Department said the fire “was started by an electrical strike”. A KGET headline the same day blamed lightning and said no homes were damaged. Microsoft's “Unlocked” feature “The first five minutes” (Bruna Mori, 27 August 2026) was published after Microsoft's $5 million contribution to ALERTCalifornia. It included a $3 million Azure grant (announced by ALERTCalifornia on 3 February 2026). The feature says in 2023 ALERTCalifornia's cameras and AI helped crews rapidly detect and contain the Trotter Fire at 52 acres. Without AI-powered imaging, ALERTCalifornia founder Neal Driscoll estimates it may have spread to nearly 4,000 acres. It quotes Zachary Wells, deputy chief of the Kern County Fire Department and deputy director of operations at ALERTCalifornia. The claim is found only in Microsoft and ALERTCalifornia material. No fire-agency, regulator or independent news account of the fire mentions AI detection. None says which agency received the alert or its timing relative to 911 calls." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ALERTCalifornia camera network with AI smoke detection (the Microsoft feature does not name the detection software; AIBD-0007 identifies DigitalPath)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "ALERTCalifornia, University of California San Diego (camera network); AI detection software supplier not named in the sources" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ALERTCalifornia cameras and AI may have helped crews rapidly detect and contain Kern County's 52-acre Trotter Fire (August 2023)", "headline": "ALERTCalifornia AI may have helped contain the Trotter Fire" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "b83738a9b8addc0e441ba937a2c21d50301a9915bf167e11bd5207f50a469d3e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Cal Fire's incident record lists the Trotter fire at Trotter Dr and Skyline Drive, west of Tehachapi, Kern County. It started 14 August 2023 at 18:10 UTC under the Kern County Fire Department (not a Cal Fire incident). It burned 52 acres and was 100% contained and extinguished 17 August 2023. It says nothing about how the fire was detected or reported. KBAK (Bakersfield, 14 August 2023) reported a mandatory evacuation for Skyline Drive between Saddleback Drive and Cumberland Road in Bear Valley Springs. It gave an estimated 40 acres, forward progress stopped by 2:17 p.m. and the evacuation lifted by 3:12 p.m. The Kern County Fire Department said the fire “was started by an electrical strike”. A KGET headline the same day blamed lightning and said no homes were damaged. Microsoft's “Unlocked” feature “The first five minutes” (Bruna Mori, 27 August 2026) was published after Microsoft's $5 million contribution to ALERTCalifornia. It included a $3 million Azure grant (announced by ALERTCalifornia on 3 February 2026). The feature says in 2023 ALERTCalifornia's cameras and AI helped crews rapidly detect and contain the Trotter Fire at 52 acres. Without AI-powered imaging, ALERTCalifornia founder Neal Driscoll estimates it may have spread to nearly 4,000 acres. It quotes Zachary Wells, deputy chief of the Kern County Fire Department and deputy director of operations at ALERTCalifornia. The claim is found only in Microsoft and ALERTCalifornia material. No fire-agency, regulator or independent news account of the fire mentions AI detection. None says which agency received the alert or its timing relative to 911 calls.", "replication": { "produced_by": [ "ALERTCalifornia's camera network with AI smoke detection covering the Tehachapi area", "A fire agency able to act on an early detection. Which agency received this alert is not stated", "A lightning or electrical-strike ignition on 14 August 2023 that crews stopped at 52 acres" ], "must_hold": [ "An alert reaching dispatch before or alongside 911 calls, which no cited source documents for this fire", "A counterfactual spread that depends on fuels, wind and response time. The 4,000-acre figure is an estimate by the network's founder" ], "echoes": [ { "date": "2024-07-06", "setting": "ALERTCalifornia AI camera alert at 2:33 a.m. sends Cal Fire engines to a fire near Auburn before any 911 call; held to a 30 x 30 ft area (AIBD-0007)", "outcome": "recurred", "source": "AIBD-0007" }, { "date": "2026-05-04", "setting": "Cal Fire battalion chief Brent Pascua, quoted by AP, says responses have started before 911 was called and in a few cases fires were put out with no 911 call ever received (aggregate statement)", "outcome": "recurred", "source": "Associated Press, 4 May 2026, https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0009", "case_url": "records/AIBD-0009.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Built to detect elephants near the track and alert loco pilots, station masters and control rooms. This benefit is that purpose." }, { "label": "AI contribution", "value": "The operator's release says the drivers noticed the herd and applied the emergency brake and were “also alerted” by the IDS. One local report says the braking followed the IDS warning. The AI alert is documented, but its share of the causal credit is not separable from the crew's own sighting. Hence protective response, prevention unproven." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0009", "title": "AI-based Intrusion Detection System alert and emergency braking avert Kamrup Express collision with about 60 elephants in Assam", "summary": "16 October 2024: AI intrusion alert and crew braking stopped 15959 Kamrup Express as about 60 elephants crossed near Lamsakhang, Assam", "date_observed": "2024-10-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 16 October 2024 at about 8:37 pm, train 15959 Kamrup Express was running from Guwahati toward Lumding. It neared a herd of about 60 wild elephants crossing the track between Habaipur and Lamsakhang stations in Assam. Northeast Frontier Railway said loco pilot J.D. Das and assistant loco pilot Umesh Kumar noticed the herd and applied the emergency brake. It said the AI-based Intrusion Detection System operating on that section also alerted the drivers. That system uses existing optical fibers as sensors to identify animal movements and alert control offices, station masters, gatemen and loco pilots. The train stopped and no collision occurred. NFR reported it saved 414 elephants in 2023 and 383 up to 16 October 2024.", "category": "protective response", "unexpected": false, "purpose": "Built to detect elephants near the track and alert loco pilots, station masters and control rooms. This benefit is that purpose.", "system": "AI-based Intrusion Detection System (distributed acoustic sensing on optical fiber), Northeast Frontier Railway", "model_version": null, "developer": null, "deployer": "Northeast Frontier Railway (Indian Railways)", "location": "Lumding, India", "domain": "Railway safety and wildlife protection", "segment": "Rail transport", "benefit_type": "Increased Product Safety", "taxonomy_note": "The collision avoided is between a train and elephants, so the gain is shared with conservation.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "NFR avoided a collision between a running express train and about 60 elephants, with the derailment and disruption risk that carries." }, { "group": "societal", "depth": 2, "reason": "About 60 wild elephants, a protected species, crossed safely; NFR reports hundreds of such events a year on this network." } ], "ai_role": { "status": "assessed", "statement": "The operator's release says the drivers noticed the herd and applied the emergency brake and were “also alerted” by the IDS. One local report says the braking followed the IDS warning. The AI alert is documented, but its share of the causal credit is not separable from the crew's own sighting. Hence protective response, prevention unproven." }, "evidence": [ { "kind": "news report", "citation": "Morung Express News (2024). Alert train drivers 'avert collision' with herd of elephants in Assam (based on NFR release, Maligaon, 17 October 2024).", "title": "Alert train drivers 'avert collision' with herd of elephants in Assam", "url": "https://morungexpress.com/alert-train-drivers-avert-collision-with-herd-of-elephants-in-assam", "date": "2024-10-17", "written_by": "independent", "quote": "The drivers were also alerted by the Artificial Intelligence (AI)-based Intrusion Detection System (IDS) that has been in operation in that section." }, { "kind": "news report", "citation": "The Sentinel (Assam) (2024). Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants.", "title": "Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants", "url": "https://www.sentinelassam.com/breakingnews/assam-ai-technology-generated-alerts-prevent-train-from-colliding-with-elephants", "date": null, "written_by": "independent", "quote": "the loco pilot and assistant loco pilot applied brakes, preventing a possible collusion." }, { "kind": "news report", "citation": "The Meghalayan Express (2024). AI technology alerts drivers to stop trains to rescue a herd of elephants in Assam.", "title": "AI technology alerts drivers to stop trains to rescue a herd of elephants in Assam", "url": "https://themeghalayanexpress.com/ai-technology-alerts-drivers-to-stop-trains-to-rescue-a-herd-of-elephants-in-assam/", "date": "2024-10-18", "written_by": "independent", "quote": "Thanks to the quick response of the pilots, who applied the emergency brakes, a potential collision was averted, ensuring the safety of the elephants." }, { "kind": "report", "citation": "DD News (2025). Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes (reporting the Railway Minister's written Rajya Sabha reply).", "title": "Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes", "url": "https://ddnews.gov.in/en/indian-railways-expands-ai-based-intrusion-detection-to-prevent-elephant-collisions-across-key-routes/", "date": "2025-12-05", "written_by": "independent", "quote": "loco pilots, station masters and control rooms when elephant movement is detected near the tracks" }, { "kind": "news report", "citation": "The Sentinel (Assam) (2026). AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway.", "title": "AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway", "url": "https://www.sentinelassam.com/north-east-india-news/assam-news/ai-shield-and-plan-bee-save-255-elephants-across-northeast-frontier-railway", "date": "2026-10-08", "written_by": "independent", "quote": "immediately transmits real-time alerts to station masters and loco pilots, prompting them to reduce train speeds to 25 kmph when wildlife wanders near the tracks." }, { "kind": "news report", "citation": "PTI. AI-based surveillance mechanism helps eliminate elephant deaths due to train collisions in 11 Northeast corridors. The Tribune, 6 September 2023.", "title": "AI-based surveillance mechanism helps eliminate elephant deaths due to train collisions in 11 Northeast corridors", "url": "https://www.tribuneindia.com/news/india/ai-based-surveillance-mechanism-helps-eliminate-elephant-deaths-due-to-train-collisions-in-11-northeast-corridors-541918", "date": "2023-09-06", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Fiber-optic acoustic sensing with AI classification installed along an elephant corridor", "Alert routed to loco pilots and station masters in time to brake" ], "must_hold": [ "Train speed and sighting distance allow a stop after the alert", "Crew act on the alert. The operator attributes this stop partly to the crew's own sighting" ], "echoes": [ { "date": null, "setting": "Aggregate, not an individual event: NFR announced 255 elephants protected in 2026 across Lumding (122), Alipurduar (106), Tinsukia (15) and Rangiya (14) divisions, with alerts prompting speed reductions to 25 km/h (The Sentinel, 8 October 2026)", "model_version": null, "outcome": "recurred", "source": "https://www.sentinelassam.com/north-east-india-news/assam-news/ai-shield-and-plan-bee-save-255-elephants-across-northeast-frontier-railway" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "severity": null, "group": "societal", "basis": "AI intrusion detection protecting wildlife: Ezemvelo KZN Wildlife's AI cameras failed to detect poachers and four rhinos were killed, the protected-animal group the NFR acoustic alert saved when about 60 elephants crossed the track." }, "headline": "AI intrusion alert stops Kamrup Express as about 60 elephants cross", "metric": "saved 414 elephants in 2023 and 383 up to 16 October 2024", "comparator": { "against": "train-elephant collisions before the system (national average of elephant deaths on tracks)", "before": "on an average 20 elephants die due to train collisions in the country every year and a majority of these incidents take place in the Northeast Frontier Railway", "after": "since the launch of the system, these 11 corridors have not reported any train-elephant collision", "basis": "measured", "quote": "De said that since the launch of the system, these 11 corridors have not reported any train-elephant collision. Government data shows that on an average 20 elephants die due to train collisions in the country every year and a majority of these incidents take place in the Northeast Frontier Railway.", "url": "https://www.tribuneindia.com/news/india/ai-based-surveillance-mechanism-helps-eliminate-elephant-deaths-due-to-train-collisions-in-11-northeast-corridors-541918" }, "later": { "status": "scaled", "date": "2025-12-05", "note": "By December 2025 the system covered 141 route kilometers of Northeast Frontier Railway and tenders had been awarded for 981 more route kilometers nationwide.", "quote": "Indian Railways has scaled up the deployment of its artificial intelligence-enabled Intrusion Detection System (IDS) to curb elephant fatalities on rail tracks, with the technology now active across 141 route kilometres (RKms) of the Northeast Frontier Railway and tenders awarded for an additional 981 RKms nationwide.", "url": "https://ddnews.gov.in/en/indian-railways-expands-ai-based-intrusion-detection-to-prevent-elephant-collisions-across-key-routes/" }, "value": null, "developer_country": null, "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 219 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.tribuneindia.com/news/india/ai-based-surveillance-mechanism-helps-eliminate-elephant-deaths-due-to-train-collisions-in-11-northeast-corridors-541918; https://ddnews.gov.in/en/indian-railways-expands-ai-based-intrusion-detection-to-prevent-elephant-collisions-across-key-routes/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Northeast_Frontier_Railway_zone", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report and five independent news reports. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0009", "catalog_version": "1.0.0", "source_fingerprint": "e2557968a4b3067e350e9314a4688b81be6e43015c3adcff9491a397285cf100", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0009-source-0001", "AIBD-0009-source-0002", "AIBD-0009-source-0003", "AIBD-0009-source-0004", "AIBD-0009-source-0005", "AIBD-0009-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Northeast Frontier Railway (Indian Railways)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The operator's release says the drivers noticed the herd and applied the emergency brake and were “also alerted” by the IDS. One local report says the braking followed the IDS warning. The AI alert is documented, but its share of the causal credit is not separable from the crew's own sighting. Hence protective response, prevention unproven." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The operator's release says the drivers noticed the herd and applied the emergency brake and were “also alerted” by the IDS. One local report says the braking followed the IDS warning. The AI alert is documented, but its share of the causal credit is not separable from the crew's own sighting. Hence protective response, prevention unproven." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "NFR avoided a collision between a running express train and about 60 elephants, with the derailment and disruption risk that carries." }, { "group": "societal", "depth": 2, "reason": "About 60 wild elephants, a protected species, crossed safely; NFR reports hundreds of such events a year on this network." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: one independent report and five independent news reports. The cited evidence includes no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "NFR avoided a collision between a running express train and about 60 elephants, with the derailment and disruption risk that carries." }, { "group": "societal", "depth": 2, "reason": "About 60 wild elephants, a protected species, crossed safely; NFR reports hundreds of such events a year on this network." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "NFR avoided a collision between a running express train and about 60 elephants, with the derailment and disruption risk that carries." }, { "group": "societal", "depth": 2, "reason": "About 60 wild elephants, a protected species, crossed safely; NFR reports hundreds of such events a year on this network." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", 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Wildlife's AI cameras failed to detect poachers and four rhinos were killed, the protected-animal group the NFR acoustic alert saved when about 60 elephants crossed the track.", "relation": "comparable_harm" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Existing paired_incident classification; no new source review.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, 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and elephants, so the gain is shared with conservation." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 16 October 2024 at about 8:37 pm, train 15959 Kamrup Express was running from Guwahati toward Lumding. 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Alert train drivers 'avert collision' with herd of elephants in Assam (based on NFR release, Maligaon, 17 October 2024).", "title": "Alert train drivers 'avert collision' with herd of elephants in Assam", "url": "https://morungexpress.com/alert-train-drivers-avert-collision-with-herd-of-elephants-in-assam", "date": "2024-10-17", "written_by": "independent", "quote": "The drivers were also alerted by the Artificial Intelligence (AI)-based Intrusion Detection System (IDS) that has been in operation in that section." }, { "kind": "news report", "citation": "The Sentinel (Assam) (2024). Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants.", "title": "Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants", "url": "https://www.sentinelassam.com/breakingnews/assam-ai-technology-generated-alerts-prevent-train-from-colliding-with-elephants", "written_by": "independent", "quote": "the loco pilot and assistant loco pilot applied brakes, preventing a possible collusion." }, { "kind": "news report", "citation": "The Meghalayan Express (2024). AI technology alerts drivers to stop trains to rescue a herd of elephants in Assam.", "title": "AI technology alerts drivers to stop trains to rescue a herd of elephants in Assam", "url": "https://themeghalayanexpress.com/ai-technology-alerts-drivers-to-stop-trains-to-rescue-a-herd-of-elephants-in-assam/", "date": "2024-10-18", "written_by": "independent", "quote": "Thanks to the quick response of the pilots, who applied the emergency brakes, a potential collision was averted, ensuring the safety of the elephants." }, { "kind": "report", "citation": "DD News (2025). Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes (reporting the Railway Minister's written Rajya Sabha reply).", "title": "Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes", "url": "https://ddnews.gov.in/en/indian-railways-expands-ai-based-intrusion-detection-to-prevent-elephant-collisions-across-key-routes/", "date": "2025-12-05", "written_by": "independent", "quote": "loco pilots, station masters and control rooms when elephant movement is detected near the tracks" }, { "kind": "news report", "citation": "The Sentinel (Assam) (2026). AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway.", "title": "AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway", "url": "https://www.sentinelassam.com/north-east-india-news/assam-news/ai-shield-and-plan-bee-save-255-elephants-across-northeast-frontier-railway", "date": "2026-10-08", "written_by": "independent", "quote": "immediately transmits real-time alerts to station masters and loco pilots, prompting them to reduce train speeds to 25 kmph when wildlife wanders near the tracks." }, { "kind": "news report", "citation": "PTI. AI-based surveillance mechanism helps eliminate elephant deaths due to train collisions in 11 Northeast corridors. 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Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants.", "title": "Assam: AI Technology Generated Alerts Prevent Train From Colliding With Elephants", "url": "https://www.sentinelassam.com/breakingnews/assam-ai-technology-generated-alerts-prevent-train-from-colliding-with-elephants", "written_by": "independent", "quote": "the loco pilot and assistant loco pilot applied brakes, preventing a possible collusion." }, { "kind": "news report", "citation": "The Meghalayan Express (2024). 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Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes (reporting the Railway Minister's written Rajya Sabha reply).", "title": "Indian Railways expands AI-based intrusion detection to prevent elephant collisions across key routes", "url": "https://ddnews.gov.in/en/indian-railways-expands-ai-based-intrusion-detection-to-prevent-elephant-collisions-across-key-routes/", "date": "2025-12-05", "written_by": "independent", "quote": "loco pilots, station masters and control rooms when elephant movement is detected near the tracks" }, { "kind": "news report", "citation": "The Sentinel (Assam) (2026). AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway.", "title": "AI Shield and 'Plan Bee' Save 255 Elephants Across Northeast Frontier Railway", "url": "https://www.sentinelassam.com/north-east-india-news/assam-news/ai-shield-and-plan-bee-save-255-elephants-across-northeast-frontier-railway", "date": "2026-10-08", "written_by": "independent", "quote": "immediately transmits real-time alerts to station masters and loco pilots, prompting them to reduce train speeds to 25 kmph when wildlife wanders near the tracks." }, { "kind": "news report", "citation": "PTI. AI-based surveillance mechanism helps eliminate elephant deaths due to train collisions in 11 Northeast corridors. The Tribune, 6 September 2023.", "title": "AI-based surveillance mechanism helps eliminate elephant deaths due to train collisions in 11 Northeast corridors", "url": "https://www.tribuneindia.com/news/india/ai-based-surveillance-mechanism-helps-eliminate-elephant-deaths-due-to-train-collisions-in-11-northeast-corridors-541918", "date": "2023-09-06", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "16 October 2024: AI intrusion alert and crew braking stopped 15959 Kamrup Express as about 60 elephants crossed near Lamsakhang, Assam", "account": "On 16 October 2024 at about 8:37 pm, train 15959 Kamrup Express was running from Guwahati toward Lumding. It neared a herd of about 60 wild elephants crossing the track between Habaipur and Lamsakhang stations in Assam. Northeast Frontier Railway said loco pilot J.D. Das and assistant loco pilot Umesh Kumar noticed the herd and applied the emergency brake. It said the AI-based Intrusion Detection System operating on that section also alerted the drivers. That system uses existing optical fibers as sensors to identify animal movements and alert control offices, station masters, gatemen and loco pilots. The train stopped and no collision occurred. NFR reported it saved 414 elephants in 2023 and 383 up to 16 October 2024." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI-based Intrusion Detection System (distributed acoustic sensing on optical fiber), Northeast Frontier Railway" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "AI-based Intrusion Detection System alert and emergency braking avert Kamrup Express collision with about 60 elephants in Assam", "headline": "AI intrusion alert stops Kamrup Express as about 60 elephants cross" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "e2557968a4b3067e350e9314a4688b81be6e43015c3adcff9491a397285cf100" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 16 October 2024 at about 8:37 pm, train 15959 Kamrup Express was running from Guwahati toward Lumding. It neared a herd of about 60 wild elephants crossing the track between Habaipur and Lamsakhang stations in Assam. Northeast Frontier Railway said loco pilot J.D. Das and assistant loco pilot Umesh Kumar noticed the herd and applied the emergency brake. It said the AI-based Intrusion Detection System operating on that section also alerted the drivers. That system uses existing optical fibers as sensors to identify animal movements and alert control offices, station masters, gatemen and loco pilots. The train stopped and no collision occurred. NFR reported it saved 414 elephants in 2023 and 383 up to 16 October 2024.", "replication": { "produced_by": [ "Fiber-optic acoustic sensing with AI classification installed along an elephant corridor", "Alert routed to loco pilots and station masters in time to brake" ], "must_hold": [ "Train speed and sighting distance allow a stop after the alert", "Crew act on the alert. The operator attributes this stop partly to the crew's own sighting" ], "echoes": [ { "setting": "Aggregate, not an individual event: NFR announced 255 elephants protected in 2026 across Lumding (122), Alipurduar (106), Tinsukia (15) and Rangiya (14) divisions, with alerts prompting speed reductions to 25 km/h (The Sentinel, 8 October 2026)", "outcome": "recurred", "source": "https://www.sentinelassam.com/north-east-india-news/assam-news/ai-shield-and-plan-bee-save-255-elephants-across-northeast-frontier-railway" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0336", "case_url": "records/AIBD-0336.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Built to flag benefit recipients at elevated risk of receiving unentitled payments for manual control by UDK's control team. The recorded outcome, cases with benefits stopped or reduced, is that purpose." }, { "label": "AI contribution", "value": "The models select “requested cases” that UDK's HOK control team screens and investigates. Decisions to stop or reduce benefits are taken by humans after investigation. UDK measures model quality by the share of cases the team accepts for manual processing. Every outcome figure is a UDK statement (FOI responses and a letter) as reproduced verbatim in Amnesty International's report. No official publication of them is among the sources. Amnesty contests effectiveness and alleges discrimination risks, and UDK rejected parts of Amnesty's assessment. NNIT built the models to ATP's specifications. No developer-authored source is on record. The dm.dk report and ATP's page are not among the sources." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Denmark" ] }, "record": { "id": "AIBD-0336", "title": "Udbetaling Danmark's fraud-control models may have produced 135, 36 and 72 cases with benefits stopped or reduced in 2023", "summary": "UDK/ATP ML fraud models (about 60 since 2019): 2023 figures of 135, 36 and 72 cases with revenue from three models; Amnesty (Nov 2024) calls their effectiveness questionable.", "date_observed": "2024-11-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Udbetaling Danmark (UDK), the Danish public authority that has centralized welfare payments since 2012, has its benefits administered by ATP (Arbejdsmarkedets Tillaegspension). ATP designs the fraud-control components of UDK's Joint Data Unit (Den Faelles Dataenhed, established 2014 per the University of Copenhagen's Offentlig AI database). NNIT A/S is the supplier. As of 2019 UDK was reported to use up to 60 algorithms for fraud detection and other purposes. The registry describes profiling and machine learning (it names Naive Bayes classification and cluster analysis) run on linked register data. In a letter to Amnesty International before November 2024, UDK supplied its 2023 statistics for three data-driven controls. The “Really Single” model produced 412 requested cases, 292 established control cases and 135 cases with revenue (benefits stopped or reduced). “Model Abroad” (unreported departures, state and disability pension) gave 511, 351 and 36. The parental-leave controls including the “Fictitious Employment” model gave 491, 207 and 72. UDK cautioned that cases with revenue in a year are not necessarily a proportion of cases established that year. A UDK/ATP control official told Amnesty the team manages 5,000 to 6,000 cases a year, of which around 1,800 come from the models. Amnesty's report of 12 November 2024 concludes it is questionable whether the algorithms are effective. It finds most opened cases end with no further action and the system risks discrimination and mass surveillance. A Copenhagen Municipality control official said more often than not there is no fraud in the cases UDK sends. Background sources give earlier yields. AlgorithmWatch (2020) reports 2017 and 2018 controls unveiled about half a billion kroner in erroneous payments. The Automating Society 2020 Denmark chapter cites a July 2019 report. It counted 705 cases in 2017 and 1,094 in 2018 where payments were stopped, adjusted or repaid, out of 2.7 million screened beneficiaries. The dm.dk FOI report (October 2024) and ATP's page are not among the sources. The report said nearly all 1.2 million pensioners were screened and only 36 cases in 2023 ended with a benefit stopped or reduced. That matches the Model Abroad row. ATP's page credits 260 million kr. a year of savings to IT modernization generally. The 2023 figures carry no day.", "category": "harm prevented", "unexpected": false, "purpose": "Built to flag benefit recipients at elevated risk of receiving unentitled payments for manual control by UDK's control team. The recorded outcome, cases with benefits stopped or reduced, is that purpose.", "system": "Udbetaling Danmark / ATP Den Faelles Dataenhed fraud-control models (about 60 profiling and machine-learning models as of 2019, including 'Really Single', 'Model Abroad' and 'Fictitious Employment')", "model_version": null, "developer": "ATP (Arbejdsmarkedets Tillaegspension) with NNIT A/S as supplier, for Udbetaling Danmark", "deployer": "Udbetaling Danmark (public authority), administered by ATP", "location": "Denmark (national)", "domain": "Public administration: social benefits fraud control", "segment": "National welfare payments authority", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary of the controls is identified. The people directly affected are those investigated. By Amnesty's reading of UDK's figures, 54% to almost 90% of opened cases per model ended with no further action." }, { "group": "organizational", "depth": 1, "reason": "UDK's own 2023 figures: 135, 36 and 72 cases with revenue from three models against 412, 511 and 491 requested cases. No amounts are given and the authority says yearly figures are not directly comparable." }, { "group": "societal", "depth": 1, "reason": "Controls cover about 2.4 million recipients paid around DKK 241 billion in 2021. The documented yield is a few hundred cases a year. Amnesty's report disputes the necessity and proportionality of the processing." } ], "ai_role": { "status": "assessed", "statement": "The models select “requested cases” that UDK's HOK control team screens and investigates. Decisions to stop or reduce benefits are taken by humans after investigation. UDK measures model quality by the share of cases the team accepts for manual processing. Every outcome figure is a UDK statement (FOI responses and a letter) as reproduced verbatim in Amnesty International's report. No official publication of them is among the sources. Amnesty contests effectiveness and alleges discrimination risks, and UDK rejected parts of Amnesty's assessment. NNIT built the models to ATP's specifications. No developer-authored source is on record. The dm.dk report and ATP's page are not among the sources." }, "evidence": [ { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State (EUR 18/8709/2024), 12 Nov 2024, Table 5 'Statistics for data-driven controls used by UDK in 2023', p. 43.", "title": "Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State", "url": "https://www.amnesty.org/en/documents/eur18/8709/2024/en/", "date": "2024-11-12", "written_by": "independent", "quote": "135 Cases with revenue – the number of cases that were found to be fraudulent or erroneous after an investigation was conducted" }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, full report PDF, p. 30 (Fraud-control algorithms).", "title": "Coded Injustice (PDF), chapter on fraud-control algorithms", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "As of 2019, UDK were using up to 60 different algorithms for fraud detection and other purposes." }, { "kind": "report", "citation": "Udbetaling Danmark, letter to Amnesty International on 2023 performance of data-driven controls, as reproduced in Amnesty International (2024), Coded Injustice, pp. 42-43.", "title": "UDK statement on how it reports cases with revenue (reproduced in Coded Injustice)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "deployer", "quote": "Finally, we report how many cases result in citizens having their benefits stopped or reduced (number of cases with revenue)." }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, p. 44, assessment of effectiveness and interview with a UDK/ATP control official (11 Jan 2024).", "title": "Coded Injustice, p. 44 (effectiveness of the fraud-control algorithms)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "It is questionable therefore whether the use of fraud control algorithms is effective at identifying fraud in this context." }, { "kind": "press release", "citation": "Amnesty International (2024). Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups – report. Press release, 12 Nov 2024.", "title": "Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups", "url": "https://www.amnesty.org/en/latest/news/2024/11/denmark-ai-powered-welfare-system-fuels-mass-surveillance-and-risks-discriminating-against-marginalized-groups/", "date": "2024-11-12", "written_by": "independent", "quote": "UDK and ATP use a system of up to 60 algorithmic models purportedly designed to detect social benefits fraud and flag individuals for further investigations." }, { "kind": "other", "citation": "University of Copenhagen, Faculty of Law (n.d.). Offentlig AI database entry 'Kontrol med udbetaling af ydelser' (Udbetaling Danmark; status: I brug; supplier NNIT A/S; about 60 models as of 2019).", "title": "Kontrol med udbetaling af ydelser (Offentlig AI database entry)", "url": "https://offentlig-ai.dk/projekter/kontrol-med-udbetaling-ydelser", "date": null, "written_by": "independent", "quote": "Udbetaling Danmark udvikler og bruger modeller til kontrol på områder, hvor myndigheden har erfaret, at der forekommer uberettigede udbetalinger." }, { "kind": "report", "citation": "Kayser-Bril, N. (2020). In a quest to optimize welfare management, Denmark built a surveillance behemoth. AlgorithmWatch, 6 Aug 2020.", "title": "In a quest to optimize welfare management, Denmark built a surveillance behemoth", "url": "https://algorithmwatch.org/en/udbetaling-danmark/", "date": "2020-08-06", "written_by": "independent", "quote": "unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros)" }, { "kind": "report", "citation": "Alfter, B. (2020). Denmark chapter, section 'Controlling social welfare recipients'. In AlgorithmWatch and Bertelsmann Stiftung, Automating Society Report 2020.", "title": "Automating Society Report 2020: Denmark", "url": "https://automatingsociety.algorithmwatch.org/report2020/denmark/", "date": null, "written_by": "independent", "quote": "leading only to 705 cases in 2017 and 1094 cases in 2018 where the payments were stopped, adjusted, or paid back because of mistakes" }, { "kind": "news report", "citation": "Amnesty International Algorithmic Accountability Lab (2024). How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System. Global Investigative Journalism Network, 14 Nov 2024.", "title": "How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System", "url": "https://gijn.org/stories/amnesty-internationals-investigation-algorithms-denmarks-welfare-system/", "date": "2024-11-14", "written_by": "independent", "quote": "ATP is responsible for designing the fraud control components of UDK's Joint Data Unit." } ], "replication": { "produced_by": [ "Register linkage of benefits data with civil, housing, business, tax, income, health and employment registers under a 2015 legal authorization", "About 60 profiling and machine-learning models developed by NNIT to ATP's specifications", "A UDK/ATP control team (HOK) that manually screens model output into established control cases and investigates them", "A legal duty to also process all externally reported cases, which the control team says limits the share drawn from the models to about 30%" ], "must_hold": [ "Legal basis for register linkage and model use remains in force despite Amnesty's challenge and the dispute over classification under the EU AI Act", "Control-team capacity of about 5,000 to 6,000 cases a year", "Models continue to be monitored on their training cases, since UDK documents no evaluation metrics", "Political tolerance of the system after the November 2024 criticism" ], "echoes": [ { "date": null, "setting": "2017 and 2018 controls over 2.7 million screened beneficiaries, as reported in a July 2019 report cited by Automating Society 2020: 705 and 1,094 cases with payments stopped, adjusted or repaid", "model_version": null, "outcome": "recurred", "source": "https://automatingsociety.algorithmwatch.org/report2020/denmark/" }, { "date": null, "setting": "dm.dk FOI report, October 2024, as read by an earlier check (not re-read): nearly all 1.2 million pensioners screened, 36 cases in 2023 ending with a benefit stopped or reduced", "model_version": null, "outcome": "recurred", "source": "dm.dk FOI report, October 2024, as summarized in an earlier check of this case; consistent with the Model Abroad row of Amnesty's Table 5" } ] }, "paired_incident": null, "headline": "UDK fraud models may have yielded 135, 36 and 72 cases", "metric": "2023 figures of 135, 36 and 72 cases with revenue from three models", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No later development in the controls is established. The employment minister forwarded a UDK board account to Parliament (Folketinget DIU Alm.del 2024-25 bilag 32), but its contents remain unverified.", "quote": null, "url": null }, "value": { "amount": "half a billion Danish crowns (approximately 70 million euros)", "what": "erroneous payments unveiled by UDK and municipal controls in 2017 and 2018; the source says it is unclear how much was recovered and that the figure includes averted future payments, and it does not isolate the models", "who": "Udbetaling Danmark and the municipalities (the Danish public purse)", "estimate": false, "quote": "In 2017 and 2018, controls by UDK and municipalities unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros). It is unclear what amount was recovered, what was not recovered due to the beneficiary’s incapacity to repay, and what was an accounting trick (the figure includes future payments that were averted because of the control).", "url": "https://algorithmwatch.org/en/udbetaling-danmark/" }, "developer_country": "Denmark", "deployer_country": "Denmark", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://algorithmwatch.org/en/udbetaling-danmark/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.atp.dk/en/about-atp; https://da.wikipedia.org/wiki/Udbetaling_Danmark", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: four independent reports, one independent news report, one independent press release and one other independent source. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0336", "catalog_version": "1.0.0", "source_fingerprint": "f17dd05704428e7ad3e78e0e01f15067f7cee4c98a7dc6f09bc1eea7d43f4f91", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0336-source-0001", "AIBD-0336-source-0002", "AIBD-0336-source-0003", "AIBD-0336-source-0004", "AIBD-0336-source-0005", "AIBD-0336-source-0006", "AIBD-0336-source-0007", "AIBD-0336-source-0008", "AIBD-0336-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0336-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "ATP (Arbejdsmarkedets Tillaegspension) with NNIT A/S as supplier, for Udbetaling Danmark", "deployer": "Udbetaling Danmark (public authority), administered by ATP" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The models select “requested cases” that UDK's HOK control team screens and investigates. Decisions to stop or reduce benefits are taken by humans after investigation. UDK measures model quality by the share of cases the team accepts for manual processing. Every outcome figure is a UDK statement (FOI responses and a letter) as reproduced verbatim in Amnesty International's report. No official publication of them is among the sources. Amnesty contests effectiveness and alleges discrimination risks, and UDK rejected parts of Amnesty's assessment. NNIT built the models to ATP's specifications. No developer-authored source is on record. The dm.dk report and ATP's page are not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Udbetaling Danmark’s models selected benefit files for investigation. Staff screened the selections before deciding whether to investigate further.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The models select “requested cases” that UDK's HOK control team screens and investigates. Decisions to stop or reduce benefits are taken by humans after investigation. UDK measures model quality by the share of cases the team accepts for manual processing. Every outcome figure is a UDK statement (FOI responses and a letter) as reproduced verbatim in Amnesty International's report. No official publication of them is among the sources. Amnesty contests effectiveness and alleges discrimination risks, and UDK rejected parts of Amnesty's assessment. NNIT built the models to ATP's specifications. No developer-authored source is on record. The dm.dk report and ATP's page are not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary of the controls is identified. The people directly affected are those investigated. By Amnesty's reading of UDK's figures, 54% to almost 90% of opened cases per model ended with no further action." }, { "group": "organizational", "depth": 1, "reason": "UDK's own 2023 figures: 135, 36 and 72 cases with revenue from three models against 412, 511 and 491 requested cases. No amounts are given and the authority says yearly figures are not directly comparable." }, { "group": "societal", "depth": 1, "reason": "Controls cover about 2.4 million recipients paid around DKK 241 billion in 2021. The documented yield is a few hundred cases a year. Amnesty's report disputes the necessity and proportionality of the processing." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: four independent reports, one independent news report, one independent press release and one other independent source. The cited evidence includes no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary of the controls is identified. The people directly affected are those investigated. By Amnesty's reading of UDK's figures, 54% to almost 90% of opened cases per model ended with no further action." }, { "group": "organizational", "depth": 1, "reason": "UDK's own 2023 figures: 135, 36 and 72 cases with revenue from three models against 412, 511 and 491 requested cases. No amounts are given and the authority says yearly figures are not directly comparable." }, { "group": "societal", "depth": 1, "reason": "Controls cover about 2.4 million recipients paid around DKK 241 billion in 2021. The documented yield is a few hundred cases a year. Amnesty's report disputes the necessity and proportionality of the processing." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The agency reports several hundred adjustments to benefits, without amounts or a matched comparison. Amnesty’s reading suggests that most opened investigations produced no further action. No individual benefit or wider improvement is established." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary of the controls is identified. The people directly affected are those investigated. By Amnesty's reading of UDK's figures, 54% to almost 90% of opened cases per model ended with no further action." }, { "group": "organizational", "depth": 1, "reason": "UDK's own 2023 figures: 135, 36 and 72 cases with revenue from three models against 412, 511 and 491 requested cases. No amounts are given and the authority says yearly figures are not directly comparable." }, { "group": "societal", "depth": 1, "reason": "Controls cover about 2.4 million recipients paid around DKK 241 billion in 2021. The documented yield is a few hundred cases a year. Amnesty's report disputes the necessity and proportionality of the processing." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "half a billion Danish crowns (approximately 70 million euros)", "what": "erroneous payments unveiled by UDK and municipal controls in 2017 and 2018; the source says it is unclear how much was recovered and that the figure includes averted future payments, and it does not isolate the models", "who": "Udbetaling Danmark and the municipalities (the Danish public purse)", "estimate": false, "quote": "In 2017 and 2018, controls by UDK and municipalities unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros). It is unclear what amount was recovered, what was not recovered due to the beneficiary’s incapacity to repay, and what was an accounting trick (the figure includes future payments that were averted because of the control).", "url": "https://algorithmwatch.org/en/udbetaling-danmark/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "half a billion Danish crowns (approximately 70 million euros)", "what": "erroneous payments unveiled by UDK and municipal controls in 2017 and 2018; the source says it is unclear how much was recovered and that the figure includes averted future payments, and it does not isolate the models", "who": "Udbetaling Danmark and the municipalities (the Danish public purse)", "estimate": false, "quote": "In 2017 and 2018, controls by UDK and municipalities unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros). It is unclear what amount was recovered, what was not recovered due to the beneficiary’s incapacity to repay, and what was an accounting trick (the figure includes future payments that were averted because of the control).", "url": "https://algorithmwatch.org/en/udbetaling-danmark/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "half a billion Danish crowns (approximately 70 million euros)", "what": "erroneous payments unveiled by UDK and municipal controls in 2017 and 2018; the source says it is unclear how much was recovered and that the figure includes averted future payments, and it does not isolate the models", "who": "Udbetaling Danmark and the municipalities (the Danish public purse)", "estimate": false, "quote": "In 2017 and 2018, controls by UDK and municipalities unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros). It is unclear what amount was recovered, what was not recovered due to the beneficiary’s incapacity to repay, and what was an accounting trick (the figure includes future payments that were averted because of the control).", "url": "https://algorithmwatch.org/en/udbetaling-danmark/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The three controls reported 135, 36, and 72 cases with payments stopped or reduced in 2023. The same controls requested 412, 511, and 491 cases, but the agency cautions that the annual totals do not necessarily refer to the same cases." } }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Cases requested, cases opened for investigation, and cases in which benefits were stopped or reduced." } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The outcome figures come from the agency’s letter reproduced by Amnesty. No monetary amounts or matched case cohorts are given. Amnesty disputes the controls’ effectiveness and necessity, while the agency rejected parts of that assessment." } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No later development in the controls is established. The employment minister forwarded a UDK board account to Parliament (Folketinget DIU Alm.del 2024-25 bilag 32), but its contents remain unverified." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Staff investigated the files and decided whether benefits should be reduced or stopped." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0336" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Udbetaling Danmark is the intended institutional beneficiary. No individual beneficiary of the controls is identified." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "ratings", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Udbetaling Danmark is the intended institutional beneficiary. No individual beneficiary of the controls is identified." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "display": { "answer": "No count of beneficiaries is established. The screened population is not a measure of people who benefited." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Udbetaling Danmark (UDK), the Danish public authority that has centralized welfare payments since 2012, has its benefits administered by ATP (Arbejdsmarkedets Tillaegspension). ATP designs the fraud-control components of UDK's Joint Data Unit (Den Faelles Dataenhed, established 2014 per the University of Copenhagen's Offentlig AI database). NNIT A/S is the supplier. As of 2019 UDK was reported to use up to 60 algorithms for fraud detection and other purposes. The registry describes profiling and machine learning (it names Naive Bayes classification and cluster analysis) run on linked register data. In a letter to Amnesty International before November 2024, UDK supplied its 2023 statistics for three data-driven controls. The “Really Single” model produced 412 requested cases, 292 established control cases and 135 cases with revenue (benefits stopped or reduced). “Model Abroad” (unreported departures, state and disability pension) gave 511, 351 and 36. The parental-leave controls including the “Fictitious Employment” model gave 491, 207 and 72. UDK cautioned that cases with revenue in a year are not necessarily a proportion of cases established that year. A UDK/ATP control official told Amnesty the team manages 5,000 to 6,000 cases a year, of which around 1,800 come from the models. Amnesty's report of 12 November 2024 concludes it is questionable whether the algorithms are effective. It finds most opened cases end with no further action and the system risks discrimination and mass surveillance. A Copenhagen Municipality control official said more often than not there is no fraud in the cases UDK sends. Background sources give earlier yields. AlgorithmWatch (2020) reports 2017 and 2018 controls unveiled about half a billion kroner in erroneous payments. The Automating Society 2020 Denmark chapter cites a July 2019 report. It counted 705 cases in 2017 and 1,094 in 2018 where payments were stopped, adjusted or repaid, out of 2.7 million screened beneficiaries. The dm.dk FOI report (October 2024) and ATP's page are not among the sources. The report said nearly all 1.2 million pensioners were screened and only 36 cases in 2023 ended with a benefit stopped or reduced. That matches the Model Abroad row. ATP's page credits 260 million kr. a year of savings to IT modernization generally. The 2023 figures carry no day.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Udbetaling Danmark reported 135, 36, and 72 cases with benefits stopped or reduced through three controls in 2023." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary of the controls is identified. The people directly affected are those investigated. By Amnesty's reading of UDK's figures, 54% to almost 90% of opened cases per model ended with no further action." }, { "group": "organizational", "depth": 1, "reason": "UDK's own 2023 figures: 135, 36 and 72 cases with revenue from three models against 412, 511 and 491 requested cases. No amounts are given and the authority says yearly figures are not directly comparable." }, { "group": "societal", "depth": 1, "reason": "Controls cover about 2.4 million recipients paid around DKK 241 billion in 2021. The documented yield is a few hundred cases a year. Amnesty's report disputes the necessity and proportionality of the processing." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "value_present", "value": { "kind": "text", "text": "Amnesty International alleges discrimination risks and disproportionate surveillance. Udbetaling Danmark disputed parts of its assessment." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Amnesty International alleges discrimination risks and disproportionate surveillance. Udbetaling Danmark disputed parts of its assessment." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to flag benefit recipients at elevated risk of receiving unentitled payments for manual control by UDK's control team. The recorded outcome, cases with benefits stopped or reduced, is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Denmark (national)", "developer_country": "Denmark", "deployer_country": "Denmark" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to flag benefit recipients at elevated risk of receiving unentitled payments for manual control by UDK's control team. The recorded outcome, cases with benefits stopped or reduced, is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-11-12", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://algorithmwatch.org/en/udbetaling-danmark/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.atp.dk/en/about-atp; https://da.wikipedia.org/wiki/Udbetaling_Danmark", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Register linkage of benefits data with civil, housing, business, tax, income, health and employment registers under a 2015 legal authorization", "About 60 profiling and machine-learning models developed by NNIT to ATP's specifications", "A UDK/ATP control team (HOK) that manually screens model output into established control cases and investigates them", "A legal duty to also process all externally reported cases, which the control team says limits the share drawn from the models to about 30%" ], "must_hold": [ "Legal basis for register linkage and model use remains in force despite Amnesty's challenge and the dispute over classification under the EU AI Act", "Control-team capacity of about 5,000 to 6,000 cases a year", "Models continue to be monitored on their training cases, since UDK documents no evaluation metrics", "Political tolerance of the system after the November 2024 criticism" ], "echoes": [ { "setting": "2017 and 2018 controls over 2.7 million screened beneficiaries, as reported in a July 2019 report cited by Automating Society 2020: 705 and 1,094 cases with payments stopped, adjusted or repaid", "outcome": "recurred", "source": "https://automatingsociety.algorithmwatch.org/report2020/denmark/" }, { "setting": "dm.dk FOI report, October 2024, as read by an earlier check (not re-read): nearly all 1.2 million pensioners screened, 36 cases in 2023 ending with a benefit stopped or reduced", "outcome": "recurred", "source": "dm.dk FOI report, October 2024, as summarized in an earlier check of this case; consistent with the Model Abroad row of Amnesty's Table 5" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Register linkage of benefits data with civil, housing, business, tax, income, health and employment registers under a 2015 legal authorization", "About 60 profiling and machine-learning models developed by NNIT to ATP's specifications", "A UDK/ATP control team (HOK) that manually screens model output into established control cases and investigates them", "A legal duty to also process all externally reported cases, which the control team says limits the share drawn from the models to about 30%" ], "must_hold": [ "Legal basis for register linkage and model use remains in force despite Amnesty's challenge and the dispute over classification under the EU AI Act", "Control-team capacity of about 5,000 to 6,000 cases a year", "Models continue to be monitored on their training cases, since UDK documents no evaluation metrics", "Political tolerance of the system after the November 2024 criticism" ], "echoes": [ { "setting": "2017 and 2018 controls over 2.7 million screened beneficiaries, as reported in a July 2019 report cited by Automating Society 2020: 705 and 1,094 cases with payments stopped, adjusted or repaid", "outcome": "recurred", "source": "https://automatingsociety.algorithmwatch.org/report2020/denmark/" }, { "setting": "dm.dk FOI report, October 2024, as read by an earlier check (not re-read): nearly all 1.2 million pensioners screened, 36 cases in 2023 ending with a benefit stopped or reduced", "outcome": "recurred", "source": "dm.dk FOI report, October 2024, as summarized in an earlier check of this case; consistent with the Model Abroad row of Amnesty's Table 5" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Register access and model use need a lawful basis. Staff must investigate the flagged cases. The cited material does not provide evaluation measures that would establish how well the controls perform." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://algorithmwatch.org/en/udbetaling-danmark/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.atp.dk/en/about-atp; https://da.wikipedia.org/wiki/Udbetaling_Danmark", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public administration: social benefits fraud control", "segment": "National welfare payments authority" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State (EUR 18/8709/2024), 12 Nov 2024, Table 5 'Statistics for data-driven controls used by UDK in 2023', p. 43.", "title": "Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State", "url": "https://www.amnesty.org/en/documents/eur18/8709/2024/en/", "date": "2024-11-12", "written_by": "independent", "quote": "135 Cases with revenue – the number of cases that were found to be fraudulent or erroneous after an investigation was conducted" }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, full report PDF, p. 30 (Fraud-control algorithms).", "title": "Coded Injustice (PDF), chapter on fraud-control algorithms", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "As of 2019, UDK were using up to 60 different algorithms for fraud detection and other purposes." }, { "kind": "report", "citation": "Udbetaling Danmark, letter to Amnesty International on 2023 performance of data-driven controls, as reproduced in Amnesty International (2024), Coded Injustice, pp. 42-43.", "title": "UDK statement on how it reports cases with revenue (reproduced in Coded Injustice)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "deployer", "quote": "Finally, we report how many cases result in citizens having their benefits stopped or reduced (number of cases with revenue)." }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, p. 44, assessment of effectiveness and interview with a UDK/ATP control official (11 Jan 2024).", "title": "Coded Injustice, p. 44 (effectiveness of the fraud-control algorithms)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "It is questionable therefore whether the use of fraud control algorithms is effective at identifying fraud in this context." }, { "kind": "press release", "citation": "Amnesty International (2024). Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups – report. Press release, 12 Nov 2024.", "title": "Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups", "url": "https://www.amnesty.org/en/latest/news/2024/11/denmark-ai-powered-welfare-system-fuels-mass-surveillance-and-risks-discriminating-against-marginalized-groups/", "date": "2024-11-12", "written_by": "independent", "quote": "UDK and ATP use a system of up to 60 algorithmic models purportedly designed to detect social benefits fraud and flag individuals for further investigations." }, { "kind": "other", "citation": "University of Copenhagen, Faculty of Law (n.d.). Offentlig AI database entry 'Kontrol med udbetaling af ydelser' (Udbetaling Danmark; status: I brug; supplier NNIT A/S; about 60 models as of 2019).", "title": "Kontrol med udbetaling af ydelser (Offentlig AI database entry)", "url": "https://offentlig-ai.dk/projekter/kontrol-med-udbetaling-ydelser", "written_by": "independent", "quote": "Udbetaling Danmark udvikler og bruger modeller til kontrol på områder, hvor myndigheden har erfaret, at der forekommer uberettigede udbetalinger." }, { "kind": "report", "citation": "Kayser-Bril, N. (2020). In a quest to optimize welfare management, Denmark built a surveillance behemoth. AlgorithmWatch, 6 Aug 2020.", "title": "In a quest to optimize welfare management, Denmark built a surveillance behemoth", "url": "https://algorithmwatch.org/en/udbetaling-danmark/", "date": "2020-08-06", "written_by": "independent", "quote": "unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros)" }, { "kind": "report", "citation": "Alfter, B. (2020). Denmark chapter, section 'Controlling social welfare recipients'. In AlgorithmWatch and Bertelsmann Stiftung, Automating Society Report 2020.", "title": "Automating Society Report 2020: Denmark", "url": "https://automatingsociety.algorithmwatch.org/report2020/denmark/", "written_by": "independent", "quote": "leading only to 705 cases in 2017 and 1094 cases in 2018 where the payments were stopped, adjusted, or paid back because of mistakes" }, { "kind": "news report", "citation": "Amnesty International Algorithmic Accountability Lab (2024). How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System. Global Investigative Journalism Network, 14 Nov 2024.", "title": "How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System", "url": "https://gijn.org/stories/amnesty-internationals-investigation-algorithms-denmarks-welfare-system/", "date": "2024-11-14", "written_by": "independent", "quote": "ATP is responsible for designing the fraud control components of UDK's Joint Data Unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0336-source-0001", "AIBD-0336-source-0002", "AIBD-0336-source-0003", "AIBD-0336-source-0004", "AIBD-0336-source-0005", "AIBD-0336-source-0006", "AIBD-0336-source-0007", "AIBD-0336-source-0008", "AIBD-0336-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State (EUR 18/8709/2024), 12 Nov 2024, Table 5 'Statistics for data-driven controls used by UDK in 2023', p. 43.", "title": "Coded Injustice: Surveillance and Discrimination in Denmark's Automated Welfare State", "url": "https://www.amnesty.org/en/documents/eur18/8709/2024/en/", "date": "2024-11-12", "written_by": "independent", "quote": "135 Cases with revenue – the number of cases that were found to be fraudulent or erroneous after an investigation was conducted" }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, full report PDF, p. 30 (Fraud-control algorithms).", "title": "Coded Injustice (PDF), chapter on fraud-control algorithms", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "As of 2019, UDK were using up to 60 different algorithms for fraud detection and other purposes." }, { "kind": "report", "citation": "Udbetaling Danmark, letter to Amnesty International on 2023 performance of data-driven controls, as reproduced in Amnesty International (2024), Coded Injustice, pp. 42-43.", "title": "UDK statement on how it reports cases with revenue (reproduced in Coded Injustice)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "deployer", "quote": "Finally, we report how many cases result in citizens having their benefits stopped or reduced (number of cases with revenue)." }, { "kind": "report", "citation": "Amnesty International (2024). Coded Injustice, p. 44, assessment of effectiveness and interview with a UDK/ATP control official (11 Jan 2024).", "title": "Coded Injustice, p. 44 (effectiveness of the fraud-control algorithms)", "url": "https://www.amnesty.org/en/wp-content/uploads/2024/11/EUR1887092024ENGLISH.pdf", "date": "2024-11-12", "written_by": "independent", "quote": "It is questionable therefore whether the use of fraud control algorithms is effective at identifying fraud in this context." }, { "kind": "press release", "citation": "Amnesty International (2024). Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups – report. Press release, 12 Nov 2024.", "title": "Denmark: AI-powered welfare system fuels mass surveillance and risks discriminating against marginalized groups", "url": "https://www.amnesty.org/en/latest/news/2024/11/denmark-ai-powered-welfare-system-fuels-mass-surveillance-and-risks-discriminating-against-marginalized-groups/", "date": "2024-11-12", "written_by": "independent", "quote": "UDK and ATP use a system of up to 60 algorithmic models purportedly designed to detect social benefits fraud and flag individuals for further investigations." }, { "kind": "other", "citation": "University of Copenhagen, Faculty of Law (n.d.). Offentlig AI database entry 'Kontrol med udbetaling af ydelser' (Udbetaling Danmark; status: I brug; supplier NNIT A/S; about 60 models as of 2019).", "title": "Kontrol med udbetaling af ydelser (Offentlig AI database entry)", "url": "https://offentlig-ai.dk/projekter/kontrol-med-udbetaling-ydelser", "written_by": "independent", "quote": "Udbetaling Danmark udvikler og bruger modeller til kontrol på områder, hvor myndigheden har erfaret, at der forekommer uberettigede udbetalinger." }, { "kind": "report", "citation": "Kayser-Bril, N. (2020). In a quest to optimize welfare management, Denmark built a surveillance behemoth. AlgorithmWatch, 6 Aug 2020.", "title": "In a quest to optimize welfare management, Denmark built a surveillance behemoth", "url": "https://algorithmwatch.org/en/udbetaling-danmark/", "date": "2020-08-06", "written_by": "independent", "quote": "unveiled half a billion Danish crowns in erroneous payment (approximately 70 million euros)" }, { "kind": "report", "citation": "Alfter, B. (2020). Denmark chapter, section 'Controlling social welfare recipients'. In AlgorithmWatch and Bertelsmann Stiftung, Automating Society Report 2020.", "title": "Automating Society Report 2020: Denmark", "url": "https://automatingsociety.algorithmwatch.org/report2020/denmark/", "written_by": "independent", "quote": "leading only to 705 cases in 2017 and 1094 cases in 2018 where the payments were stopped, adjusted, or paid back because of mistakes" }, { "kind": "news report", "citation": "Amnesty International Algorithmic Accountability Lab (2024). How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System. Global Investigative Journalism Network, 14 Nov 2024.", "title": "How We Did It: Amnesty International's Investigation of Algorithms in Denmark's Welfare System", "url": "https://gijn.org/stories/amnesty-internationals-investigation-algorithms-denmarks-welfare-system/", "date": "2024-11-14", "written_by": "independent", "quote": "ATP is responsible for designing the fraud control components of UDK's Joint Data Unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "UDK/ATP ML fraud models (about 60 since 2019): 2023 figures of 135, 36 and 72 cases with revenue from three models; Amnesty (Nov 2024) calls their effectiveness questionable.", "account": "Udbetaling Danmark (UDK), the Danish public authority that has centralized welfare payments since 2012, has its benefits administered by ATP (Arbejdsmarkedets Tillaegspension). ATP designs the fraud-control components of UDK's Joint Data Unit (Den Faelles Dataenhed, established 2014 per the University of Copenhagen's Offentlig AI database). NNIT A/S is the supplier. As of 2019 UDK was reported to use up to 60 algorithms for fraud detection and other purposes. The registry describes profiling and machine learning (it names Naive Bayes classification and cluster analysis) run on linked register data. In a letter to Amnesty International before November 2024, UDK supplied its 2023 statistics for three data-driven controls. The “Really Single” model produced 412 requested cases, 292 established control cases and 135 cases with revenue (benefits stopped or reduced). “Model Abroad” (unreported departures, state and disability pension) gave 511, 351 and 36. The parental-leave controls including the “Fictitious Employment” model gave 491, 207 and 72. UDK cautioned that cases with revenue in a year are not necessarily a proportion of cases established that year. A UDK/ATP control official told Amnesty the team manages 5,000 to 6,000 cases a year, of which around 1,800 come from the models. Amnesty's report of 12 November 2024 concludes it is questionable whether the algorithms are effective. It finds most opened cases end with no further action and the system risks discrimination and mass surveillance. A Copenhagen Municipality control official said more often than not there is no fraud in the cases UDK sends. Background sources give earlier yields. AlgorithmWatch (2020) reports 2017 and 2018 controls unveiled about half a billion kroner in erroneous payments. The Automating Society 2020 Denmark chapter cites a July 2019 report. It counted 705 cases in 2017 and 1,094 in 2018 where payments were stopped, adjusted or repaid, out of 2.7 million screened beneficiaries. The dm.dk FOI report (October 2024) and ATP's page are not among the sources. The report said nearly all 1.2 million pensioners were screened and only 36 cases in 2023 ended with a benefit stopped or reduced. That matches the Model Abroad row. ATP's page credits 260 million kr. a year of savings to IT modernization generally. The 2023 figures carry no day." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Udbetaling Danmark / ATP Den Faelles Dataenhed fraud-control models (about 60 profiling and machine-learning models as of 2019, including 'Really Single', 'Model Abroad' and 'Fictitious Employment')" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "ATP (Arbejdsmarkedets Tillaegspension) with NNIT A/S as supplier, for Udbetaling Danmark" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Udbetaling Danmark's fraud-control models may have produced 135, 36 and 72 cases with benefits stopped or reduced in 2023", "headline": "UDK fraud models may have yielded 135, 36 and 72 cases" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f17dd05704428e7ad3e78e0e01f15067f7cee4c98a7dc6f09bc1eea7d43f4f91" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Udbetaling Danmark (UDK), the Danish public authority that has centralized welfare payments since 2012, has its benefits administered by ATP (Arbejdsmarkedets Tillaegspension). ATP designs the fraud-control components of UDK's Joint Data Unit (Den Faelles Dataenhed, established 2014 per the University of Copenhagen's Offentlig AI database). NNIT A/S is the supplier. As of 2019 UDK was reported to use up to 60 algorithms for fraud detection and other purposes. The registry describes profiling and machine learning (it names Naive Bayes classification and cluster analysis) run on linked register data. In a letter to Amnesty International before November 2024, UDK supplied its 2023 statistics for three data-driven controls. The “Really Single” model produced 412 requested cases, 292 established control cases and 135 cases with revenue (benefits stopped or reduced). “Model Abroad” (unreported departures, state and disability pension) gave 511, 351 and 36. The parental-leave controls including the “Fictitious Employment” model gave 491, 207 and 72. UDK cautioned that cases with revenue in a year are not necessarily a proportion of cases established that year. A UDK/ATP control official told Amnesty the team manages 5,000 to 6,000 cases a year, of which around 1,800 come from the models. Amnesty's report of 12 November 2024 concludes it is questionable whether the algorithms are effective. It finds most opened cases end with no further action and the system risks discrimination and mass surveillance. A Copenhagen Municipality control official said more often than not there is no fraud in the cases UDK sends. Background sources give earlier yields. AlgorithmWatch (2020) reports 2017 and 2018 controls unveiled about half a billion kroner in erroneous payments. The Automating Society 2020 Denmark chapter cites a July 2019 report. It counted 705 cases in 2017 and 1,094 in 2018 where payments were stopped, adjusted or repaid, out of 2.7 million screened beneficiaries. The dm.dk FOI report (October 2024) and ATP's page are not among the sources. The report said nearly all 1.2 million pensioners were screened and only 36 cases in 2023 ended with a benefit stopped or reduced. That matches the Model Abroad row. ATP's page credits 260 million kr. a year of savings to IT modernization generally. The 2023 figures carry no day.", "replication": { "produced_by": [ "Register linkage of benefits data with civil, housing, business, tax, income, health and employment registers under a 2015 legal authorization", "About 60 profiling and machine-learning models developed by NNIT to ATP's specifications", "A UDK/ATP control team (HOK) that manually screens model output into established control cases and investigates them", "A legal duty to also process all externally reported cases, which the control team says limits the share drawn from the models to about 30%" ], "must_hold": [ "Legal basis for register linkage and model use remains in force despite Amnesty's challenge and the dispute over classification under the EU AI Act", "Control-team capacity of about 5,000 to 6,000 cases a year", "Models continue to be monitored on their training cases, since UDK documents no evaluation metrics", "Political tolerance of the system after the November 2024 criticism" ], "echoes": [ { "setting": "2017 and 2018 controls over 2.7 million screened beneficiaries, as reported in a July 2019 report cited by Automating Society 2020: 705 and 1,094 cases with payments stopped, adjusted or repaid", "outcome": "recurred", "source": "https://automatingsociety.algorithmwatch.org/report2020/denmark/" }, { "setting": "dm.dk FOI report, October 2024, as read by an earlier check (not re-read): nearly all 1.2 million pensioners screened, 36 cases in 2023 ending with a benefit stopped or reduced", "outcome": "recurred", "source": "dm.dk FOI report, October 2024, as summarized in an earlier check of this case; consistent with the Model Abroad row of Amnesty's Table 5" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Models examine linked public registers and flag recipients for review. A UDK/ATP team that handles about 5,000 to 6,000 cases a year screens the results and investigates selected cases. Around 1,800 of its annual cases come from the models." } } }, "summary": { "total": 91, "addressed": 91, "known": 35, "reviewed": 0, "record_mapped": 35, "not_assessed": 56, "complete": false, "by_state": { "value_present": 35, "not_reported": 0, "unresolved": 0, "not_assessed": 56, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0184", "case_url": "records/AIBD-0184.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Assess the fraud risk of Universal Credit advance requests and refer high-risk requests to a caseworker for review." }, { "label": "AI contribution", "value": "The NAO describes a model trained on historical fraud and error data that scores advance requests and refers those above a threshold. Every payment decision is made by a caseworker, so the model's contribution is prioritization. Developer and deployer are the same public body (DWP). The NAO, an independent auditor, wrote the report." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0184", "title": "DWP's Universal Credit advances machine-learning model saved an estimated £4.4 million from 2022–23 to 2024–25", "summary": "The National Audit Office reports that DWP's model, in use since May 2022, flags high-risk advance requests for caseworker review, with estimated savings of £4.4 million and about three times the h...", "date_observed": "2025-10-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The National Audit Office (NAO) published “Tackling benefit overpayments due to fraud and error” on 22 October 2025. It states that since May 2022 the Department for Work & Pensions (DWP) has used a machine learning model. The model flags potentially fraudulent claims for Universal Credit advances. DWP estimates it saved £4.4 million from 2022–23 to 2024–25. The model scores new claims and refers those above a threshold to a caseworker. Decisions are made by DWP staff. DWP cannot disaggregate the model's costs from wider spending on data analytics. The NAO reports DWP's finding that the model is around three times more effective at identifying fraud risk than a randomized control group sample. It reports a median payment delay of one day for referred requests. DWP's July 2025 fairness analysis for 2024–25 showed older claimants (age groups 45 to 54 and above) and non-UK nationals over-referred for review. Fairness analysis was possible on only one of nine protected characteristics. Four further models were in development. Civil Service World (Jim Dunton, 22 October 2025) reported the NAO's warnings on data infrastructure. It quoted Transformation Minister Andrew Western saying that the one machine learning model has made the department significantly more effective.", "category": "general benefit", "unexpected": false, "purpose": "Assess the fraud risk of Universal Credit advance requests and refer high-risk requests to a caseworker for review.", "system": "Universal Credit advances machine learning model", "model_version": null, "developer": "Department for Work & Pensions (DWP)", "deployer": "Department for Work & Pensions (DWP)", "location": null, "domain": "Benefit fraud and error", "segment": "Universal Credit advances", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "The saving is an aggregate departmental estimate over three years, not an attributable outcome." }, { "group": "societal", "depth": null, "reason": "No outcome for claimants or taxpayers beyond the aggregate estimate is documented. Referred claimants bear an added evidence burden." } ], "ai_role": { "status": "assessed", "statement": "The NAO describes a model trained on historical fraud and error data that scores advance requests and refers those above a threshold. Every payment decision is made by a caseworker, so the model's contribution is prioritization. Developer and deployer are the same public body (DWP). The NAO, an independent auditor, wrote the report." }, "evidence": [ { "kind": "report", "citation": "National Audit Office, 'Tackling benefit overpayments due to fraud and error', 22 October 2025, paragraphs 15-16 and 3.4-3.15", "title": "Tackling benefit overpayments due to fraud and error", "url": "https://www.nao.org.uk/reports/tackling-benefit-overpayments-due-to-fraud-and-error/", "date": "2025-10-22", "written_by": "independent", "quote": "Since May 2022, DWP has used a machine learning model to flag potentially fraudulent claims for UC advances, saving an estimated £4.4 million." }, { "kind": "news report", "citation": "Jim Dunton, Civil Service World, 22 October 2025", "title": "Digital and data challenges could hamper DWP's fraud strategy, NAO warns", "url": "https://www.civilserviceworld.com/news/article/digital-and-data-challenges-could-hamper-dwps-fraud-strategy-nao-warns", "date": "2025-10-22", "written_by": "independent", "quote": "our one machine learning model has made us significantly more effective" }, { "kind": "report", "citation": "Department for Work & Pensions (2026). Effectiveness Assessment of Universal Credit Advances Model, 1 April 2025 to 31 March 2026. GOV.UK, 9 July 2026.", "title": "Effectiveness Assessment of Universal Credit Advances Model", "url": "https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model", "date": "2026-07-09", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "A model trained on historical fraud and error data in advance claims. Referrals above a threshold are reviewed by caseworkers alongside control-group referrals with scores removed" ], "must_hold": [ "A human decision on every advance", "Regular fairness assessments (age is the only protected characteristic in the model)", "Feedback of review outcomes into the model" ], "echoes": [ { "date": null, "setting": "DWP fairness assessment of the model for 1 April 2024 to 31 March 2025 (2025–07)", "model_version": null, "outcome": "recurred", "source": "NAO: around three times more effective than a random control sample; over-referral of older and non-UK claimants" }, { "date": null, "setting": "Four further DWP machine learning models in development (2025–26)", "model_version": null, "outcome": "unknown", "source": "NAO: deployment decisions expected in 2025-26" } ] }, "paired_incident": null, "headline": "Universal Credit advances model saves an estimated £4.4 million", "metric": "saving an estimated £4.4 million from 2022–23 to 2024–25 by DWP's estimate", "comparator": { "against": "a randomised control group sample of advance requests", "before": "fraud-risk identification rate of the random control sample (not quantified)", "after": "around three times more effective at identifying fraud risk", "basis": "measured", "quote": "In terms of performance, DWP found the model to be around three times more effective at identifying fraud risk than a randomised control group sample.", "url": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf" }, "later": { "status": "persisted", "date": "2026-07-09", "note": "DWP's effectiveness assessment for April 2025 to March 2026 found the retrained model 2.5 times more effective than a random control group and concluded it should keep operating.", "quote": "the model is 2.5 times more effective at identifying high-risk advances than a random control group, increasing the precision of referrals and enabling human decision-makers to focus on the riskiest cases", "url": "https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model" }, "value": { "amount": "£4.4 million", "what": "advances that would otherwise have been overpaid, saved from 2022-23 to 2024-25", "who": "Department for Work & Pensions", "estimate": true, "quote": "DWP estimates that, from 2022-23 to 2024-25, the model generated savings of £4.4 million that would otherwise have been overpaid.", "url": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf" }, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf; https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model; https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Department_for_Work_and_Pensions; https://en.wikipedia.org/wiki/Department_for_Work_and_Pensions", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report, one independent news report and the deployer's report. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0184", "catalog_version": "1.0.0", "source_fingerprint": "537199b2c3da80a94f17562f462dc8ae10d28abb15116260b2664d1adffa355e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0184-source-0001", "AIBD-0184-source-0002", "AIBD-0184-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0184-legacy-value", "DIV-0031" ], "role": "Existing monetary observations and Fructus statements with matched AIBD case links; each statement retains its own review and eligibility status" }, "provenance": { "kind": "curatorial", "basis": "Linked to the current Fructus dataset by its validated case identifier. 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GOV.UK, 9 July 2026.", "title": "Effectiveness Assessment of Universal Credit Advances Model", "url": "https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model", "date": "2026-07-09", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0184-source-0001", "AIBD-0184-source-0002", "AIBD-0184-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "National Audit Office, 'Tackling benefit overpayments due to fraud and error', 22 October 2025, paragraphs 15-16 and 3.4-3.15", "title": "Tackling benefit overpayments due to fraud and error", "url": "https://www.nao.org.uk/reports/tackling-benefit-overpayments-due-to-fraud-and-error/", "date": "2025-10-22", "written_by": "independent", "quote": "Since May 2022, DWP has used a machine learning model to flag potentially fraudulent claims for UC advances, saving an estimated £4.4 million." }, { "kind": "news report", "citation": "Jim Dunton, Civil Service World, 22 October 2025", "title": "Digital and data challenges could hamper DWP's fraud strategy, NAO warns", "url": "https://www.civilserviceworld.com/news/article/digital-and-data-challenges-could-hamper-dwps-fraud-strategy-nao-warns", "date": "2025-10-22", "written_by": "independent", "quote": "our one machine learning model has made us significantly more effective" }, { "kind": "report", "citation": "Department for Work & Pensions (2026). Effectiveness Assessment of Universal Credit Advances Model, 1 April 2025 to 31 March 2026. GOV.UK, 9 July 2026.", "title": "Effectiveness Assessment of Universal Credit Advances Model", "url": "https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model", "date": "2026-07-09", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The National Audit Office reports that DWP's model, in use since May 2022, flags high-risk advance requests for caseworker review, with estimated savings of £4.4 million and about three times the h...", "account": "The National Audit Office (NAO) published “Tackling benefit overpayments due to fraud and error” on 22 October 2025. It states that since May 2022 the Department for Work & Pensions (DWP) has used a machine learning model. The model flags potentially fraudulent claims for Universal Credit advances. DWP estimates it saved £4.4 million from 2022–23 to 2024–25. The model scores new claims and refers those above a threshold to a caseworker. Decisions are made by DWP staff. DWP cannot disaggregate the model's costs from wider spending on data analytics. The NAO reports DWP's finding that the model is around three times more effective at identifying fraud risk than a randomized control group sample. It reports a median payment delay of one day for referred requests. DWP's July 2025 fairness analysis for 2024–25 showed older claimants (age groups 45 to 54 and above) and non-UK nationals over-referred for review. Fairness analysis was possible on only one of nine protected characteristics. Four further models were in development. Civil Service World (Jim Dunton, 22 October 2025) reported the NAO's warnings on data infrastructure. It quoted Transformation Minister Andrew Western saying that the one machine learning model has made the department significantly more effective." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Universal Credit advances machine learning model" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Department for Work & Pensions (DWP)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "DWP's Universal Credit advances machine-learning model saved an estimated £4.4 million from 2022–23 to 2024–25", "headline": "Universal Credit advances model saves an estimated £4.4 million" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "537199b2c3da80a94f17562f462dc8ae10d28abb15116260b2664d1adffa355e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The National Audit Office (NAO) published “Tackling benefit overpayments due to fraud and error” on 22 October 2025. It states that since May 2022 the Department for Work & Pensions (DWP) has used a machine learning model. The model flags potentially fraudulent claims for Universal Credit advances. DWP estimates it saved £4.4 million from 2022–23 to 2024–25. The model scores new claims and refers those above a threshold to a caseworker. Decisions are made by DWP staff. DWP cannot disaggregate the model's costs from wider spending on data analytics. The NAO reports DWP's finding that the model is around three times more effective at identifying fraud risk than a randomized control group sample. It reports a median payment delay of one day for referred requests. DWP's July 2025 fairness analysis for 2024–25 showed older claimants (age groups 45 to 54 and above) and non-UK nationals over-referred for review. Fairness analysis was possible on only one of nine protected characteristics. Four further models were in development. Civil Service World (Jim Dunton, 22 October 2025) reported the NAO's warnings on data infrastructure. It quoted Transformation Minister Andrew Western saying that the one machine learning model has made the department significantly more effective.", "replication": { "produced_by": [ "A model trained on historical fraud and error data in advance claims. Referrals above a threshold are reviewed by caseworkers alongside control-group referrals with scores removed" ], "must_hold": [ "A human decision on every advance", "Regular fairness assessments (age is the only protected characteristic in the model)", "Feedback of review outcomes into the model" ], "echoes": [ { "setting": "DWP fairness assessment of the model for 1 April 2024 to 31 March 2025 (2025–07)", "outcome": "recurred", "source": "NAO: around three times more effective than a random control sample; over-referral of older and non-UK claimants" }, { "setting": "Four further DWP machine learning models in development (2025–26)", "outcome": "unknown", "source": "NAO: deployment decisions expected in 2025-26" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 36, "reviewed": 0, "record_mapped": 36, "not_assessed": 55, "complete": false, "by_state": { "value_present": 36, "not_reported": 0, "unresolved": 0, "not_assessed": 55, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0145", "case_url": "records/AIBD-0145.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Match and analyze data from many sources to identify tax non-compliance and risky repayment claims" }, { "label": "AI contribution", "value": "Yield attribution to Connect comes from HMRC's FOI response as reported by Pinsent Masons. No independent evaluation apportions yield between the system and caseworkers. The NAO's audit report describes TRUCE/IDRS as automated risk-assessment of repayment claims whose controls it reviewed, without quantifying stopped repayments. The cited sources describe the system as data analytics and risk rules rather than machine learning." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0145", "title": "HMRC's Connect data-matching system: about £4.6 billion of additional yield in 2024–25", "summary": "HMRC's Connect data-matching system yielded ~£4.6bn extra tax in 2024–25 per FOI; C&AG confirms its use to stop risky repayments", "date_observed": "2025-03-31", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "HM Revenue & Customs launched Connect in 2010–11. Its annual report calls it an IT system that joins a billion pieces of data across its tax regimes to help identify fraud. In a freedom-of-information response reported by Pinsent Masons on 8 October 2025, HMRC said Connect cases generated an average £3.4 billion of additional annual yield. It was about £4.6 billion in 2024–25, with around 4,300 staff using it. The Comptroller and Auditor General's report in HMRC's 2024–25 accounts records that HMRC used TRUCE (Transaction Risking Upstream in a Connect Environment). It served to identify and stop VAT and income-tax repayments needing investigation. IDRS replaced it from August 2023. 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No independent evaluation apportions yield between the system and caseworkers. The NAO's audit report describes TRUCE/IDRS as automated risk-assessment of repayment claims whose controls it reviewed, without quantifying stopped repayments. The cited sources describe the system as data analytics and risk rules rather than machine learning." }, "evidence": [ { "kind": "report", "citation": "Comptroller and Auditor General (2025). Report on the Trust Statement, in HM Revenue & Customs Annual Report and Accounts 2024 to 2025, Key audit matter 5 (printed pp. 177-178).", "title": "HMRC Annual Report and Accounts 2024 to 2025 – Report of the Comptroller and Auditor General", "url": "https://assets.publishing.service.gov.uk/media/687e5d318adf4250705c96d8/HMRC_annual_report_and_accounts_2024_to_2025.pdf", "date": null, "written_by": "independent", "quote": "For both VAT and income tax, HMRC used TRUCE (the Transaction Risking Upstream in a Connect Environment) to identify and stop repayments that may require investigation." }, { "kind": "report", "citation": "HM Revenue & Customs (2011). Annual Report and Accounts 2010-11, HC 981, p. 10.", "title": "HMRC Annual Report and Accounts 2010-11", "url": "https://assets.publishing.service.gov.uk/media/5a78b230ed915d07d35b1b53/annual-report-accounts-1011.pdf", "date": null, "written_by": "deployer", "quote": "We successfully launched the ‘Connect’ IT system which joins a billion pieces of data from across our tax regimes to help identify fraud." }, { "kind": "news report", "citation": "Pinsent Masons Out-Law (2025). HMRC finds value in 'big data' after bringing in extra £4.6 billion, 8 Oct 2025 (reporting an HMRC FOI response).", "title": "HMRC finds value in ‘big data’ after bringing in extra £4.6billion", "url": "https://www.pinsentmasons.com/out-law/news/hmrc-big-data-tax-revenue-boost-4-6-billion", "date": "2025-10-08", "written_by": "independent", "quote": "HMRC, in a freedom of information request to Pinsent Masons, said it generated on average £3.4bn in additional annual yield from Connect cases" }, { "kind": "report", "citation": "HM Revenue & Customs (2026). Annual Report and Accounts 2025 to 2026, HC 350. 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The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0145", "catalog_version": "1.0.0", "source_fingerprint": "609307d763754e7933c1086b7de496e0819b101a501f5bc3aa8947927223a934", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0145-source-0001", "AIBD-0145-source-0002", "AIBD-0145-source-0003", "AIBD-0145-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0145-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "HM Revenue & Customs (HMRC)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "Yield attribution to Connect comes from HMRC's FOI response as reported by Pinsent Masons. No independent evaluation apportions yield between the system and caseworkers. The NAO's audit report describes TRUCE/IDRS as automated risk-assessment of repayment claims whose controls it reviewed, without quantifying stopped repayments. 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the report does not name Connect.", "quote": "Artificial intelligence (AI) and advanced analytics has already enabled the protection and recovery of £10 billion in tax between 2025 and 2026, and we are continuing to work at pace in this area, appointing our first ever Chief AI Officer in April 2026, to provide clear leadership and ensure the AI advancements that we make are responsible.", "url": "https://assets.publishing.service.gov.uk/media/6a4f66c406e7256c331df8a2/HMRC_annual_report_and_accounts_2025_to_2026.pdf" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0145" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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Its annual report calls it an IT system that joins a billion pieces of data across its tax regimes to help identify fraud. In a freedom-of-information response reported by Pinsent Masons on 8 October 2025, HMRC said Connect cases generated an average £3.4 billion of additional annual yield. It was about £4.6 billion in 2024–25, with around 4,300 staff using it. The Comptroller and Auditor General's report in HMRC's 2024–25 accounts records that HMRC used TRUCE (Transaction Risking Upstream in a Connect Environment). It served to identify and stop VAT and income-tax repayments needing investigation. IDRS replaced it from August 2023. Official sources describe data analytics and risk rules, not AI.", "benefit_type": "Increased Product Revenue" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Aggregate yield with no discrete attributable event." }, { "group": "organizational", "reason": "The £4.6 billion figure is HMRC's own annual attribution via FOI, not an audited event." }, { "group": "societal", "reason": "Aggregate national revenue figure." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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Its annual report calls it an IT system that joins a billion pieces of data across its tax regimes to help identify fraud. In a freedom-of-information response reported by Pinsent Masons on 8 October 2025, HMRC said Connect cases generated an average £3.4 billion of additional annual yield. It was about £4.6 billion in 2024–25, with around 4,300 staff using it. The Comptroller and Auditor General's report in HMRC's 2024–25 accounts records that HMRC used TRUCE (Transaction Risking Upstream in a Connect Environment). It served to identify and stop VAT and income-tax repayments needing investigation. IDRS replaced it from August 2023. Official sources describe data analytics and risk rules, not AI.", "replication": { "produced_by": [ "Linking of bank, marketplace, land-registry and other data to taxpayer records", "Risk rules and scorecards feeding compliance cases to about 4,300 staff", "Upstream risking of VAT and Self Assessment repayment claims (TRUCE, then IDRS)" ], "must_hold": [ "Legal gateways to third-party data", "Staff capacity to work cases generated by the system", "Governance of risk rules, which the C&AG reviews annually" ], "echoes": [ { "date": "2023-08-01", "setting": "TRUCE replaced by IDRS for VAT repayments from August 2023, Self Assessment migrated May 2024 (C&AG report; month only)", "outcome": "unknown", "source": "https://assets.publishing.service.gov.uk/media/687e5d318adf4250705c96d8/HMRC_annual_report_and_accounts_2024_to_2025.pdf" }, { "date": "2025-03-31", "setting": "Average annual yield £3.4bn from Connect cases over the period covered by the FOI response (Pinsent Masons, 8 October 2025)", "outcome": "recurred", "source": "https://www.pinsentmasons.com/out-law/news/hmrc-big-data-tax-revenue-boost-4-6-billion" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0092", "case_url": "records/AIBD-0092.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "AI-based riverine flood forecasting for ungauged basins to support early warning and anticipatory action." }, { "label": "AI contribution", "value": "The study directly evaluates Google Flood Hub’s machine-learning forecasts. It measures forecast performance, not a humanitarian outcome or a deployed change to the Mali Red Cross’s early-action protocol." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0092", "title": "Independent evaluation finds Google Flood Hub and GloFAS forecasts skillful beyond four days in Mali's frequently flooded regions, suggesting seven-day Red Cross early-action triggers", "summary": "Forecasts could allow earlier action in parts of Mali, but Google Flood Hub produced frequent false alarms. No benefit from an implemented early action was measured.", "date_observed": "2025-03-14", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Researchers evaluated Google Flood Hub and GloFAS forecasts against river-flow observations and records of flood impacts in Mali. Their results suggest that some early-action plans could extend from four to seven days without much loss of forecast skill. Performance varied across districts and thresholds, and no correctly predicted floods were found in the northern districts that were evaluated. At a seven-day lead time, Google Flood Hub’s false-alarm ratio was around 0.8 across discharge thresholds, compared with around 0.5 for GloFAS. The authors describe GloFAS as more balanced in several comparisons. They used a ten-day matching window to compare forecasts with reported impacts, which affects the interpretation of performance. This was a retrospective forecast evaluation; it did not measure benefits from an implemented early action. The researchers are independent of Google, although one coauthor works at the Joint Research Centre, which contributes to GloFAS.", "category": "general benefit", "unexpected": false, "purpose": "AI-based riverine flood forecasting for ungauged basins to support early warning and anticipatory action.", "system": "Google Flood Hub", "model_version": null, "developer": "Google", "deployer": "Evaluated for the Mali Red Cross Early Action Protocol; not documented as an operational trigger", "location": null, "domain": "Hydrology / flood forecasting", "segment": "Humanitarian anticipatory action", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "A forecast-skill evaluation is read as task accuracy; no early action is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Skill evaluation, no discrete event." }, { "group": "organizational", "depth": null, "reason": "Skill evaluation, no discrete event." }, { "group": "societal", "depth": null, "reason": "Skill evaluation, no discrete event." } ], "ai_role": { "status": "assessed", "statement": "The study directly evaluates Google Flood Hub’s machine-learning forecasts. 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The nine-day Hurricane Lee landfall forecast rests on vendor statements and press quotes of ECMWF staff. ECMWF documents read (Newsletters 177 and 178, science blog, Severe Event Catalog) do not record it." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0090", "title": "Independent 2023-season verification finds GraphCast and three peer AI models match the best operational cyclone track models", "summary": "CIRA/Colorado State evaluation: GraphCast and three AI models match top operational cyclone track models in 2023; adding all four cut consensus error 11% at 120 h.", "date_observed": "2024-09-08", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google DeepMind's GraphCast was run live on ECMWF's public charts from 2023. DeepMind's blog of 14 November 2023 says the live model predicted Hurricane Lee's landfall in Nova Scotia in September 2023 about nine days in advance. The blog says only “In September”, so the year is implied by its 14 November 2023 post date. The ECMWF publications read do not document the case. MIT Technology Review quoted ECMWF's Peter Dueben saying the models are so good they cannot be avoided. An independent CIRA/Colorado State evaluation applied NHC verification to May-November 2023 cyclones. It found GraphCast-operational and three other AI models' track errors comparable to the best operational models. 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The nine-day Hurricane Lee landfall forecast rests on vendor statements and press quotes of ECMWF staff. ECMWF documents read (Newsletters 177 and 178, science blog, Severe Event Catalog) do not record it." }, "evidence": [ { "kind": "preprint", "citation": "DeMaria, M., Franklin, J. L., Chirokova, G., Radford, J., DeMaria, R., Musgrave, K. D., Ebert-Uphoff, I. (2024). Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models. arXiv:2409.06735, submitted 8 Sep 2024; the preprint's disclaimer reads 'This work has been submitted to the Artificial Intelligence in the Earth Sciences American Meteorological Society journal'. The quote is on p. 23 of the full PDF, not on the abstract page.", "title": "Evaluation of Tropical Cyclone Track and Intensity Forecasts from AIWP Models", "url": "https://arxiv.org/abs/2409.06735", "date": "2024-09-08", "written_by": "independent", "quote": "When all four were added the track consensus improved by 11% at 120 h, with improvements that were statistically significant at 72-120 h." }, { "kind": "press release", "citation": "Google DeepMind (2023). GraphCast: AI model for faster and more accurate global weather forecasting. DeepMind blog, 14 November 2023.", "title": "GraphCast: AI model for faster and more accurate global weather forecasting", "url": "https://deepmind.google/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "date": "2023-11-14", "written_by": "makers", "quote": "a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia" }, { "kind": "preprint", "citation": "Lam, R., Sanchez-Gonzalez, A., Willson, M., et al. (2023). GraphCast: Learning skillful medium-range global weather forecasting. arXiv:2212.12794 (published in Science, 2023).", "title": "GraphCast: Learning skillful medium-range global weather forecasting", "url": "https://arxiv.org/abs/2212.12794", "date": "2023-08-04", "written_by": "makers", "quote": "GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets" }, { "kind": "news report", "citation": "MIT Technology Review (2023). Google DeepMind's weather AI can forecast extreme weather faster and more accurately. 14 November 2023.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately", "url": "https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "It showed that these models are so good that we cannot avoid them anymore." }, { "kind": "press release", "citation": "Rabier, F. (2023). The AIFS is launched (editorial). 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This forecast was that purpose." }, { "label": "AI contribution", "value": "The only sources for the nine-day Lee forecast are the maker's own blog and a news report quoting the maker's scientist. ECMWF's editorial confirms only that GraphCast ran experimentally on its charts. NHC's Lee report records exceptionally low official track errors without mentioning AI guidance. The AI output is therefore documented as a forecast, not as an input to any decision." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Canada" ] }, "record": { "id": "AIBD-0341", "title": "A GraphCast live run on ECMWF charts may have forecast Hurricane Lee's Nova Scotia landfall about nine days ahead", "summary": "DeepMind says a live GraphCast run on ECMWF's site forecast Lee's Nova Scotia landfall about nine days ahead; NHC records landfall 16 September 2023; no forecaster use of the run is documented.", "date_observed": "2023-09-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google DeepMind's blog post of 14 November 2023 describes a live version of its publicly available GraphCast model, deployed on the ECMWF website. In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "category": "general benefit", "unexpected": false, "purpose": "Machine-learning global medium-range weather forecasting (10 days, 0.25 degree), including severe-event prediction such as tropical cyclone tracks. This forecast was that purpose.", "system": "GraphCast (Google DeepMind graph neural network weather model), experimental live run on ECMWF public charts", "model_version": "GraphCast (live version deployed on the ECMWF website)", "developer": "DeepMind", "deployer": "ECMWF (experimental data-driven forecasts on its public charts pages)", "location": "Long Island, southwestern Nova Scotia, Canada (landfall)", "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service; AI research laboratory", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The nine-day claim is vendor-sourced and no forecaster use of the run is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual outcome is documented." }, { "group": "organizational", "depth": null, "reason": "General benefit. No cited source documents a forecast office using the GraphCast Lee run in its products." }, { "group": "societal", "depth": null, "reason": "General benefit. No public-safety decision is linked to the run in any cited source." } ], "ai_role": { "status": "assessed", "statement": "The only sources for the nine-day Lee forecast are the maker's own blog and a news report quoting the maker's scientist. ECMWF's editorial confirms only that GraphCast ran experimentally on its charts. NHC's Lee report records exceptionally low official track errors without mentioning AI guidance. The AI output is therefore documented as a forecast, not as an input to any decision." }, "evidence": [ { "kind": "press release", "citation": "Google DeepMind (2023). GraphCast: AI model for faster and more accurate global weather forecasting. DeepMind blog, 14 Nov 2023.", "title": "GraphCast: AI model for faster and more accurate global weather forecasting", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "date": "2023-11-14", "written_by": "makers", "quote": "a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia" }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Google DeepMind's weather AI can forecast extreme weather faster and more accurately. MIT Technology Review, 14 Nov 2023.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "In September, GraphCast accurately predicted that Hurricane Lee would make landfall in Nova Scotia nine days in advance, says Rémi Lam, a staff research scientist at Google DeepMind." }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Same article, comparison with conventional models.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately (conventional-model comparison)", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "Traditional weather forecasting models pinpointed the hurricane to Nova Scotia only six days in advance." }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). National Hurricane Center Tropical Cyclone Report: Hurricane Lee (AL132023), 5-16 September 2023. NOAA/NWS, 21 Mar 2024.", "title": "Tropical Cyclone Report: Hurricane Lee (AL132023)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "making landfall on Long Island, in southwestern Nova Scotia, later that day around 2000 UTC with maximum sustained winds of about 55 kt well east of the center" }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). Same report, forecast verification section (no AI model is mentioned anywhere in the report).", "title": "Tropical Cyclone Report: Hurricane Lee (track verification)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "Official track forecast errors were exceptionally low through 72 h, and below the previous 5-year period means through 120 h." }, { "kind": "press release", "citation": "Rabier, F. (2023). The AIFS is launched (editorial). ECMWF Newsletter No. 177, Autumn 2023.", "title": "The AIFS is launched", "url": "https://www.ecmwf.int/en/newsletter/177/editorial/aifs-launched", "date": null, "written_by": "deployer", "quote": "We have made these systems available on ECMWF's public charts pages, based on our own initial conditions." }, { "kind": "preprint", "citation": "Lam, R., Sanchez-Gonzalez, A., Willson, M., et al. (2022/2023). GraphCast: Learning skillful medium-range global weather forecasting. arXiv:2212.12794 (published in Science, Nov 2023).", "title": "GraphCast: Learning skillful medium-range global weather forecasting", "url": "https://arxiv.org/abs/2212.12794", "date": "2022-12-24", "written_by": "makers", "quote": "its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures" }, { "kind": "peer-reviewed study", "citation": "Olivetti L, Messori G (2024). Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast. Geoscientific Model Development 17:7915-7962, doi:10.5194/gmd-17-7915-2024.", "title": "Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast", "url": "https://gmd.copernicus.org/articles/17/7915/2024/", "date": "2024-11-07", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "GraphCast trained on decades of ERA5 reanalysis and run live from ECMWF initial conditions on ECMWF's experimental charts", "A long-lived Cabo Verde hurricane whose track was, per NHC, easier than normal to forecast (low climatology-persistence errors)", "Vendor retrospective comparison of its own run against conventional models" ], "must_hold": [ "Independent verification of the specific nine-day run against the official track record (not found in any ECMWF or NHC document among the sources)", "Access to ECMWF initial conditions for the live run", "A forecaster or authority actually consulting the AI run for it to become an attributable benefit" ], "echoes": [ { "date": null, "setting": "2023 season verification by CIRA of GraphCast and peer AI models against operational cyclone track guidance (existing record AIBD-0090)", "model_version": null, "outcome": "recurred", "source": "AIBD-0090 (DeMaria et al. 2024 preprint cited there)" } ] }, "paired_incident": null, "headline": "GraphCast may have forecast Lee's landfall nine days ahead", "metric": "forecast Lee's Nova Scotia landfall about nine days ahead", "comparator": { "against": "traditional physics-based forecasts of Hurricane Lee's landfall (DeepMind's own account)", "before": "only locked in on Nova Scotia about six days in advance", "after": "about nine days in advance", "basis": "assumed", "quote": "In September, a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. By contrast, traditional forecasts had greater variability in where and when landfall would occur, and only locked in on Nova Scotia about six days in advance.", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/" }, "later": { "status": "persisted", "date": "2024-11-07", "note": "An independent Uppsala University evaluation published 7 November 2024 confirmed GraphCast's general advantage over ECMWF's IFS HRES, including on near-surface temperature and wind extremes; it does not examine the Hurricane Lee forecast, and the NHC's March 2024 report on Lee names no AI model.", "quote": "Both data-driven models perform significantly better than ECMWF's IFS HRES globally and in most regions. Most impressively, GraphCast significantly outperforms IFS HRES across all regions and lead times.", "url": "https://gmd.copernicus.org/articles/17/7915/2024/" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/; https://gmd.copernicus.org/articles/17/7915/2024/; https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://en.wikipedia.org/wiki/European_Centre_for_Medium-Range_Weather_Forecasts", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study, one independent report, one independent news report, the makers' preprint, the makers' press release and the deployer's press release. 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model.", "quote": "Both data-driven models perform significantly better than ECMWF's IFS HRES globally and in most regions. 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In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual outcome is documented." }, { "group": "organizational", "reason": "General benefit. No cited source documents a forecast office using the GraphCast Lee run in its products." }, { "group": "societal", "reason": "General benefit. No public-safety decision is linked to the run in any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Machine-learning global medium-range weather forecasting (10 days, 0.25 degree), including severe-event prediction such as tropical cyclone tracks. 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This forecast was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-09-16", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/; https://gmd.copernicus.org/articles/17/7915/2024/; https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://en.wikipedia.org/wiki/European_Centre_for_Medium-Range_Weather_Forecasts", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "GraphCast trained on decades of ERA5 reanalysis and run live from ECMWF initial conditions on ECMWF's experimental charts", "A long-lived Cabo Verde hurricane whose track was, per NHC, easier than normal to forecast (low climatology-persistence errors)", "Vendor retrospective comparison of its own run against conventional models" ], "must_hold": [ "Independent verification of the specific nine-day run against the official track record (not found in any ECMWF or NHC document among the sources)", "Access to ECMWF initial conditions for the live run", "A forecaster or authority actually consulting the AI run for it to become an attributable benefit" ], "echoes": [ { "setting": "2023 season verification by CIRA of GraphCast and peer AI models against operational cyclone track guidance (existing record AIBD-0090)", "outcome": "recurred", "source": "AIBD-0090 (DeMaria et al. 2024 preprint cited there)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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GraphCast: AI model for faster and more accurate global weather forecasting. DeepMind blog, 14 Nov 2023.", "title": "GraphCast: AI model for faster and more accurate global weather forecasting", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "date": "2023-11-14", "written_by": "makers", "quote": "a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia" }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Google DeepMind's weather AI can forecast extreme weather faster and more accurately. MIT Technology Review, 14 Nov 2023.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "In September, GraphCast accurately predicted that Hurricane Lee would make landfall in Nova Scotia nine days in advance, says Rémi Lam, a staff research scientist at Google DeepMind." }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Same article, comparison with conventional models.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately (conventional-model comparison)", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "Traditional weather forecasting models pinpointed the hurricane to Nova Scotia only six days in advance." }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). National Hurricane Center Tropical Cyclone Report: Hurricane Lee (AL132023), 5-16 September 2023. NOAA/NWS, 21 Mar 2024.", "title": "Tropical Cyclone Report: Hurricane Lee (AL132023)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "making landfall on Long Island, in southwestern Nova Scotia, later that day around 2000 UTC with maximum sustained winds of about 55 kt well east of the center" }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). Same report, forecast verification section (no AI model is mentioned anywhere in the report).", "title": "Tropical Cyclone Report: Hurricane Lee (track verification)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "Official track forecast errors were exceptionally low through 72 h, and below the previous 5-year period means through 120 h." }, { "kind": "press release", "citation": "Rabier, F. (2023). The AIFS is launched (editorial). 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A comparison of IFS HRES, Pangu-Weather, and GraphCast. Geoscientific Model Development 17:7915-7962, doi:10.5194/gmd-17-7915-2024.", "title": "Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast", "url": "https://gmd.copernicus.org/articles/17/7915/2024/", "date": "2024-11-07", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0341-source-0001", "AIBD-0341-source-0002", "AIBD-0341-source-0003", "AIBD-0341-source-0004", "AIBD-0341-source-0005", "AIBD-0341-source-0006", "AIBD-0341-source-0007", "AIBD-0341-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Google DeepMind (2023). GraphCast: AI model for faster and more accurate global weather forecasting. DeepMind blog, 14 Nov 2023.", "title": "GraphCast: AI model for faster and more accurate global weather forecasting", "url": "https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/", "date": "2023-11-14", "written_by": "makers", "quote": "a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia" }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Google DeepMind's weather AI can forecast extreme weather faster and more accurately. MIT Technology Review, 14 Nov 2023.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "In September, GraphCast accurately predicted that Hurricane Lee would make landfall in Nova Scotia nine days in advance, says Rémi Lam, a staff research scientist at Google DeepMind." }, { "kind": "news report", "citation": "Heikkilä, M. (2023). Same article, comparison with conventional models.", "title": "Google DeepMind's weather AI can forecast extreme weather faster and more accurately (conventional-model comparison)", "url": "https://web.archive.org/web/20231114164801/https://www.technologyreview.com/2023/11/14/1083366/google-deepminds-weather-ai-can-forecast-extreme-weather-quicker-and-more-accurately/", "date": "2023-11-14", "written_by": "independent", "quote": "Traditional weather forecasting models pinpointed the hurricane to Nova Scotia only six days in advance." }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). National Hurricane Center Tropical Cyclone Report: Hurricane Lee (AL132023), 5-16 September 2023. NOAA/NWS, 21 Mar 2024.", "title": "Tropical Cyclone Report: Hurricane Lee (AL132023)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "making landfall on Long Island, in southwestern Nova Scotia, later that day around 2000 UTC with maximum sustained winds of about 55 kt well east of the center" }, { "kind": "report", "citation": "Blake, E. and Nepaul, H. (2024). Same report, forecast verification section (no AI model is mentioned anywhere in the report).", "title": "Tropical Cyclone Report: Hurricane Lee (track verification)", "url": "https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf", "date": "2024-03-21", "written_by": "independent", "quote": "Official track forecast errors were exceptionally low through 72 h, and below the previous 5-year period means through 120 h." }, { "kind": "press release", "citation": "Rabier, F. (2023). The AIFS is launched (editorial). ECMWF Newsletter No. 177, Autumn 2023.", "title": "The AIFS is launched", "url": "https://www.ecmwf.int/en/newsletter/177/editorial/aifs-launched", "written_by": "deployer", "quote": "We have made these systems available on ECMWF's public charts pages, based on our own initial conditions." }, { "kind": "preprint", "citation": "Lam, R., Sanchez-Gonzalez, A., Willson, M., et al. (2022/2023). GraphCast: Learning skillful medium-range global weather forecasting. arXiv:2212.12794 (published in Science, Nov 2023).", "title": "GraphCast: Learning skillful medium-range global weather forecasting", "url": "https://arxiv.org/abs/2212.12794", "date": "2022-12-24", "written_by": "makers", "quote": "its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures" }, { "kind": "peer-reviewed study", "citation": "Olivetti L, Messori G (2024). Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast. Geoscientific Model Development 17:7915-7962, doi:10.5194/gmd-17-7915-2024.", "title": "Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast", "url": "https://gmd.copernicus.org/articles/17/7915/2024/", "date": "2024-11-07", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "DeepMind says a live GraphCast run on ECMWF's site forecast Lee's Nova Scotia landfall about nine days ahead; NHC records landfall 16 September 2023; no forecaster use of the run is documented.", "account": "Google DeepMind's blog post of 14 November 2023 describes a live version of its publicly available GraphCast model, deployed on the ECMWF website. In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "GraphCast generated weather forecasts, including the projected path of Hurricane Lee." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "Researchers used the live GraphCast forecasts available through ECMWF." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "GraphCast." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "GraphCast (live version deployed on the ECMWF website)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "GraphCast (Google DeepMind graph neural network weather model), experimental live run on ECMWF public charts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "DeepMind" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "A GraphCast live run on ECMWF charts may have forecast Hurricane Lee's Nova Scotia landfall about nine days ahead", "headline": "GraphCast may have forecast Lee's landfall nine days ahead" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "9911fcda891b59bf2c4d643ddd69e62d178eee161f30fd0f122f939e02fca957" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Google DeepMind's blog post of 14 November 2023 describes a live version of its publicly available GraphCast model, deployed on the ECMWF website. In September 2023 it predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia. MIT Technology Review (14 November 2023) relayed the claim from DeepMind staff research scientist Rémi Lam. It added that traditional forecasting models pinpointed Nova Scotia only six days in advance. The article quotes no ECMWF staff on Lee. ECMWF's Newsletter 177 editorial (Autumn 2023) confirms that GraphCast, Pangu-Weather and FourCastNet were made available on ECMWF's public charts pages from ECMWF's own initial conditions. The NHC tropical cyclone report on Lee (21 March 2024) records the landfall. Lee, by then post-tropical, made landfall on Long Island in southwestern Nova Scotia around 2000 UTC on 16 September 2023. Maximum sustained winds were about 55 kt. NHC official track errors were exceptionally low through 72 h and below five-year means through 120 h. The report mentions no AI model. No ECMWF document recording the Lee forecast is on record, and the NHC 2023 verification report contains no AI-model entry. It found only an intensity-structure mention of Lee in the independent CIRA preprint. The forecast itself is dated only as “September” by the vendor. No cited source documents a forecaster or authority acting on the GraphCast Lee run.", "replication": { "produced_by": [ "GraphCast trained on decades of ERA5 reanalysis and run live from ECMWF initial conditions on ECMWF's experimental charts", "A long-lived Cabo Verde hurricane whose track was, per NHC, easier than normal to forecast (low climatology-persistence errors)", "Vendor retrospective comparison of its own run against conventional models" ], "must_hold": [ "Independent verification of the specific nine-day run against the official track record (not found in any ECMWF or NHC document among the sources)", "Access to ECMWF initial conditions for the live run", "A forecaster or authority actually consulting the AI run for it to become an attributable benefit" ], "echoes": [ { "setting": "2023 season verification by CIRA of GraphCast and peer AI models against operational cyclone track guidance (existing record AIBD-0090)", "outcome": "recurred", "source": "AIBD-0090 (DeMaria et al. 2024 preprint cited there)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0031", "case_url": "records/AIBD-0031.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "The forecasting system was built to warn people of riverine floods before water arrives so they can prepare or evacuate. The measured preparedness and health effects are that intended benefit, but only at cohort level." }, { "label": "AI contribution", "value": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0031", "title": "Randomized evaluation of Google AI flood alerts with community dissemination in Bihar, India (2022–2023 monsoons)", "summary": "Bihar RCT, 319 panchayats: households relayed Google flood alerts reported 16.6% fewer illness symptoms and about 30% lower medical costs.", "date_observed": "2022-06-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google's AI flood-forecasting system, run with India's Central Water Commission (CWC), issues riverine flood alerts as Android push notifications. In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. No specific evacuation tied to an alert is documented.", "category": "general benefit", "unexpected": false, "purpose": "The forecasting system was built to warn people of riverine floods before water arrives so they can prepare or evacuate. The measured preparedness and health effects are that intended benefit, but only at cohort level.", "system": "Google flood forecasting with Android push alerts (Google Public Alerts), plus community dissemination by Yuganter volunteers", "model_version": null, "developer": "Google", "deployer": "Central Water Commission of India with Google; last-mile dissemination by Yuganter (NGO) under the Jagnani-Pande evaluation", "location": "Bihar (twelve flood-prone districts), India", "domain": "Disaster risk reduction / flood early warning", "segment": "Rural households in riverine flood zones", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Effects are cohort averages across roughly 3.6 million people's communities (fewer illness symptoms, lower medical spending). No identifiable household with an attributable changed outcome is documented." }, { "group": "societal", "depth": null, "reason": "The study estimates population-level preparedness and health effects from a randomized design. It reports no discrete event such as a named evacuation." } ], "ai_role": { "status": "assessed", "statement": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." }, "evidence": [ { "kind": "preprint", "citation": "Jagnani M, Pande R (2024). Forecasting Fate: Experimental Evaluation of a Flood Early Warning System. Working paper, Tufts University and Yale University (Economic Growth Center), 3 April 2024.", "title": "Forecasting Fate: Experimental Evaluation of a Flood Early Warning System", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "date": "2024-04-03", "written_by": "independent", "quote": "treatment households exhibiting higher adaptation and health scores and a 30% reduction in medical costs compared to control communities" }, { "kind": "report", "citation": "Abdul Latif Jameel Poverty Action Lab (J-PAL) (n.d.). Disseminating Flood Warnings to Increase Disaster Preparedness in India. Evaluation summary (PDF).", "title": "Disseminating Flood Warnings to Increase Disaster Preparedness in India", "url": "https://www.povertyactionlab.org/print/pdf/node/8026750", "date": null, "written_by": "independent", "quote": "households in treatment communities received 2.37 more alerts (a 290 percent increase) on average during the 2022 flood season (June-October)" } ], "replication": { "produced_by": [ "Google/CWC flood alerts available to all 319 study panchayats (both arms); the randomized element was volunteer dissemination", "Trained community volunteers relaying smartphone alerts in villages with low literacy and limited location-services use", "Repeated flood seasons (2022 was more intense than 2023) allowing effects to be measured" ], "must_hold": [ "Alerts reach households before water arrives (46.5% more likely in treatment communities in 2022)", "Households trust alerts despite more false positives (8.09% vs 3.64%)", "Dissemination capacity is maintained. From 2023 two volunteers per panchayat replaced three, supplemented by direct WhatsApp/SMS messages to treatment households enrolled in the 2022 endline survey." ], "echoes": [ { "date": "2023-06-01", "setting": "2023 monsoon season in the same 319 Bihar panchayats: treatment households received 4.35 more alerts and were 88% more likely to receive an alert before water reached their panchayat", "model_version": null, "outcome": "recurred", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Same developer and channel: Google's Android hazard alerts; flood alerts cut illness in Bihar, while the Android Earthquake Alerts system underestimated the Kahramanmaraş quake and failed to warn millions of Android users in Turkey." }, "headline": "Google flood alerts in Bihar cut reported illness by 16.6%", "metric": "16.56% fewer illness symptoms and about 30% lower medical expenditure", "comparator": { "against": "control communities, where households had access to the smartphone alerts but no community volunteers relaying them", "before": "control households (reference group)", "after": "0.36 fewer illness symptoms (a 16.56% decrease); roughly INR 6,500 less on illness treatment (a 32.87% decrease)", "basis": "measured", "quote": "Overall, treatment households observed 0.36 fewer illness symptoms (a 16.56% decrease) compared to control households (Table 4). As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease).", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "later": { "status": "unknown", "date": null, "note": "Neither cited source reports a development after the 2022-2023 evaluation seasons.", "quote": null, "url": null }, "value": { "amount": "INR 6,500", "what": "less spent on illness treatment per household over the flood season", "who": "treatment households in the Bihar study panchayats", "estimate": false, "quote": "As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease). In fact, based on the estimated reduction in medical expenditures alone, we estimate that for every $1 spent on the program, the intervention would generate a minimum benefit of $30 during a severe flooding season.", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Removed “Flood Hub”, “Google Research” and the Kosi-river claim because neither cited source states them, and reworded the 2023 volunteer change and the 2019 pilot to match the working paper, which adds direct WhatsApp/SMS messaging in 2023 and describes the pilot's treatment as amplifying alert awareness to a single local leader.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf; https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Central_Water_Commission", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report and one independent preprint. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0031", "catalog_version": "1.0.0", "source_fingerprint": "1bfab83fdd45a0a55bd8861c4524bc8554fc9c682ab1f94bcf4c48375513c3f8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0031-source-0001", "AIBD-0031-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0031-legacy-value", "DIV-0010" ], "role": "Existing monetary observations and Fructus statements with matched AIBD case links; each statement retains its own review and eligibility status" }, "provenance": { "kind": "curatorial", "basis": "Linked to the current Fructus dataset by its validated case identifier. Eligible statements can supply unanswered monetary questions without a new source review or a finding of net benefit.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0031-legacy-value", "DIV-0010" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Central Water Commission of India with Google; last-mile dissemination by Yuganter (NGO) under the Jagnani-Pande evaluation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: one independent report and one independent preprint. 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It reports no discrete event such as a named evacuation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Effects are cohort averages across roughly 3.6 million people's communities (fewer illness symptoms, lower medical spending). 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Forecasting Fate: Experimental Evaluation of a Flood Early Warning System. Working paper, Tufts University and Yale University (Economic Growth Center), 3 April 2024.", "title": "Forecasting Fate: Experimental Evaluation of a Flood Early Warning System", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "date": "2024-04-03", "written_by": "independent", "quote": "treatment households exhibiting higher adaptation and health scores and a 30% reduction in medical costs compared to control communities" }, { "kind": "report", "citation": "Abdul Latif Jameel Poverty Action Lab (J-PAL) (n.d.). Disseminating Flood Warnings to Increase Disaster Preparedness in India. 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In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. 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From 2023 two volunteers per panchayat replaced three, supplemented by direct WhatsApp/SMS messages to treatment households enrolled in the 2022 endline survey." ], "echoes": [ { "date": "2023-06-01", "setting": "2023 monsoon season in the same 319 Bihar panchayats: treatment households received 4.35 more alerts and were 88% more likely to receive an alert before water reached their panchayat", "outcome": "recurred", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 38, "reviewed": 0, "record_mapped": 38, "not_assessed": 53, "complete": false, "by_state": { "value_present": 38, "not_reported": 0, "unresolved": 0, "not_assessed": 53, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0167", "case_url": "records/AIBD-0167.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "IdentiFlight is built to detect and classify flying eagles and curtail individual turbines to avoid collisions; reduced eagle fatalities are the intended outcome." }, { "label": "AI contribution", "value": "The system's classification decisions trigger curtailment, so any fatality reduction runs through the AI. The peer-reviewed estimate is independent of the maker but is challenged by a peer-reviewed reanalysis (50%, interval including zero). The California assessment shows 23% of eagles missed and six times more non-eagle than eagle curtailments. The size of the benefit is therefore unsettled." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0167", "title": "IdentiFlight automated curtailment at Top of the World cuts the eagle fatality rate by an estimated 50–82%, with wide uncertainty", "summary": "Peregrine Fund, WEST and USGS researchers estimated an 82% (75%–89%) relative cut in eagle deaths at a Wyoming wind farm after IdentiFlight curtailment; a 2023 reanalysis put it at 50% (−159%, 89%).", "date_observed": "2021-01-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "McClure et al. (Journal of Applied Ecology, Crossref publication date 31 January 2021) tested an automated curtailment system. The site was Top of the World Windpower Facility, Wyoming, USA. Authors are from The Peregrine Fund, Western EcoSystems Technology and the U.S. Geological Survey. The cameras detect and classify flying objects and decide whether to curtail individual turbines. The design was before–after–control–impact, with a control site 15 km away. Correcting for carcass detection probability, they estimated fatalities fell 63% at the treatment site (95% confidence interval, CI, 59%–66%). They rose 113% at the control site, an 82% (75%–89%) relative reduction. They noted fatalities were not eliminated. Recharge reported the claim on 28 January 2021. Huso and Dalthorp (Journal of Applied Ecology, 30 August 2023) reanalyzed the data and reported four major errors. Corrected, these give 50% (−159%, 89%), consistent with no effect. McClure et al. replied the same day that their data support the efficacy of automated curtailment. Duerr et al. (PLoS One, 26 January 2023) assessed an IdentiFlight installation in California. It correctly identified 77% of eagles and 85% of non-eagles, with 5,439 curtailment orders for non-eagle targets against 850 for eagles. The OECD AI Incidents Monitor logged the 82% result as a positive record.", "category": "general benefit", "unexpected": false, "purpose": "IdentiFlight is built to detect and classify flying eagles and curtail individual turbines to avoid collisions; reduced eagle fatalities are the intended outcome.", "system": "IdentiFlight automated eagle detection and turbine curtailment (camera system with machine-learning classification)", "model_version": null, "developer": "IdentiFlight (Boulder Imaging)", "deployer": "Top of the World Windpower Facility, Wyoming", "location": "Wyoming, United States", "domain": "Energy", "segment": "Wind power wildlife mitigation", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "Reduced eagle fatalities are an aggregate estimate whose size is disputed; no individually attributable outcome is documented." }, { "group": "organizational", "depth": null, "reason": "The operator's benefit (fewer protected-species fatalities) is not quantified independently of the disputed estimate." } ], "ai_role": { "status": "assessed", "statement": "The system's classification decisions trigger curtailment, so any fatality reduction runs through the AI. The peer-reviewed estimate is independent of the maker but is challenged by a peer-reviewed reanalysis (50%, interval including zero). The California assessment shows 23% of eagles missed and six times more non-eagle than eagle curtailments. The size of the benefit is therefore unsettled." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "McClure, C. J. W., Rolek, B. W., Dunn, L., McCabe, J. D., Martinson, L., Katzner, T., 'Eagle fatalities are reduced by automated curtailment of wind turbines', Journal of Applied Ecology (2021), doi:10.1111/1365-2664.13831", "title": "Eagle fatalities are reduced by automated curtailment of wind turbines", "url": "https://doi.org/10.1111/1365-2664.13831", "date": "2021-01-31", "written_by": "independent", "quote": "In total, there was an 82% (75%–89%) reduction in the fatality rate at the treatment site relative to the control site." }, { "kind": "peer-reviewed study", "citation": "Huso, M., Dalthorp, D., 'Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines', Journal of Applied Ecology (2023), doi:10.1111/1365-2664.14196", "title": "Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines", "url": "https://doi.org/10.1111/1365-2664.14196", "date": "2023-08-30", "written_by": "independent", "quote": "We show that reported results stem from four major errors, which, when corrected, give an unremarkable estimate of 50% (−159%, 89%) reduction (or possible increase) in the fatality rate." }, { "kind": "peer-reviewed study", "citation": "McClure, C. J. W. et al., 'Reanalysis ignores pertinent data, includes inappropriate observations, and disregards realities of applied ecology: Response to Huso and Dalthorp (2023)', Journal of Applied Ecology (2023), doi:10.1111/1365-2664.14490", "title": "Reanalysis ignores pertinent data, includes inappropriate observations, and disregards realities of applied ecology: Response to Huso and Dalthorp (2023)", "url": "https://doi.org/10.1111/1365-2664.14490", "date": "2023-08-30", "written_by": "independent", "quote": "As we show here, our data do indeed provide support for the efficacy of automated curtailment." }, { "kind": "peer-reviewed study", "citation": "Duerr, A. E., Parsons, A. E., Nagy, L. R., Kuehn, M. J., Bloom, P. H., 'Effectiveness of an artificial intelligence-based system to curtail wind turbines to reduce eagle collisions', PLoS One (2023), doi:10.1371/journal.pone.0278754", "title": "Effectiveness of an artificial intelligence-based system to curtail wind turbines to reduce eagle collisions", "url": "https://doi.org/10.1371/journal.pone.0278754", "date": "2023-01-26", "written_by": "independent", "quote": "The system correctly identified 77% of eagles and 85% of non-eagles." }, { "kind": "news report", "citation": "Andrew Lee, 'Wind farm eagle deaths cut by 82% as smart cameras spot birds then halt turbines', Recharge, 28 January 2021", "title": "Wind farm eagle deaths 'cut by 82%' as smart cameras spot birds then halt turbines", "url": "https://www.rechargenews.com/wind/wind-farm-eagle-deaths-cut-by-82-as-smart-cameras-spot-birds-then-halt-turbines/2-1-953479", "date": "2021-01-28", "written_by": "independent", "quote": "Optical and machine-learning system that prompts 'informed curtailment' reduces fatalities in Wyoming test, it is claimed" }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, record 2021-01-28-085c", "title": "AI System IdentiFlight Reduces Eagle Deaths at Wind Farms by 82%", "url": "https://oecd.ai/en/incidents/2021-01-28-085c", "date": "2021-01-28", "written_by": "independent", "quote": "has reduced eagle fatalities by 82% at wind farms, according to multiple studies." }, { "kind": "peer-reviewed study", "citation": "McClure, C. J. W., Rolek, B. W., Dunn, L., McCabe, J. D., Martinson, L., Katzner, T. E. (2022). Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines. Ecological Solutions and Evidence, published online 26 Aug 2022. doi:10.1002/2688-8319.12173", "title": "Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines", "url": "https://doi.org/10.1002/2688-8319.12173", "date": "2022-08-26", "written_by": "independent", "quote": "We demonstrate that eagle fatalities were reduced by 85% (95% highest density interval = 12%, 100%) after implementation of automated curtailment." }, { "kind": "peer-reviewed study", "citation": "McClure, C. J. W., Rolek, B. W., Dunn, L., McCabe, J. D., Martinson, L., Katzner, T. (2021). Eagle fatalities are reduced by automated curtailment of wind turbines. Journal of Applied Ecology 58(3): 446-452, as recorded in the USGS Publications Warehouse.", "title": "Eagle fatalities are reduced by automated curtailment of wind turbines", "url": "https://pubs.usgs.gov/publication/70219527", "date": "2021-01-28", "written_by": "independent", "quote": null }, { "kind": "peer-reviewed study", "citation": "Huso, M., Dalthorp, D. (2023). Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines. Journal of Applied Ecology, 30 August 2023, as recorded in the USGS Publications Warehouse.", "title": "Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines", "url": "https://pubs.usgs.gov/publication/70248352", "date": "2023-08-30", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Camera towers covering turbine airspace with per-turbine curtailment control", "Carcass searches corrected for detection probability at treatment and control sites", "A single post-treatment year at one site, which the reanalysis says limits causal inference" ], "must_hold": [ "Eagle abundance and flight behavior comparable to the Wyoming site", "Operators willing to accept curtailment losses, including misidentified non-eagle targets", "Longer, replicated monitoring to resolve the disputed effect size" ], "echoes": [ { "date": "2023-01-26", "setting": "IdentiFlight installation at a California wind project; identification accuracy and virtual curtailments measured, no fatality outcome", "model_version": null, "outcome": "unknown", "source": "https://doi.org/10.1371/journal.pone.0278754" }, { "date": "2022-09-20", "setting": "Germany, IdentiFlight cameras deployed for lesser spotted eagles", "model_version": null, "outcome": "unknown", "source": "https://oecd.ai/en/incidents/2022-09-20-caeb" }, { "date": "2022-08-26", "setting": "Same site, same authors, additional data and revised analysis", "model_version": null, "outcome": "recurred", "source": "https://doi.org/10.1002/2688-8319.12173" } ] }, "paired_incident": null, "headline": "IdentiFlight cuts eagle deaths by an estimated 50–82%", "metric": "82% (75%–89%) relative cut in eagle deaths at a Wyoming wind farm", "comparator": { "against": "a control wind facility 15 km away without automated curtailment, before and after the installation", "before": "increasing at the control site by 113% (51%–218%)", "after": "the number of fatalities at the treatment site declined by 63% (95% CI = 59%–66%)", "basis": "measured", "quote": "After correcting for carcass detection probability and scaling fatality estimates to turbine‐years, we estimate that the number of fatalities at the treatment site declined by 63% (95% CI = 59%–66%) between before and after periods while increasing at the control site by 113% (51%–218%). In total, there was an 82% (75%–89%) reduction in the fatality rate at the treatment site relative to the control site.", "url": "https://pubs.usgs.gov/publication/70219527" }, "later": { "status": "reversed", "date": "2023-08-30", "note": "A USGS reanalysis by Huso and Dalthorp found the 82% claim unsupported, estimating a 50% reduction with an interval spanning an increase; the original authors published a rebuttal the same day.", "quote": "We show that reported results stem from four major errors, which, when corrected, give an unremarkable estimate of 50% (−159%, 89%) reduction (or possible increase) in the fatality rate.", "url": "https://pubs.usgs.gov/publication/70248352" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged a second write-up of the same study: added the authors' 2022 confirmation paper as a source and a replication.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://pubs.usgs.gov/publication/70219527; https://pubs.usgs.gov/publication/70248352", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.identiflight.com/contact; https://en.wikipedia.org/wiki/Top_of_the_World_Windpower_Project", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: seven independent peer-reviewed studies, one independent news report and a registry entry. 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(Journal of Applied Ecology, Crossref publication date 31 January 2021) tested an automated curtailment system. The site was Top of the World Windpower Facility, Wyoming, USA. Authors are from The Peregrine Fund, Western EcoSystems Technology and the U.S. Geological Survey. The cameras detect and classify flying objects and decide whether to curtail individual turbines. The design was before–after–control–impact, with a control site 15 km away. Correcting for carcass detection probability, they estimated fatalities fell 63% at the treatment site (95% confidence interval, CI, 59%–66%). They rose 113% at the control site, an 82% (75%–89%) relative reduction. They noted fatalities were not eliminated. Recharge reported the claim on 28 January 2021. Huso and Dalthorp (Journal of Applied Ecology, 30 August 2023) reanalyzed the data and reported four major errors. Corrected, these give 50% (−159%, 89%), consistent with no effect. McClure et al. replied the same day that their data support the efficacy of automated curtailment. Duerr et al. (PLoS One, 26 January 2023) assessed an IdentiFlight installation in California. It correctly identified 77% of eagles and 85% of non-eagles, with 5,439 curtailment orders for non-eagle targets against 850 for eagles. 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(Journal of Applied Ecology, Crossref publication date 31 January 2021) tested an automated curtailment system. The site was Top of the World Windpower Facility, Wyoming, USA. Authors are from The Peregrine Fund, Western EcoSystems Technology and the U.S. Geological Survey. The cameras detect and classify flying objects and decide whether to curtail individual turbines. The design was before–after–control–impact, with a control site 15 km away. Correcting for carcass detection probability, they estimated fatalities fell 63% at the treatment site (95% confidence interval, CI, 59%–66%). They rose 113% at the control site, an 82% (75%–89%) relative reduction. They noted fatalities were not eliminated. Recharge reported the claim on 28 January 2021. Huso and Dalthorp (Journal of Applied Ecology, 30 August 2023) reanalyzed the data and reported four major errors. Corrected, these give 50% (−159%, 89%), consistent with no effect. 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The OECD AI Incidents Monitor logged the 82% result as a positive record." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "IdentiFlight automated eagle detection and turbine curtailment (camera system with machine-learning classification)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "IdentiFlight (Boulder Imaging)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "IdentiFlight automated curtailment at Top of the World cuts the eagle fatality rate by an estimated 50–82%, with wide uncertainty", "headline": "IdentiFlight cuts eagle deaths by an estimated 50–82%" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "8121fb847884cafd69af797b7755308b878861b8632b148ca226f7cbd53f91a9" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "McClure et al. (Journal of Applied Ecology, Crossref publication date 31 January 2021) tested an automated curtailment system. The site was Top of the World Windpower Facility, Wyoming, USA. Authors are from The Peregrine Fund, Western EcoSystems Technology and the U.S. Geological Survey. The cameras detect and classify flying objects and decide whether to curtail individual turbines. The design was before–after–control–impact, with a control site 15 km away. Correcting for carcass detection probability, they estimated fatalities fell 63% at the treatment site (95% confidence interval, CI, 59%–66%). They rose 113% at the control site, an 82% (75%–89%) relative reduction. They noted fatalities were not eliminated. Recharge reported the claim on 28 January 2021. Huso and Dalthorp (Journal of Applied Ecology, 30 August 2023) reanalyzed the data and reported four major errors. Corrected, these give 50% (−159%, 89%), consistent with no effect. McClure et al. replied the same day that their data support the efficacy of automated curtailment. Duerr et al. (PLoS One, 26 January 2023) assessed an IdentiFlight installation in California. It correctly identified 77% of eagles and 85% of non-eagles, with 5,439 curtailment orders for non-eagle targets against 850 for eagles. The OECD AI Incidents Monitor logged the 82% result as a positive record.", "replication": { "produced_by": [ "Camera towers covering turbine airspace with per-turbine curtailment control", "Carcass searches corrected for detection probability at treatment and control sites", "A single post-treatment year at one site, which the reanalysis says limits causal inference" ], "must_hold": [ "Eagle abundance and flight behavior comparable to the Wyoming site", "Operators willing to accept curtailment losses, including misidentified non-eagle targets", "Longer, replicated monitoring to resolve the disputed effect size" ], "echoes": [ { "date": "2023-01-26", "setting": "IdentiFlight installation at a California wind project; identification accuracy and virtual curtailments measured, no fatality outcome", "outcome": "unknown", "source": "https://doi.org/10.1371/journal.pone.0278754" }, { "date": "2022-09-20", "setting": "Germany, IdentiFlight cameras deployed for lesser spotted eagles", "outcome": "unknown", "source": "https://oecd.ai/en/incidents/2022-09-20-caeb" }, { "date": "2022-08-26", "setting": "Same site, same authors, additional data and revised analysis", "outcome": "recurred", "source": "https://doi.org/10.1002/2688-8319.12173" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0328", "case_url": "records/AIBD-0328.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Per the Post's sources, it is a tool built to find a human heartbeat at distance. The uses are intelligence collection and personnel recovery, and it was tested on Black Hawk helicopters. Its stated purpose is exactly the use claimed." }, { "label": "AI contribution", "value": "The Post's anonymous sources describe AI that isolates a heartbeat signal from sensor noise. No public technical evidence establishes that this system located the airman. The official account confirms the rescue and camera observation, while the later presidential remark does not establish the sensing method." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Iran" ] }, "record": { "id": "AIBD-0328", "title": "A CIA tool called “Ghost Murmur”, pairing quantum magnetometry with AI, may have located downed F-15E officer “Dude 44 Bravo” in Iran before his 4 April 2026 rescue", "summary": "The rescue is documented. The claim that Ghost Murmur located the airman rests on two anonymous sources and a subsequent presidential remark; its technical capability remains unverified.", "date_observed": "2026-04-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The April 6 White House briefing documents the rescue of the downed F-15E weapons systems officer in Iran. The official transcript also describes watching his suspected position with a camera from 40 miles away. That distance concerns the camera, not an established heartbeat-detection range. On April 7, the New York Post reported that two anonymous sources attributed the officer's location to a CIA tool called Ghost Murmur. They described quantum magnetic sensing combined with AI that separates a heartbeat signal from background noise, and attributed its development to Lockheed Martin's Skunk Works. Lockheed Martin declined to comment. In an April 8 telephone interview with the Post, President Trump called the tool important to the rescue, but supplied no technical explanation. The Post and Scientific American separately interviewed physicists who questioned the described capability. The official briefing does not name Ghost Murmur or establish an AI contribution. No public measurements or inspectable technical account establish that the claimed system located the officer.", "category": "harm prevented", "unexpected": false, "purpose": "Per the Post's sources, it is a tool built to find a human heartbeat at distance. The uses are intelligence collection and personnel recovery, and it was tested on Black Hawk helicopters. Its stated purpose is exactly the use claimed.", "system": "'Ghost Murmur': long-range quantum (nitrogen-vacancy diamond) magnetometry paired with AI software that isolates heartbeat signatures from background noise, as described by the NY Post's anonymous sources", "model_version": null, "developer": "Lockheed Martin Skunk Works, per two NY Post sources; the company declined to comment", "deployer": "US Central Intelligence Agency, per the NY Post; the CIA and White House named no tool", "location": "Mountains of southern Iran", "domain": "Defense and intelligence: combat search and rescue", "segment": "Remote personnel-location sensing", "benefit_type": "Improved Physical Health", "taxonomy_note": "A combat rescue is read as a health outcome because no rescue or defense type exists.", "registry_ids": { "oecd_aim": "2026-04-08-d905" }, "ratings": [ { "group": "personal", "depth": 5, "reason": "The weapons systems officer was found alive on 4 April 2026 and brought out with no US casualties, per Ratcliffe and Trump. Whether Ghost Murmur produced the find rests on the Post's anonymous sources and Trump's “very important”. Physicists dispute the described mechanism, and a beacon and a long-range camera were also in play." }, { "group": "organizational", "depth": null, "reason": "No source quantifies the tool's contribution to the search beyond Trump's remark. The mission also used human assets, deception, a camera at 40 miles and the airman's beacon." } ], "ai_role": { "status": "unassessed", "statement": "The Post's anonymous sources describe AI that isolates a heartbeat signal from sensor noise. No public technical evidence establishes that this system located the airman. The official account confirms the rescue and camera observation, while the later presidential remark does not establish the sensing method." }, "evidence": [ { "kind": "news report", "citation": "Steven Nelson, New York Post. 'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission. 2026-04-07. Original reporting based on two anonymous sources.", "title": "'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission", "url": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "date": "2026-04-07", "written_by": "independent", "quote": null }, { "kind": "report", "citation": "White House; Daily Compilation of Presidential Documents. Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters. 2026-04-06. DCPD-202600233, pp. 1–2 and 7. Official account of the rescue and long-range camera observation.", "title": "Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters", "url": "https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "date": "2026-04-06", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "Steven Nelson, New York Post. Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works. 2026-04-08. Adds a direct telephone interview with President Trump and interviews with physicists.", "title": "Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works", "url": "https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "date": "2026-04-08", "written_by": "independent", "quote": null }, { "kind": "news report", "citation": "Deni Ellis Béchard, Scientific American. Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical. 2026-04-08. Adds original expert interviews challenging the publicly described sensing mechanism.", "title": "Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical", "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/", "date": "2026-04-08", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A rescue operation that involved personnel, aircraft, intelligence, and camera observation", "An alleged AI-assisted sensing contribution whose specifications and performance are not public" ], "must_hold": [ "Public evidence of the sensing method and its role in locating the officer would be needed before identifying reproducible conditions for an AI benefit" ], "echoes": [] }, "paired_incident": null, "headline": "*Ghost Murmur* may have found downed F-15E officer", "metric": "one rescued airman; the claimed AI contribution remains unverified", "comparator": null, "later": { "status": "challenged", "date": "2026-04-08", "note": "Physicists interviewed by the Post and Scientific American disputed the public technical description. Their criticism does not establish that the rescue claim was formally reversed or withdrawn.", "quote": null, "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": [ "White House, April 6, 2026, DCPD-202600233, pp. 1–2 and 7. https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "Steven Nelson, New York Post, April 7 and 8, 2026. https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/ https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "Deni Ellis Béchard, Scientific American, April 8, 2026, original expert interviews. https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/" ], "brief": "The April 6 White House briefing documents the rescue of a downed American airman in Iran.[1] The claim that an AI-assisted quantum sensor located him comes from two anonymous sources in the New York Post. President Trump subsequently told the Post that the tool was important, without explaining how it worked.[2] Physicists interviewed by the Post and Scientific American questioned the described capability.[3] The rescue is established; the contribution attributed to Ghost Murmur remains unverified.", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Brief from Peter added.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Brief from Peter rewritten to say why the case matters now.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Sidenotes added to the brief.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.military.com/feature/2026/04/18/ghost-murmur-heartbeat-tracking-tech-has-experts-questioning-laws-of-physics.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Skunk_Works; https://www.cia.gov/legacy/headquarters/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced the incident-monitor summary with original reporting, separated camera distance from heartbeat claims, and corrected the unsupported reversal label.", "source": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and the deployer's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0328", "catalog_version": "1.0.0", "source_fingerprint": "4678006907444b0cd74c0e1b28a900499156578134b32a7632876f04ae852846", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Lockheed Martin Skunk Works, per two NY Post sources; the company declined to comment", "deployer": "US Central Intelligence Agency, per the NY Post; the CIA and White House named no tool" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "The Post's anonymous sources describe AI that isolates a heartbeat signal from sensor noise. No public technical evidence establishes that this system located the airman. The official account confirms the rescue and camera observation, while the later presidential remark does not establish the sensing method." } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "The weapons systems officer was found alive on 4 April 2026 and brought out with no US casualties, per Ratcliffe and Trump. Whether Ghost Murmur produced the find rests on the Post's anonymous sources and Trump's “very important”. Physicists dispute the described mechanism, and a beacon and a long-range camera were also in play." }, { "group": "organizational", "reason": "No source quantifies the tool's contribution to the search beyond Trump's remark. The mission also used human assets, deception, a camera at 40 miles and the airman's beacon." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports and the deployer's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "The weapons systems officer was found alive on 4 April 2026 and brought out with no US casualties, per Ratcliffe and Trump. Whether Ghost Murmur produced the find rests on the Post's anonymous sources and Trump's “very important”. Physicists dispute the described mechanism, and a beacon and a long-range camera were also in play." }, { "group": "organizational", "reason": "No source quantifies the tool's contribution to the search beyond Trump's remark. 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Physicists dispute the described mechanism, and a beacon and a long-range camera were also in play." }, { "group": "organizational", "reason": "No source quantifies the tool's contribution to the search beyond Trump's remark. The mission also used human assets, deception, a camera at 40 miles and the airman's beacon." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "value_present", "value": { "kind": "text", "text": "The April 6 White House briefing documents the rescue of a downed American airman in Iran.[1] The claim that an AI-assisted quantum sensor located him comes from two anonymous sources in the New York Post. 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'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission. 2026-04-07. Original reporting based on two anonymous sources.", "title": "'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission", "url": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "date": "2026-04-07", "written_by": "independent" }, { "kind": "report", "citation": "White House; Daily Compilation of Presidential Documents. Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters. 2026-04-06. DCPD-202600233, pp. 1–2 and 7. 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Scientists are skeptical", "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/", "date": "2026-04-08", "written_by": "independent" } ] } }, "provenance": { "kind": "source_review", "basis": "Replaced the incident-monitor summary with original reporting, separated camera distance from heartbeat claims, and corrected the unsupported reversal label.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0328-source-0001" ], "url": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "title": "'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission", "published_on": "2026-04-07", "relationship": "independent", "citation": "Steven Nelson, New York Post. 'Ghost Murmur,' a never-used secret tool, deployed to find lost airman in Iran in daring mission. 2026-04-07. Original reporting based on two anonymous sources." }, { "source_refs": [ "AIBD-0328-source-0002" ], "url": "https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "title": "Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters", "published_on": "2026-04-06", "relationship": "deployer", "citation": "White House; Daily Compilation of Presidential Documents. Remarks on the Rescue of United States Military Personnel in Iran and an Exchange With Reporters. 2026-04-06. DCPD-202600233, pp. 1–2 and 7. Official account of the rescue and long-range camera observation." }, { "source_refs": [ "AIBD-0328-source-0003" ], "url": "https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "title": "Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works", "published_on": "2026-04-08", "relationship": "independent", "citation": "Steven Nelson, New York Post. Trump confirms CIA 'Ghost Murmur' tool was ‘very important’ to find airman in Iran — as experts debate how it works. 2026-04-08. Adds a direct telephone interview with President Trump and interviews with physicists." }, { "source_refs": [ "AIBD-0328-source-0004" ], "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/", "title": "Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical", "published_on": "2026-04-08", "relationship": "independent", "citation": "Deni Ellis Béchard, Scientific American. Is the ‘Ghost Murmur’ quantum device possible? Scientists are skeptical. 2026-04-08. Adds original expert interviews challenging the publicly described sensing mechanism." } ], "excluded_republications": [ { "url": "https://oecd.ai/en/incidents/2026-04-08-d905", "title": "CIA Uses AI System 'Ghost Murmur' to Rescue Downed Pilot in Iran", "published_on": "2026-04-08", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0001" }, { "url": "https://www.airandspaceforces.com/dude-44-rescue-massive-operation-iran-save-downed-airmen/", "title": "Inside the Massive Operation to Save Two Downed Airmen In Iran", "published_on": "2026-04-06", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0004" }, { "url": "https://www.military.com/feature/2026/04/18/ghost-murmur-heartbeat-tracking-tech-has-experts-questioning-laws-of-physics.html", "title": "Ghost Murmur: The Heartbeat-Tracking Tech That Has Experts Questioning the Laws of Physics", "published_on": "2026-04-18", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0006" }, { "url": "https://www.ynetnews.com/tech-and-digital/article/bywkzwhh11e", "title": "CIA's experimental ‘Ghost Murmur’ device reportedly helped locate downed pilot in Iran", "published_on": "2026-04-09", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0007" }, { "url": "https://thequantuminsider.com/2026/04/08/did-a-quantum-sensor-help-rescuers-find-a-downed-american-pilot/", "title": "Did a Quantum Sensor Help Rescuers Find a Downed American Pilot?", "published_on": "2026-04-08", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0008" }, { "url": "https://www.diariopanorama.com/noticia/548513/ghost-murmur-ciencia-o-ficcion-inedita-tecnologia-habria-usado-cia-para-rescate-iran", "title": "Ghost Murmur: ¿Ciencia o ficción?, la inédita tecnología que habría usado la CIA para el rescate en Irán", "published_on": "2026-04-10", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0328-source-0009" } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0328-source-0001", "retained_source_order": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "ordering_basis": "The source that directly supports the central case leads. Additional sources contribute the specific evidence or limitations described in claim_lineage.", "claim_lineage": [ { "claim": "Alleged Ghost Murmur mechanism and role", "source_chain": [ { "url": "https://nypost.com/2026/04/07/us-news/ghost-murmur-a-never-used-secret-tool-deployed-to-find-lost-airman-in-iran-in-daring-mission/", "locator": "Body: two sources close to the breakthrough; Lockheed Martin response", "role": "Original anonymous-source reporting", "access_status": "Full relevant text reviewed", "published_on": "2026-04-07" } ], "limitations": [ "Anonymous sources; no technical measurements or inspectable device evidence" ], "source_ref": "AIBD-0328-source-0001" }, { "claim": "Rescue and camera observation", "source_chain": [ { "url": "https://www.govinfo.gov/content/pkg/DCPD-202600233/pdf/DCPD-202600233.pdf", "locator": "pp. 1–2 and 7", "role": "Original official briefing transcript", "access_status": "Full relevant text reviewed", "published_on": "2026-04-06" } ], "limitations": [ "The transcript does not name Ghost Murmur; the 40-mile statement concerns a camera", "Ratcliffe’s remarks are abridged in this official transcript; the full April 6 briefing transcript is archived at https://rollcall.com/factbase/trump/transcript/donald-trump-press-conference-briefing-room-april-6-2026/" ], "source_ref": "AIBD-0328-source-0002" }, { "claim": "Presidential remark and independent technical criticism", "source_chain": [ { "url": "https://nypost.com/2026/04/08/us-news/trump-confirms-cia-ghost-murmur-tool-was-very-important-to-find-airman-in-iran-as-experts-debate-how-it-works/", "locator": "Trump telephone interview and named physicist interviews", "role": "Original interviews", "access_status": "Full relevant text reviewed", "published_on": "2026-04-08" }, { "url": "https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/", "locator": "Interviews with John Wikswo, Chad Orzel, and Bradley Roth", "role": "Independent scrutiny of the public description", "access_status": "Indexed article text reviewed; direct publisher access was restricted" } ], "limitations": [ "Criticism of the public account is not an independent test of the actual classified device" ], "source_ref": "AIBD-0328-source-0003" } ], "limitations": [ "No independent evaluation of the alleged device", "Military.com full text could not be re-opened; it is not used for retained claims" ] } }, "provenance": { "kind": "source_review", "basis": "Replaced the incident-monitor summary with original reporting, separated camera distance from heartbeat claims, and corrected the unsupported reversal label.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The rescue is documented. The alleged device has no independent evaluation, and physicists dispute its described mechanism.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The rescue is documented. The claim that Ghost Murmur located the airman rests on two anonymous sources and a subsequent presidential remark; its technical capability remains unverified.", "account": "The April 6 White House briefing documents the rescue of the downed F-15E weapons systems officer in Iran. The official transcript also describes watching his suspected position with a camera from 40 miles away. That distance concerns the camera, not an established heartbeat-detection range. On April 7, the New York Post reported that two anonymous sources attributed the officer's location to a CIA tool called Ghost Murmur. They described quantum magnetic sensing combined with AI that separates a heartbeat signal from background noise, and attributed its development to Lockheed Martin's Skunk Works. Lockheed Martin declined to comment. In an April 8 telephone interview with the Post, President Trump called the tool important to the rescue, but supplied no technical explanation. The Post and Scientific American separately interviewed physicists who questioned the described capability. The official briefing does not name Ghost Murmur or establish an AI contribution. No public measurements or inspectable technical account establish that the claimed system located the officer." } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "'Ghost Murmur': long-range quantum (nitrogen-vacancy diamond) magnetometry paired with AI software that isolates heartbeat signatures from background noise, as described by the NY Post's anonymous sources" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Lockheed Martin Skunk Works, per two NY Post sources; the company declined to comment" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "A CIA tool called “Ghost Murmur”, pairing quantum magnetometry with AI, may have located downed F-15E officer “Dude 44 Bravo” in Iran before his 4 April 2026 rescue", "headline": "*Ghost Murmur* may have found downed F-15E officer" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "4678006907444b0cd74c0e1b28a900499156578134b32a7632876f04ae852846" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version after the October 24, 2026 primary-source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The April 6 White House briefing documents the rescue of the downed F-15E weapons systems officer in Iran. The official transcript also describes watching his suspected position with a camera from 40 miles away. That distance concerns the camera, not an established heartbeat-detection range. On April 7, the New York Post reported that two anonymous sources attributed the officer's location to a CIA tool called Ghost Murmur. They described quantum magnetic sensing combined with AI that separates a heartbeat signal from background noise, and attributed its development to Lockheed Martin's Skunk Works. Lockheed Martin declined to comment. In an April 8 telephone interview with the Post, President Trump called the tool important to the rescue, but supplied no technical explanation. The Post and Scientific American separately interviewed physicists who questioned the described capability. The official briefing does not name Ghost Murmur or establish an AI contribution. No public measurements or inspectable technical account establish that the claimed system located the officer.", "replication": { "produced_by": [ "A rescue operation that involved personnel, aircraft, intelligence, and camera observation", "An alleged AI-assisted sensing contribution whose specifications and performance are not public" ], "must_hold": [ "Public evidence of the sensing method and its role in locating the officer would be needed before identifying reproducible conditions for an AI benefit" ], "echoes": [] } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0328-source-0001", "AIBD-0328-source-0002", "AIBD-0328-source-0003", "AIBD-0328-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 4, "record_mapped": 26, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0010", "case_url": "records/AIBD-0010.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Built to flag posts and live videos indicating suicide or self-harm risk for review and, in imminent cases, referral to emergency services. This benefit is that purpose." }, { "label": "AI contribution", "value": "Police sources say Meta sent the alert after the post. They do not state whether Meta's classifier or a user report flagged it. Meta's documentation and NPR describe the process as machine-learning detection with human review before contacting authorities. The record relies on that description and should be read with that caveat." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0010", "title": "Meta self-harm alert routed to Uttar Pradesh Police leads to hospital treatment of a 25-year-old in Meerut within eight minutes", "summary": "7 June 2026: Meta alert at 6:53 pm; Meerut police reached a 25-year-old in 8 minutes, found him critical; hospital treatment, improved", "date_observed": "2026-06-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 7 June 2026 a 25-year-old man from the Sardhana area of Meerut, Uttar Pradesh, posted an Instagram video. In it he appeared to drink a white liquid. Police said Meta sent a suicide alert about the post to the Social Media Center at Uttar Pradesh Police headquarters at 6:53 pm. The center alerted Meerut Police, and a Sardhana team reached his home within eight minutes. They found him in a critical condition with a bottle of suspected liquid nearby and took him to hospital, where his condition improved after treatment. He told police he was under stress following the end of a relationship. Police data cited by PTI credit such alerts with 3,011 lives saved statewide from 1 January 2023 to 31 May 2026.", "category": "harm prevented", "unexpected": false, "purpose": "Built to flag posts and live videos indicating suicide or self-harm risk for review and, in imminent cases, referral to emergency services. This benefit is that purpose.", "system": "Meta proactive self-harm detection on Instagram with alert routing to the Uttar Pradesh Police Social Media Center", "model_version": null, "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (agreement signed March 2022)", "location": "Meerut, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2026-06-08-7d81" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "Police found him in a critical condition after apparent ingestion, and he was treated in hospital within minutes of the alert. Police describe the outcome as a life saved." } ], "ai_role": { "status": "assessed", "statement": "Police sources say Meta sent the alert after the post. They do not state whether Meta's classifier or a user report flagged it. Meta's documentation and NPR describe the process as machine-learning detection with human review before contacting authorities. The record relies on that description and should be read with that caveat." }, "evidence": [ { "kind": "news report", "citation": "PTI (2026). When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post. Deccan Herald.", "title": "When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post", "url": "https://www.deccanherald.com/india/uttar-pradesh/when-every-minute-counts-uttar-pradesh-police-save-youth-attempting-suicide-in-8-minutes-after-meta-alert-over-instagram-post-4032393", "date": "2026-06-09", "written_by": "independent", "quote": "flags posts indicating possible self-harm and shares alerts with authorities" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": "Police said Meta sent a suicide alert regarding the post to the Social Media Centre at the police headquarters at 6.53 pm." }, { "kind": "news report", "citation": "Kaste, M. (2018). Facebook Increasingly Reliant On A.I. To Predict Suicide Risk. NPR (transcript republished by North Country Public Radio).", "title": "Facebook Increasingly Reliant On A.I. To Predict Suicide Risk", "url": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk", "date": "2018-11-17", "written_by": "independent", "quote": "We also are able to use AI to coordinate a bunch of information on location to try to identify the location of that individual" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it's determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "news report", "citation": "PTI (2023). Facebook helps UP Police avert two suicides in a week in state. The Tribune.", "title": "Facebook helps UP Police avert two suicides in a week in state", "url": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp", "date": "2023-02-05", "written_by": "independent", "quote": "Meta Hqs USA immediately sends an alert to the social media centre of PHQ UP Police through phone and email" }, { "kind": "news report", "citation": "PTI (2021). Man saved by Delhi Police after Facebook alerts on self-harm video. The Tribune.", "title": "Man saved by Delhi Police after Facebook alerts on self-harm video", "url": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp", "date": "2021-06-05", "written_by": "independent", "quote": "received a call alert from Facebook's US office about a suspicious self-harm live video being posted by a male Facebook user located in Delhi" } ], "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "date": null, "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "date": null, "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "model_version": null, "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": null, "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "model_version": null, "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "382", "title": "Instagram's Exposure of Harmful Content Allegedly Contributed to Teenage Girl's Suicide", "url": "https://incidentdatabase.ai/cite/382", "severity": null, "group": "personal", "basis": "Same developer and platform, Meta's Instagram, and the same at-risk user group: Instagram's recommendation of suicide and self-harm content was ruled to have contributed to Molly Russell's death, where the Meerut alert reached a user in time." }, "headline": "Meta self-harm alert gets Meerut police to a man in 8 minutes", "metric": "3,011 lives saved statewide from 1 January 2023 to 31 May 2026", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development dated after the 7 June 2026 case was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 382 (high confidence); 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This benefit is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-07", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 382 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/382", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Linked the OECD AI Incidents entry, which describes this event.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 382 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/382", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Linked the OECD AI Incidents entry, which describes this event.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2026). When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post. Deccan Herald.", "title": "When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post", "url": "https://www.deccanherald.com/india/uttar-pradesh/when-every-minute-counts-uttar-pradesh-police-save-youth-attempting-suicide-in-8-minutes-after-meta-alert-over-instagram-post-4032393", "date": "2026-06-09", "written_by": "independent", "quote": "flags posts indicating possible self-harm and shares alerts with authorities" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": "Police said Meta sent a suicide alert regarding the post to the Social Media Centre at the police headquarters at 6.53 pm." }, { "kind": "news report", "citation": "Kaste, M. (2018). Facebook Increasingly Reliant On A.I. To Predict Suicide Risk. NPR (transcript republished by North Country Public Radio).", "title": "Facebook Increasingly Reliant On A.I. To Predict Suicide Risk", "url": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk", "date": "2018-11-17", "written_by": "independent", "quote": "We also are able to use AI to coordinate a bunch of information on location to try to identify the location of that individual" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it's determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "news report", "citation": "PTI (2023). Facebook helps UP Police avert two suicides in a week in state. The Tribune.", "title": "Facebook helps UP Police avert two suicides in a week in state", "url": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp", "date": "2023-02-05", "written_by": "independent", "quote": "Meta Hqs USA immediately sends an alert to the social media centre of PHQ UP Police through phone and email" }, { "kind": "news report", "citation": "PTI (2021). Man saved by Delhi Police after Facebook alerts on self-harm video. The Tribune.", "title": "Man saved by Delhi Police after Facebook alerts on self-harm video", "url": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp", "date": "2021-06-05", "written_by": "independent", "quote": "received a call alert from Facebook's US office about a suspicious self-harm live video being posted by a male Facebook user located in Delhi" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0010-source-0001", "AIBD-0010-source-0002", "AIBD-0010-source-0003", "AIBD-0010-source-0004", "AIBD-0010-source-0005", "AIBD-0010-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2026). When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post. Deccan Herald.", "title": "When every minute counts: Uttar Pradesh Police save youth attempting suicide in 8 minutes after Meta alert over Instagram post", "url": "https://www.deccanherald.com/india/uttar-pradesh/when-every-minute-counts-uttar-pradesh-police-save-youth-attempting-suicide-in-8-minutes-after-meta-alert-over-instagram-post-4032393", "date": "2026-06-09", "written_by": "independent", "quote": "flags posts indicating possible self-harm and shares alerts with authorities" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": "Police said Meta sent a suicide alert regarding the post to the Social Media Centre at the police headquarters at 6.53 pm." }, { "kind": "news report", "citation": "Kaste, M. (2018). Facebook Increasingly Reliant On A.I. To Predict Suicide Risk. NPR (transcript republished by North Country Public Radio).", "title": "Facebook Increasingly Reliant On A.I. To Predict Suicide Risk", "url": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk", "date": "2018-11-17", "written_by": "independent", "quote": "We also are able to use AI to coordinate a bunch of information on location to try to identify the location of that individual" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it's determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "news report", "citation": "PTI (2023). Facebook helps UP Police avert two suicides in a week in state. The Tribune.", "title": "Facebook helps UP Police avert two suicides in a week in state", "url": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp", "date": "2023-02-05", "written_by": "independent", "quote": "Meta Hqs USA immediately sends an alert to the social media centre of PHQ UP Police through phone and email" }, { "kind": "news report", "citation": "PTI (2021). Man saved by Delhi Police after Facebook alerts on self-harm video. The Tribune.", "title": "Man saved by Delhi Police after Facebook alerts on self-harm video", "url": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp", "date": "2021-06-05", "written_by": "independent", "quote": "received a call alert from Facebook's US office about a suspicious self-harm live video being posted by a male Facebook user located in Delhi" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "7 June 2026: Meta alert at 6:53 pm; Meerut police reached a 25-year-old in 8 minutes, found him critical; hospital treatment, improved", "account": "On 7 June 2026 a 25-year-old man from the Sardhana area of Meerut, Uttar Pradesh, posted an Instagram video. In it he appeared to drink a white liquid. Police said Meta sent a suicide alert about the post to the Social Media Center at Uttar Pradesh Police headquarters at 6:53 pm. The center alerted Meerut Police, and a Sardhana team reached his home within eight minutes. They found him in a critical condition with a bottle of suspected liquid nearby and took him to hospital, where his condition improved after treatment. He told police he was under stress following the end of a relationship. Police data cited by PTI credit such alerts with 3,011 lives saved statewide from 1 January 2023 to 31 May 2026." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Meta proactive self-harm detection on Instagram with alert routing to the Uttar Pradesh Police Social Media Center" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Meta self-harm alert routed to Uttar Pradesh Police leads to hospital treatment of a 25-year-old in Meerut within eight minutes", "headline": "Meta self-harm alert gets Meerut police to a man in 8 minutes" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "343d2f4067b78a2fb96d5b25eda3d840e5bf4035a99427db5e079f2fbcdc2624" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 7 June 2026 a 25-year-old man from the Sardhana area of Meerut, Uttar Pradesh, posted an Instagram video. In it he appeared to drink a white liquid. Police said Meta sent a suicide alert about the post to the Social Media Center at Uttar Pradesh Police headquarters at 6:53 pm. The center alerted Meerut Police, and a Sardhana team reached his home within eight minutes. They found him in a critical condition with a bottle of suspected liquid nearby and took him to hospital, where his condition improved after treatment. He told police he was under stress following the end of a relationship. Police data cited by PTI credit such alerts with 3,011 lives saved statewide from 1 January 2023 to 31 May 2026.", "replication": { "produced_by": [ "Platform detection and review that flags self-harm posts and contacts a police nodal center with account phone and location details", "Police nodal center with a standing arrangement to dispatch local officers immediately" ], "must_hold": [ "Account details resolve to a reachable address within minutes", "Local officers are available to respond immediately" ], "echoes": [ { "setting": "Dwarka, Delhi (reported 5 June 2021): Delhi Police CyPAD received a Facebook alert at about 12:50 am on a self-harm live video; the man was found with heavy blood loss, taken to hospital and AIIMS Trauma Center, condition stable (PTI/The Tribune)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/delhi/man-saved-by-delhi-police-after-facebook-alerts-on-self-harm-video-263850/amp" }, { "setting": "Ambedkarnagar, Uttar Pradesh (late January 2023): Meta alert at 11:37 pm on a student's Instagram photos of pills; police arrived within 15 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "date": "2023-01-31", "setting": "Ghaziabad, Uttar Pradesh: Instagram live of a man preparing to hang himself; Inspector Anita Chauhan arrived within 13 minutes and prevented the attempt (PTI/The Tribune, 5 February 2023)", "outcome": "recurred", "source": "https://www.tribuneindia.com/news/nation/facebook-helps-up-police-avert-two-suicides-in-a-week-in-state-476915/amp" }, { "setting": "Upstate New York, from a Facebook promotional video described by NPR (17 November 2018): officers found a woman who had taken medication and got her to a local hospital", "outcome": "recurred", "source": "https://www.northcountrypublicradio.org/news/npr/668408122/facebook-increasingly-reliant-on-a-i-to-predict-suicide-risk" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0159", "case_url": "records/AIBD-0159.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Meta's detection and review system is built to find posts and live videos that indicate suicide or self-injury risk. It routes them to trained reviewers and, in imminent cases, contacts local authorities. The Uttar Pradesh Police Social Media Center exists to act on those alerts. This rescue is that purpose." }, { "label": "AI contribution", "value": "Police say Meta flagged the Instagram post at about 8:58 pm on 29 March and emailed the Uttar Pradesh Police Social Media Center. No source says whether Meta's classifier or a user report started the review. Decode reports that UP alerts arise from “artificial intelligence systems or user reports” reviewed by Meta staff. Everything after the email was human work. The center traced the phone number and location. Raebareli police reached the home in 12 minutes on the DGP's instructions, and a hospital treated him." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0159", "title": "Meta flags a Raebareli 18-year-old's Instagram suicide post at 8:58 pm; Uttar Pradesh Police reach him within 12 minutes after he took rat poison, and he is treated and stable", "summary": "29 March 2026: Meta alert at 8:58 pm; Raebareli police reached an 18-year-old in 12 minutes after rat poison; hospital treatment, stable", "date_observed": "2026-03-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 29 March 2026 an 18-year-old class 12 student from Raebareli, Uttar Pradesh, texted his girlfriend “Alvida” after a breakup. He lived within the Mill Area police station limits. He also posted a suicide video on Instagram with rat poison and messages of intent. At about 8:58 pm Meta flagged the content and emailed an alert to the Social Media Center at Uttar Pradesh Police headquarters. The center traced his mobile number and location and alerted Raebareli police. A police team reached his home within 12 minutes and found him in severe distress, with an empty rat-poison packet nearby. Police and family took him to a nearby hospital, where he was treated. On 1 April 2026 police said he was stable.", "category": "harm prevented", "unexpected": false, "purpose": "Meta's detection and review system is built to find posts and live videos that indicate suicide or self-injury risk. It routes them to trained reviewers and, in imminent cases, contacts local authorities. The Uttar Pradesh Police Social Media Center exists to act on those alerts. This rescue is that purpose.", "system": "Meta's suicide and self-injury detection and review pipeline on Instagram, with email alerts to the Uttar Pradesh Police Social Media Center at DGP headquarters", "model_version": null, "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (Decode: Uttar Pradesh has been part of Meta's suicide-prevention program since 2022)", "location": "Raebareli, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2026-04-01-6e56" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "An 18-year-old who had taken rat poison was reached within 12 minutes of Meta's 8:58 pm alert. He was treated in hospital and reported stable. Officials said even a slight delay could have been fatal. No clinician is quoted and the rodenticide is not identified. The life-saving reading rests on the police account." } ], "ai_role": { "status": "assessed", "statement": "Police say Meta flagged the Instagram post at about 8:58 pm on 29 March and emailed the Uttar Pradesh Police Social Media Center. No source says whether Meta's classifier or a user report started the review. Decode reports that UP alerts arise from “artificial intelligence systems or user reports” reviewed by Meta staff. Everything after the email was human work. The center traced the phone number and location. Raebareli police reached the home in 12 minutes on the DGP's instructions, and a hospital treated him." }, "evidence": [ { "kind": "news report", "citation": "TNN (2026). Cops save 18-yr-old from suicide after Meta alert. The Times of India, Lucknow.", "title": "Cops save 18-yr-old from suicide after Meta alert", "url": "https://timesofindia.indiatimes.com/city/lucknow/cops-save-18-yr-old-from-suicide-after-meta-alert/articleshow/129935943.cms", "date": "2026-04-01", "written_by": "independent", "quote": "At around 8:58pm, Meta flagged the content and sent an alert via email to the social media centre at the UP police headquarters." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert. Telangana Today.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://telanganatoday.com/teens-life-saved-in-12-minutes-up-police-act-swiftly-after-social-media-suicide-alert", "date": "2026-04-01", "written_by": "independent", "quote": "The information was received by the police through Meta. Action was taken within minutes, and he was saved." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: Raebareli police act swiftly after social media suicide alert. IANS Live.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://ianslive.in/teens-life-saved-in-12-minutes-raebareli-police-act-swiftly-after-social-media-suicide-alert--20260401091324", "date": "2026-04-01", "written_by": "independent", "quote": "a video related to suicide was posted on Instagram, triggering an alert through the Meta Alert System at the police headquarters" }, { "kind": "news report", "citation": "Mathrubhumi English (2026). 'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen.", "title": "'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen", "url": "https://english.mathrubhumi.com/news/good-news/up-police-rescue-teen-social-media-alert-ar7x14gi", "date": "2026-04-01", "written_by": "independent", "quote": "police in Raebareli saved an 18-year-old youth after a distressing Instagram post triggered an automated alert through Meta's monitoring system" }, { "kind": "news report", "citation": "Srivastava, S. (2026). Meta Flagged A Suicide Post. Police Had Minutes To Save The Man. Decode (BOOM).", "title": "Meta Flagged A Suicide Post. Police Had Minutes To Save The Man", "url": "https://www.decodeinternet.in/decode/meta-sent-suicide-alert-uttar-pradesh-police-had-minutes-to-save-30394", "date": "2026-01-06", "written_by": "independent", "quote": "flagged—by artificial intelligence systems or user reports—as potentially indicating self-harm are reviewed by Meta and, in high-risk cases, shared with law enforcement partners" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it’s determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "press release", "citation": "Rosen, G. (2017). Getting Our Community Help in Real Time. Meta Newsroom.", "title": "Getting Our Community Help in Real Time", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/", "date": "2017-11-27", "written_by": "makers", "quote": "Using pattern recognition to detect posts or live videos where someone might be expressing thoughts of suicide" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta. The Hans India, 10 June 2026.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "An Instagram post with rat poison and messages of intent, after a breakup. Meta's detection and review system flagged it as a suicide risk at about 8:58 pm on 29 March 2026", "A standing arrangement, in place since 2022 per Decode, under which Meta emails such alerts to the Uttar Pradesh Police Social Media Center. The center traces the account's phone number and location and forwards them to district police", "A Raebareli police team able to reach the Mill Area address within 12 minutes and take him to a nearby hospital with his family" ], "must_hold": [ "The post is flagged and reviewed while the person can still be treated. Here the alert came at about 8:58 pm and police arrived within 12 minutes", "The account leads to a phone number and a reachable address. Decode reports that UP police usually receive a phone number and trace the SIM and live location", "Officers and a hospital are available at once. Officials said even a slight delay could have been fatal" ], "echoes": [] }, "paired_incident": null, "headline": "Meta flags a suicide post; Raebareli police arrive in 12 minutes", "metric": "A police team reached his home within 12 minutes", "comparator": { "against": "reports escalated without Meta's pattern-recognition acceleration", "before": "escalation speed of other reports", "after": "escalated to local authorities twice as quickly as other reports", "basis": "measured", "quote": "We also use pattern recognition to help accelerate the most concerning reports. We’ve found these accelerated reports— that we have signaled require immediate attention—are escalated to local authorities twice as quickly as other reports.", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/" }, "later": { "status": "persisted", "date": "2026-06-10", "note": "A further Meta alert on 7 June 2026 led Meerut police to reach a 25-year-old within eight minutes of a suicide post and get him to hospital.", "quote": "A 25-year-old man’s life was saved after Meerut Police acted on a suicide alert received from Meta following an Instagram post, reaching him within eight minutes and getting him admitted to a hospital, officials have said.", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-01-6e56", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/; https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0159", "catalog_version": "1.0.0", "source_fingerprint": "24892167a8185c5d54ff449ed7c0697ebac643babb4fe7e28ef0fa4aa89df921", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0159-source-0001", "AIBD-0159-source-0002", "AIBD-0159-source-0003", "AIBD-0159-source-0004", "AIBD-0159-source-0005", "AIBD-0159-source-0006", "AIBD-0159-source-0007", "AIBD-0159-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Meta Platforms", "deployer": "Meta Platforms with the Uttar Pradesh Police Social Media Center (Decode: Uttar Pradesh has been part of Meta's suicide-prevention program since 2022)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Police say Meta flagged the Instagram post at about 8:58 pm on 29 March and emailed the Uttar Pradesh Police Social Media Center. 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No source says whether Meta's classifier or a user report started the review. Decode reports that UP alerts arise from “artificial intelligence systems or user reports” reviewed by Meta staff. Everything after the email was human work. The center traced the phone number and location. Raebareli police reached the home in 12 minutes on the DGP's instructions, and a hospital treated him." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "An 18-year-old who had taken rat poison was reached within 12 minutes of Meta's 8:58 pm alert. He was treated in hospital and reported stable. Officials said even a slight delay could have been fatal. 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"state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-04-01-6e56" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-06-10", "note": "A further Meta alert on 7 June 2026 led Meerut police to reach a 25-year-old within eight minutes of a suicide post and get him to hospital.", "quote": "A 25-year-old man’s life was saved after Meerut Police acted on a suicide alert received from Meta following an Instagram post, reaching him within eight minutes and getting him admitted to a hospital, officials have said.", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0159" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 29 March 2026 an 18-year-old class 12 student from Raebareli, Uttar Pradesh, texted his girlfriend “Alvida” after a breakup. He lived within the Mill Area police station limits. He also posted a suicide video on Instagram with rat poison and messages of intent. At about 8:58 pm Meta flagged the content and emailed an alert to the Social Media Center at Uttar Pradesh Police headquarters. The center traced his mobile number and location and alerted Raebareli police. A police team reached his home within 12 minutes and found him in severe distress, with an empty rat-poison packet nearby. Police and family took him to a nearby hospital, where he was treated. On 1 April 2026 police said he was stable.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "An 18-year-old who had taken rat poison was reached within 12 minutes of Meta's 8:58 pm alert. He was treated in hospital and reported stable. Officials said even a slight delay could have been fatal. No clinician is quoted and the rodenticide is not identified. The life-saving reading rests on the police account." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Meta's detection and review system is built to find posts and live videos that indicate suicide or self-injury risk. It routes them to trained reviewers and, in imminent cases, contacts local authorities. 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This rescue is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Raebareli, India", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Meta's detection and review system is built to find posts and live videos that indicate suicide or self-injury risk. It routes them to trained reviewers and, in imminent cases, contacts local authorities. The Uttar Pradesh Police Social Media Center exists to act on those alerts. This rescue is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-03-29", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-01-6e56", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/; https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "An Instagram post with rat poison and messages of intent, after a breakup. Meta's detection and review system flagged it as a suicide risk at about 8:58 pm on 29 March 2026", "A standing arrangement, in place since 2022 per Decode, under which Meta emails such alerts to the Uttar Pradesh Police Social Media Center. The center traces the account's phone number and location and forwards them to district police", "A Raebareli police team able to reach the Mill Area address within 12 minutes and take him to a nearby hospital with his family" ], "must_hold": [ "The post is flagged and reviewed while the person can still be treated. Here the alert came at about 8:58 pm and police arrived within 12 minutes", "The account leads to a phone number and a reachable address. Decode reports that UP police usually receive a phone number and trace the SIM and live location", "Officers and a hospital are available at once. Officials said even a slight delay could have been fatal" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-01-6e56", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/; https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2026). Cops save 18-yr-old from suicide after Meta alert. The Times of India, Lucknow.", "title": "Cops save 18-yr-old from suicide after Meta alert", "url": "https://timesofindia.indiatimes.com/city/lucknow/cops-save-18-yr-old-from-suicide-after-meta-alert/articleshow/129935943.cms", "date": "2026-04-01", "written_by": "independent", "quote": "At around 8:58pm, Meta flagged the content and sent an alert via email to the social media centre at the UP police headquarters." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert. Telangana Today.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://telanganatoday.com/teens-life-saved-in-12-minutes-up-police-act-swiftly-after-social-media-suicide-alert", "date": "2026-04-01", "written_by": "independent", "quote": "The information was received by the police through Meta. Action was taken within minutes, and he was saved." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: Raebareli police act swiftly after social media suicide alert. IANS Live.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://ianslive.in/teens-life-saved-in-12-minutes-raebareli-police-act-swiftly-after-social-media-suicide-alert--20260401091324", "date": "2026-04-01", "written_by": "independent", "quote": "a video related to suicide was posted on Instagram, triggering an alert through the Meta Alert System at the police headquarters" }, { "kind": "news report", "citation": "Mathrubhumi English (2026). 'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen.", "title": "'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen", "url": "https://english.mathrubhumi.com/news/good-news/up-police-rescue-teen-social-media-alert-ar7x14gi", "date": "2026-04-01", "written_by": "independent", "quote": "police in Raebareli saved an 18-year-old youth after a distressing Instagram post triggered an automated alert through Meta's monitoring system" }, { "kind": "news report", "citation": "Srivastava, S. (2026). Meta Flagged A Suicide Post. Police Had Minutes To Save The Man. Decode (BOOM).", "title": "Meta Flagged A Suicide Post. Police Had Minutes To Save The Man", "url": "https://www.decodeinternet.in/decode/meta-sent-suicide-alert-uttar-pradesh-police-had-minutes-to-save-30394", "date": "2026-01-06", "written_by": "independent", "quote": "flagged—by artificial intelligence systems or user reports—as potentially indicating self-harm are reviewed by Meta and, in high-risk cases, shared with law enforcement partners" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it’s determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "press release", "citation": "Rosen, G. (2017). Getting Our Community Help in Real Time. Meta Newsroom.", "title": "Getting Our Community Help in Real Time", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/", "date": "2017-11-27", "written_by": "makers", "quote": "Using pattern recognition to detect posts or live videos where someone might be expressing thoughts of suicide" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta. The Hans India, 10 June 2026.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0159-source-0001", "AIBD-0159-source-0002", "AIBD-0159-source-0003", "AIBD-0159-source-0004", "AIBD-0159-source-0005", "AIBD-0159-source-0006", "AIBD-0159-source-0007", "AIBD-0159-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2026). Cops save 18-yr-old from suicide after Meta alert. The Times of India, Lucknow.", "title": "Cops save 18-yr-old from suicide after Meta alert", "url": "https://timesofindia.indiatimes.com/city/lucknow/cops-save-18-yr-old-from-suicide-after-meta-alert/articleshow/129935943.cms", "date": "2026-04-01", "written_by": "independent", "quote": "At around 8:58pm, Meta flagged the content and sent an alert via email to the social media centre at the UP police headquarters." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert. Telangana Today.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://telanganatoday.com/teens-life-saved-in-12-minutes-up-police-act-swiftly-after-social-media-suicide-alert", "date": "2026-04-01", "written_by": "independent", "quote": "The information was received by the police through Meta. Action was taken within minutes, and he was saved." }, { "kind": "news report", "citation": "IANS (2026). Teen's life saved in 12 minutes: Raebareli police act swiftly after social media suicide alert. IANS Live.", "title": "Teen's life saved in 12 minutes: UP Police act swiftly after social media suicide alert", "url": "https://ianslive.in/teens-life-saved-in-12-minutes-raebareli-police-act-swiftly-after-social-media-suicide-alert--20260401091324", "date": "2026-04-01", "written_by": "independent", "quote": "a video related to suicide was posted on Instagram, triggering an alert through the Meta Alert System at the police headquarters" }, { "kind": "news report", "citation": "Mathrubhumi English (2026). 'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen.", "title": "'Alvida' text, poison post, and a 12-min race against death: How UP police saved a teen", "url": "https://english.mathrubhumi.com/news/good-news/up-police-rescue-teen-social-media-alert-ar7x14gi", "date": "2026-04-01", "written_by": "independent", "quote": "police in Raebareli saved an 18-year-old youth after a distressing Instagram post triggered an automated alert through Meta's monitoring system" }, { "kind": "news report", "citation": "Srivastava, S. (2026). Meta Flagged A Suicide Post. Police Had Minutes To Save The Man. Decode (BOOM).", "title": "Meta Flagged A Suicide Post. Police Had Minutes To Save The Man", "url": "https://www.decodeinternet.in/decode/meta-sent-suicide-alert-uttar-pradesh-police-had-minutes-to-save-30394", "date": "2026-01-06", "written_by": "independent", "quote": "flagged—by artificial intelligence systems or user reports—as potentially indicating self-harm are reviewed by Meta and, in high-risk cases, shared with law enforcement partners" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom.", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "In serious cases, when it’s determined that there may be imminent danger of self harm, Facebook may contact local authorities." }, { "kind": "press release", "citation": "Rosen, G. (2017). Getting Our Community Help in Real Time. Meta Newsroom.", "title": "Getting Our Community Help in Real Time", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/", "date": "2017-11-27", "written_by": "makers", "quote": "Using pattern recognition to detect posts or live videos where someone might be expressing thoughts of suicide" }, { "kind": "news report", "citation": "The Hans India (2026). Cops save man in 8 mins after suicide alert on Insta. The Hans India, 10 June 2026.", "title": "Cops save man in 8 mins after suicide alert on Insta", "url": "https://www.thehansindia.com/news/national/cops-save-man-in-8-mins-after-suicide-alert-on-insta-1084758", "date": "2026-06-10", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "29 March 2026: Meta alert at 8:58 pm; Raebareli police reached an 18-year-old in 12 minutes after rat poison; hospital treatment, stable", "account": "On 29 March 2026 an 18-year-old class 12 student from Raebareli, Uttar Pradesh, texted his girlfriend “Alvida” after a breakup. He lived within the Mill Area police station limits. He also posted a suicide video on Instagram with rat poison and messages of intent. At about 8:58 pm Meta flagged the content and emailed an alert to the Social Media Center at Uttar Pradesh Police headquarters. The center traced his mobile number and location and alerted Raebareli police. A police team reached his home within 12 minutes and found him in severe distress, with an empty rat-poison packet nearby. Police and family took him to a nearby hospital, where he was treated. On 1 April 2026 police said he was stable." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Meta's suicide and self-injury detection and review pipeline on Instagram, with email alerts to the Uttar Pradesh Police Social Media Center at DGP headquarters" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Meta flags a Raebareli 18-year-old's Instagram suicide post at 8:58 pm; Uttar Pradesh Police reach him within 12 minutes after he took rat poison, and he is treated and stable", "headline": "Meta flags a suicide post; Raebareli police arrive in 12 minutes" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "24892167a8185c5d54ff449ed7c0697ebac643babb4fe7e28ef0fa4aa89df921" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 29 March 2026 an 18-year-old class 12 student from Raebareli, Uttar Pradesh, texted his girlfriend “Alvida” after a breakup. He lived within the Mill Area police station limits. He also posted a suicide video on Instagram with rat poison and messages of intent. At about 8:58 pm Meta flagged the content and emailed an alert to the Social Media Center at Uttar Pradesh Police headquarters. The center traced his mobile number and location and alerted Raebareli police. A police team reached his home within 12 minutes and found him in severe distress, with an empty rat-poison packet nearby. Police and family took him to a nearby hospital, where he was treated. On 1 April 2026 police said he was stable.", "replication": { "produced_by": [ "An Instagram post with rat poison and messages of intent, after a breakup. Meta's detection and review system flagged it as a suicide risk at about 8:58 pm on 29 March 2026", "A standing arrangement, in place since 2022 per Decode, under which Meta emails such alerts to the Uttar Pradesh Police Social Media Center. The center traces the account's phone number and location and forwards them to district police", "A Raebareli police team able to reach the Mill Area address within 12 minutes and take him to a nearby hospital with his family" ], "must_hold": [ "The post is flagged and reviewed while the person can still be treated. Here the alert came at about 8:58 pm and police arrived within 12 minutes", "The account leads to a phone number and a reachable address. Decode reports that UP police usually receive a phone number and trace the SIM and live location", "Officers and a hospital are available at once. Officials said even a slight delay could have been fatal" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0224", "case_url": "records/AIBD-0224.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Identify previously unknown victims and offenders in seized child sexual abuse material so that children can be located and rescued." }, { "label": "AI contribution", "value": "Official and independent sources agree the surge produced the identifications. The AI's share is described only qualitatively. NBC quotes an HSI official on AI-enhanced images giving investigators a starting point or tentative identification. ABC names Clearview AI's facial recognition as one tool. HSI did not confirm any product, and much of the work was manual contextual analysis (flora, fauna, geolocation). No figure separates AI-derived identifications from the rest." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0224", "title": "HSI's Operation Renewed Hope uses facial recognition and AI image enhancement to identify 311 probable child victims in cold cases", "summary": "A three-week HSI-led surge from 17 Jul 2023 produced 311 probable identifications of unknown child victims, 14 positive contacts and several rescues; officials credit facial recognition and AI images.", "date_observed": "2023-08-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The operation began on 17 July 2023 and ended in early August. HSI's Cyber Crimes Center hosted investigators from the FBI, U.S. Marshals, Interpol, Europol and 13 foreign agencies to review unidentified child sexual abuse material series. ICE reports 311 probable identifications of previously unknown victims, 14 positive contacts and several confirmed rescues from active abuse. Over 100 leads went to field offices and 25 countries, with arrests already made in the United States and Canada. NBC News reports that HSI officials credited new facial recognition and artificial intelligence technology for fresh leads. Mike Prado of HSI described AI- or machine-learning-enhanced images as a starting point for investigation. ABC (Australia) reports that the operation used Clearview AI's facial recognition alongside other techniques, citing Queensland Police analyst Scott Anderson. Forbes reports that officials did not confirm Clearview or any specific program. The ICE release itself does not mention AI.", "category": "benefit created", "unexpected": false, "purpose": "Identify previously unknown victims and offenders in seized child sexual abuse material so that children can be located and rescued.", "system": "Facial recognition (reported as Clearview AI) and AI/machine-learning image enhancement used in victim identification", "model_version": null, "developer": "Clearview AI (reported by ABC and Forbes; not confirmed by HSI) and unnamed image-enhancement tools", "deployer": "Homeland Security Investigations (U.S. Immigration and Customs Enforcement), Cyber Crimes Center", "location": "Washington, United States", "domain": "Child protection / online child sexual exploitation", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 4, "reason": "ICE reports several confirmed rescues of children from active abuse and 14 positive contacts. The number of rescues is unstated." }, { "group": "societal", "depth": 3, "reason": "More than 100 leads disseminated and arrests of active abusers in the United States and Canada, per ICE and NBC." } ], "ai_role": { "status": "assessed", "statement": "Official and independent sources agree the surge produced the identifications. The AI's share is described only qualitatively. NBC quotes an HSI official on AI-enhanced images giving investigators a starting point or tentative identification. ABC names Clearview AI's facial recognition as one tool. HSI did not confirm any product, and much of the work was manual contextual analysis (flora, fauna, geolocation). No figure separates AI-derived identifications from the rest." }, "evidence": [ { "kind": "press release", "citation": "U.S. Immigration and Customs Enforcement, news release, 9 Aug 2023", "title": "HSI, partners launch first US-based international victim identification surge", "url": "https://www.ice.gov/news/releases/hsi-partners-launch-first-us-based-international-victim-identification-surge", "date": "2023-08-09", "written_by": "deployer", "quote": "resulting in the generation of 311 probable identifications of previously unknown victims, including 14 positive contacts and several confirmed victim rescues from active abuse." }, { "kind": "news report", "citation": "Julia Ainsley, NBC News, 9 Aug 2023", "title": "Homeland Security identifies 311 child victims of sexual exploitation in 'cold cases'", "url": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357", "date": "2023-08-09", "written_by": "independent", "quote": "Thanks to new facial recognition and artificial intelligence technology, there are now fresh leads in these formerly cold cases." }, { "kind": "news report", "citation": "Antonia O'Flaherty, ABC News (Australia), 24 Aug 2023", "title": "Operation Renewed Hope identifies more than 300 probable victims of child sexual abuse using controversial AI", "url": "https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "date": "2023-08-24", "written_by": "independent", "quote": "The operation used the US-based company Clearview AI's facial recognition technology as well as other victim identification techniques." }, { "kind": "news report", "citation": "Mary Whitfill Roeloffs, Forbes, 9 Aug 2023", "title": "DHS Says It Identified 311 New Child Sexual Exploitation Victims—After Forbes Reports AI Facial Recognition Push", "url": "https://www.forbes.com/sites/maryroeloffs/2023/08/09/dhs-says-it-identified-311-new-child-sexual-exploitation-victims-after-forbes-reports-ai-facial-recognition-push/", "date": "2023-08-09", "written_by": "independent", "quote": "scanning more than 4.3 million images and videos to pick up on previously overlooked clues or identifying materials." } ], "replication": { "produced_by": [ "Large holdings of unidentified abuse-material series concentrated for a time-boxed multi-agency review", "Facial recognition against web-scraped reference images plus AI image enhancement feeding human investigators", "Lead dissemination through Interpol's ICSE database and partner agencies" ], "must_hold": [ "Legal authority to use web-scraped facial recognition (banned for this use in Australia, per ABC)", "Human investigators to verify tentative identifications before action", "Partner agencies able to act on leads" ], "echoes": [ { "date": "2024-03-14", "setting": "Second US-based international victim identification surge reported by HSToday (headline only)", "model_version": null, "outcome": "unknown", "source": "https://www.hstoday.us/subject-matter-areas/customs-immigration/hsi-partners-conduct-us-based-international-victim-identification-surge/" } ] }, "paired_incident": null, "headline": "Facial recognition helps HSI identify 311 probable child victims", "metric": "311 probable identifications of unknown child victims, 14 positive contacts", "comparator": { "against": "the same abuse-material series as previously reviewed, which had left the cases cold", "before": "formerly cold cases", "after": "there are now fresh leads", "basis": "assumed", "quote": "Thanks to new facial recognition and artificial intelligence technology, there are now fresh leads in these formerly cold cases.", "url": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357" }, "later": { "status": "persisted", "date": "2023-08-24", "note": "Two weeks after the results release, ABC reported that the operation had led to arrests of active abusers in the United States and Canada.", "quote": "The three-week operation led to the arrests of active abusers in the United States and Canada.", "url": "https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357; https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Clearview_AI; https://en.wikipedia.org/wiki/Homeland_Security_Investigations", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0224", "catalog_version": "1.0.0", "source_fingerprint": "c810f6131be841afe2b59d0dda3bc5c8de217b37e35eb344472e8535098e3170", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0224-source-0001", "AIBD-0224-source-0002", "AIBD-0224-source-0003", "AIBD-0224-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Clearview AI (reported by ABC and Forbes; not confirmed by HSI) and unnamed image-enhancement tools", "deployer": "Homeland Security Investigations (U.S. Immigration and Customs Enforcement), Cyber Crimes Center" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Official and independent sources agree the surge produced the identifications. 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three-week operation led to the arrests of active abusers in the United States and Canada.", "url": "https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0224" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", 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HSI's Cyber Crimes Center hosted investigators from the FBI, U.S. Marshals, Interpol, Europol and 13 foreign agencies to review unidentified child sexual abuse material series. ICE reports 311 probable identifications of previously unknown victims, 14 positive contacts and several confirmed rescues from active abuse. Over 100 leads went to field offices and 25 countries, with arrests already made in the United States and Canada. NBC News reports that HSI officials credited new facial recognition and artificial intelligence technology for fresh leads. Mike Prado of HSI described AI- or machine-learning-enhanced images as a starting point for investigation. ABC (Australia) reports that the operation used Clearview AI's facial recognition alongside other techniques, citing Queensland Police analyst Scott Anderson. Forbes reports that officials did not confirm Clearview or any specific program. The ICE release itself does not mention AI.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "ICE reports several confirmed rescues of children from active abuse and 14 positive contacts. The number of rescues is unstated." }, { "group": "societal", "depth": 3, "reason": "More than 100 leads disseminated and arrests of active abusers in the United States and Canada, per ICE and NBC." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify previously unknown victims and offenders in seized child sexual abuse material so that children can be located and rescued." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Washington, United States", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Identify previously unknown victims and offenders in seized child sexual abuse material so that children can be located and rescued." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-08-09", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357; https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Clearview_AI; https://en.wikipedia.org/wiki/Homeland_Security_Investigations", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Large holdings of unidentified abuse-material series concentrated for a time-boxed multi-agency review", "Facial recognition against web-scraped reference images plus AI image enhancement feeding human investigators", "Lead dissemination through Interpol's ICSE database and partner agencies" ], "must_hold": [ "Legal authority to use web-scraped facial recognition (banned for this use in Australia, per ABC)", "Human investigators to verify tentative identifications before action", "Partner agencies able to act on leads" ], "echoes": [ { "date": "2024-03-14", "setting": "Second US-based international victim identification surge reported by HSToday (headline only)", "outcome": "unknown", "source": "https://www.hstoday.us/subject-matter-areas/customs-immigration/hsi-partners-conduct-us-based-international-victim-identification-surge/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Large holdings of unidentified abuse-material series concentrated for a time-boxed multi-agency review", "Facial recognition against web-scraped reference images plus AI image enhancement feeding human investigators", "Lead dissemination through Interpol's ICSE database and partner agencies" ], "must_hold": [ "Legal authority to use web-scraped facial recognition (banned for this use in Australia, per ABC)", "Human investigators to verify tentative identifications before action", "Partner agencies able to act on leads" ], "echoes": [ { "date": "2024-03-14", "setting": "Second US-based international victim identification surge reported by HSToday (headline only)", "outcome": "unknown", "source": "https://www.hstoday.us/subject-matter-areas/customs-immigration/hsi-partners-conduct-us-based-international-victim-identification-surge/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357; https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Clearview_AI; https://en.wikipedia.org/wiki/Homeland_Security_Investigations", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Child protection / online child sexual exploitation", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Immigration and Customs Enforcement, news release, 9 Aug 2023", "title": "HSI, partners launch first US-based international victim identification surge", "url": "https://www.ice.gov/news/releases/hsi-partners-launch-first-us-based-international-victim-identification-surge", "date": "2023-08-09", "written_by": "deployer", "quote": "resulting in the generation of 311 probable identifications of previously unknown victims, including 14 positive contacts and several confirmed victim rescues from active abuse." }, { "kind": "news report", "citation": "Julia Ainsley, NBC News, 9 Aug 2023", "title": "Homeland Security identifies 311 child victims of sexual exploitation in 'cold cases'", "url": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357", "date": "2023-08-09", "written_by": "independent", "quote": "Thanks to new facial recognition and artificial intelligence technology, there are now fresh leads in these formerly cold cases." }, { "kind": "news report", "citation": "Antonia O'Flaherty, ABC News (Australia), 24 Aug 2023", "title": "Operation Renewed Hope identifies more than 300 probable victims of child sexual abuse using controversial AI", "url": "https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "date": "2023-08-24", "written_by": "independent", "quote": "The operation used the US-based company Clearview AI's facial recognition technology as well as other victim identification techniques." }, { "kind": "news report", "citation": "Mary Whitfill Roeloffs, Forbes, 9 Aug 2023", "title": "DHS Says It Identified 311 New Child Sexual Exploitation Victims—After Forbes Reports AI Facial Recognition Push", "url": "https://www.forbes.com/sites/maryroeloffs/2023/08/09/dhs-says-it-identified-311-new-child-sexual-exploitation-victims-after-forbes-reports-ai-facial-recognition-push/", "date": "2023-08-09", "written_by": "independent", "quote": "scanning more than 4.3 million images and videos to pick up on previously overlooked clues or identifying materials." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0224-source-0001", "AIBD-0224-source-0002", "AIBD-0224-source-0003", "AIBD-0224-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Immigration and Customs Enforcement, news release, 9 Aug 2023", "title": "HSI, partners launch first US-based international victim identification surge", "url": "https://www.ice.gov/news/releases/hsi-partners-launch-first-us-based-international-victim-identification-surge", "date": "2023-08-09", "written_by": "deployer", "quote": "resulting in the generation of 311 probable identifications of previously unknown victims, including 14 positive contacts and several confirmed victim rescues from active abuse." }, { "kind": "news report", "citation": "Julia Ainsley, NBC News, 9 Aug 2023", "title": "Homeland Security identifies 311 child victims of sexual exploitation in 'cold cases'", "url": "https://www.nbcnews.com/news/investigations/homeland-security-identifies-311-child-victims-sexual-exploitation-rcna97357", "date": "2023-08-09", "written_by": "independent", "quote": "Thanks to new facial recognition and artificial intelligence technology, there are now fresh leads in these formerly cold cases." }, { "kind": "news report", "citation": "Antonia O'Flaherty, ABC News (Australia), 24 Aug 2023", "title": "Operation Renewed Hope identifies more than 300 probable victims of child sexual abuse using controversial AI", "url": "https://www.abc.net.au/news/2023-08-24/qld-facial-recognition-child-abuse-artificial-intelligence/102761618", "date": "2023-08-24", "written_by": "independent", "quote": "The operation used the US-based company Clearview AI's facial recognition technology as well as other victim identification techniques." }, { "kind": "news report", "citation": "Mary Whitfill Roeloffs, Forbes, 9 Aug 2023", "title": "DHS Says It Identified 311 New Child Sexual Exploitation Victims—After Forbes Reports AI Facial Recognition Push", "url": "https://www.forbes.com/sites/maryroeloffs/2023/08/09/dhs-says-it-identified-311-new-child-sexual-exploitation-victims-after-forbes-reports-ai-facial-recognition-push/", "date": "2023-08-09", "written_by": "independent", "quote": "scanning more than 4.3 million images and videos to pick up on previously overlooked clues or identifying materials." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "A three-week HSI-led surge from 17 Jul 2023 produced 311 probable identifications of unknown child victims, 14 positive contacts and several rescues; officials credit facial recognition and AI images.", "account": "The operation began on 17 July 2023 and ended in early August. HSI's Cyber Crimes Center hosted investigators from the FBI, U.S. Marshals, Interpol, Europol and 13 foreign agencies to review unidentified child sexual abuse material series. ICE reports 311 probable identifications of previously unknown victims, 14 positive contacts and several confirmed rescues from active abuse. Over 100 leads went to field offices and 25 countries, with arrests already made in the United States and Canada. NBC News reports that HSI officials credited new facial recognition and artificial intelligence technology for fresh leads. Mike Prado of HSI described AI- or machine-learning-enhanced images as a starting point for investigation. ABC (Australia) reports that the operation used Clearview AI's facial recognition alongside other techniques, citing Queensland Police analyst Scott Anderson. Forbes reports that officials did not confirm Clearview or any specific program. The ICE release itself does not mention AI." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Facial recognition (reported as Clearview AI) and AI/machine-learning image enhancement used in victim identification" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Clearview AI (reported by ABC and Forbes; not confirmed by HSI) and unnamed image-enhancement tools" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "HSI's Operation Renewed Hope uses facial recognition and AI image enhancement to identify 311 probable child victims in cold cases", "headline": "Facial recognition helps HSI identify 311 probable child victims" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c810f6131be841afe2b59d0dda3bc5c8de217b37e35eb344472e8535098e3170" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The operation began on 17 July 2023 and ended in early August. HSI's Cyber Crimes Center hosted investigators from the FBI, U.S. Marshals, Interpol, Europol and 13 foreign agencies to review unidentified child sexual abuse material series. ICE reports 311 probable identifications of previously unknown victims, 14 positive contacts and several confirmed rescues from active abuse. Over 100 leads went to field offices and 25 countries, with arrests already made in the United States and Canada. NBC News reports that HSI officials credited new facial recognition and artificial intelligence technology for fresh leads. Mike Prado of HSI described AI- or machine-learning-enhanced images as a starting point for investigation. ABC (Australia) reports that the operation used Clearview AI's facial recognition alongside other techniques, citing Queensland Police analyst Scott Anderson. Forbes reports that officials did not confirm Clearview or any specific program. The ICE release itself does not mention AI.", "replication": { "produced_by": [ "Large holdings of unidentified abuse-material series concentrated for a time-boxed multi-agency review", "Facial recognition against web-scraped reference images plus AI image enhancement feeding human investigators", "Lead dissemination through Interpol's ICSE database and partner agencies" ], "must_hold": [ "Legal authority to use web-scraped facial recognition (banned for this use in Australia, per ABC)", "Human investigators to verify tentative identifications before action", "Partner agencies able to act on leads" ], "echoes": [ { "date": "2024-03-14", "setting": "Second US-based international victim identification surge reported by HSToday (headline only)", "outcome": "unknown", "source": "https://www.hstoday.us/subject-matter-areas/customs-immigration/hsi-partners-conduct-us-based-international-victim-identification-surge/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0317", "case_url": "records/AIBD-0317.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "The risk models are built to score payments and e-banking sessions against customer and bank profiles in real time and block fraudulent transfers. Each case is that intended use." }, { "label": "AI contribution", "value": "The evidence is limited to the maker's account. Vyntra's page attributes each block to its AI risk models detecting variables outside the customer's or bank's usual profile. No customer, bank, date or official record is available to assess whether the models, rather than staff or other controls, produced the outcome. The AI's contribution is not established." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0317", "title": "Vyntra's (formerly NetGuardians) AI risk models may have blocked fraudulent payments at unnamed banks, including a USD 500,000 ATM scheme", "summary": "Vendor “Fraud Cases” page lists blocked payments from CHF 19,990 to USD 850,000 and a USD 500,000 (KSh 58 million) ATM fraud ending in arrests; customers, banks and dates are not given.", "date_observed": "2022-04-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Vyntra's “Fraud Cases” page was published 4 April 2022 per its page metadata; the cases themselves are undated. It states that “Our solutions stop the fraud before the money leaves the bank” and lists about two dozen short case studies at unnamed banks. One is an account takeover after pharming stopped before a CHF 19,990 payment. Another is an impersonation that led a bank employee to validate a CHF 1,000,000 transaction, blocked because the beneficiary was unusual at bank level. There are vishing, tech-support, CEO-fraud, business-email-compromise, romance and invoice-redirection cases. Their payments of EUR 22,114, EUR 33,170, USD 7,500, GBP 21,000, EUR 125,000, EUR 170,000, USD 100,000, USD 170,000 and USD 850,000 were stopped. Transaction or e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language) did not match the customer's or bank's usual profile. There are two internal-fraud cases. In one case “fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs”. The models had flagged unusual timing and frequency of withdrawals. One entry still names “NetGuardians solution”, the Swiss company that merged with Belgium's Intix to form Vyntra in June 2025 (FinTech Global, 19 June 2025). No customer, bank, country, date, regulator or court record is given for any case, and none was found. The page is the vendor's own account.", "category": "harm prevented", "unexpected": false, "purpose": "The risk models are built to score payments and e-banking sessions against customer and bank profiles in real time and block fraudulent transfers. Each case is that intended use.", "system": "Vyntra (NetGuardians) AI risk models for real-time payment and e-banking fraud detection", "model_version": null, "developer": "Vyntra (NetGuardians SA, merged with Intix in June 2025)", "deployer": "Unnamed client banks", "location": "Not stated; amounts are given in Swiss francs, euros, pounds, US dollars and Kenyan shillings", "domain": "Banking fraud prevention", "segment": "Retail and corporate payment fraud at client banks", "benefit_type": "Reduced Risk", "taxonomy_note": "The blocked payments and the ATM fraud are described only on the vendor's page, with no named bank, customer or date.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "The vendor describes customers whose social-engineering transfers of EUR 125,000 and USD 170,000 were blocked, a loss averted for each. No customer is named or independently documented." }, { "group": "organizational", "depth": 4, "reason": "The vendor states that a USD 500,000 (KSh 58 million) ATM fraud was stopped with arrests. It says payments up to USD 850,000 were blocked before funds left client banks. Serious losses averted if the account holds. Customers, banks and dates are not given." }, { "group": "societal", "depth": null, "reason": "No aggregate verified by a regulator is given." } ], "ai_role": { "status": "unassessed", "statement": "The evidence is limited to the maker's account. Vyntra's page attributes each block to its AI risk models detecting variables outside the customer's or bank's usual profile. No customer, bank, date or official record is available to assess whether the models, rather than staff or other controls, produced the outcome. The AI's contribution is not established." }, "evidence": [ { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases. vyntra.com (vendor case-study page; page metadata gives 4 April 2022; cases undated).", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "Vyntra's AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals." }, { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases, same page, ATM case.", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs." }, { "kind": "news report", "citation": "FinTech Global (2025). 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the June 2025 merger of NetGuardians and Intix into Vyntra concerns the vendor, not the cases." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0317" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "The blocked payments and the ATM fraud are described only on the vendor's page, with no named bank, customer or date." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Vyntra's “Fraud Cases” page was published 4 April 2022 per its page metadata; the cases themselves are undated. It states that “Our solutions stop the fraud before the money leaves the bank” and lists about two dozen short case studies at unnamed banks. One is an account takeover after pharming stopped before a CHF 19,990 payment. Another is an impersonation that led a bank employee to validate a CHF 1,000,000 transaction, blocked because the beneficiary was unusual at bank level. There are vishing, tech-support, CEO-fraud, business-email-compromise, romance and invoice-redirection cases. Their payments of EUR 22,114, EUR 33,170, USD 7,500, GBP 21,000, EUR 125,000, EUR 170,000, USD 100,000, USD 170,000 and USD 850,000 were stopped. Transaction or e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language) did not match the customer's or bank's usual profile. There are two internal-fraud cases. In one case “fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs”. The models had flagged unusual timing and frequency of withdrawals. One entry still names “NetGuardians solution”, the Swiss company that merged with Belgium's Intix to form Vyntra in June 2025 (FinTech Global, 19 June 2025). No customer, bank, country, date, regulator or court record is given for any case, and none was found. The page is the vendor's own account.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The vendor describes customers whose social-engineering transfers of EUR 125,000 and USD 170,000 were blocked, a loss averted for each. No customer is named or independently documented." }, { "group": "organizational", "depth": 4, "reason": "The vendor states that a USD 500,000 (KSh 58 million) ATM fraud was stopped with arrests. It says payments up to USD 850,000 were blocked before funds left client banks. Serious losses averted if the account holds. Customers, banks and dates are not given." }, { "group": "societal", "reason": "No aggregate verified by a regulator is given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The risk models are built to score payments and e-banking sessions against customer and bank profiles in real time and block fraudulent transfers. Each case is that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Not stated; amounts are given in Swiss francs, euros, pounds, US dollars and Kenyan shillings", "developer_country": "Switzerland" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The risk models are built to score payments and e-banking sessions against customer and bank profiles in real time and block fraudulent transfers. Each case is that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-04-04", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 3 for personal and 4 for organizational on the documented outcome, with the reason stated on each rating.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://www.vyntra.com/fraud-cases/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.vyntra.com/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Behavioral profiles of each customer's and the bank's usual transaction and e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language)", "Real-time scoring before a payment is released" ], "must_hold": [ "The fraudulent transaction departs from the profile on several variables; the vendor notes cases with little customer history relied on bank-level anomalies", "The bank acts on the alert before the money leaves" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 3 for personal and 4 for organizational on the documented outcome, with the reason stated on each rating.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://www.vyntra.com/fraud-cases/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.vyntra.com/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Banking fraud prevention", "segment": "Retail and corporate payment fraud at client banks" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases. vyntra.com (vendor case-study page; page metadata gives 4 April 2022; cases undated).", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "Vyntra's AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals." }, { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases, same page, ATM case.", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs." }, { "kind": "news report", "citation": "FinTech Global (2025). Vyntra launched by NetGuardians and Intix to boost banking intelligence. 19 June 2025.", "title": "Vyntra launched by NetGuardians and Intix to boost banking intelligence", "url": "https://fintech.global/2025/06/19/vyntra-launched-by-netguardians-and-intix-to-boost-banking-intelligence/", "date": "2025-06-19", "written_by": "independent", "quote": "NetGuardians brings its advanced artificial intelligence tools to detect and prevent financial crime, while Intix contributes its expertise in transaction data analysis and visibility." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0317-source-0001", "AIBD-0317-source-0002", "AIBD-0317-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases. vyntra.com (vendor case-study page; page metadata gives 4 April 2022; cases undated).", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "Vyntra's AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals." }, { "kind": "press release", "citation": "Vyntra (2022). Fraud Cases, same page, ATM case.", "title": "Fraud Cases - Vyntra", "url": "https://www.vyntra.com/fraud-cases/", "date": "2022-04-04", "written_by": "makers", "quote": "fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs." }, { "kind": "news report", "citation": "FinTech Global (2025). Vyntra launched by NetGuardians and Intix to boost banking intelligence. 19 June 2025.", "title": "Vyntra launched by NetGuardians and Intix to boost banking intelligence", "url": "https://fintech.global/2025/06/19/vyntra-launched-by-netguardians-and-intix-to-boost-banking-intelligence/", "date": "2025-06-19", "written_by": "independent", "quote": "NetGuardians brings its advanced artificial intelligence tools to detect and prevent financial crime, while Intix contributes its expertise in transaction data analysis and visibility." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Vendor “Fraud Cases” page lists blocked payments from CHF 19,990 to USD 850,000 and a USD 500,000 (KSh 58 million) ATM fraud ending in arrests; customers, banks and dates are not given.", "account": "Vyntra's “Fraud Cases” page was published 4 April 2022 per its page metadata; the cases themselves are undated. It states that “Our solutions stop the fraud before the money leaves the bank” and lists about two dozen short case studies at unnamed banks. One is an account takeover after pharming stopped before a CHF 19,990 payment. Another is an impersonation that led a bank employee to validate a CHF 1,000,000 transaction, blocked because the beneficiary was unusual at bank level. There are vishing, tech-support, CEO-fraud, business-email-compromise, romance and invoice-redirection cases. Their payments of EUR 22,114, EUR 33,170, USD 7,500, GBP 21,000, EUR 125,000, EUR 170,000, USD 100,000, USD 170,000 and USD 850,000 were stopped. Transaction or e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language) did not match the customer's or bank's usual profile. There are two internal-fraud cases. In one case “fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs”. The models had flagged unusual timing and frequency of withdrawals. One entry still names “NetGuardians solution”, the Swiss company that merged with Belgium's Intix to form Vyntra in June 2025 (FinTech Global, 19 June 2025). No customer, bank, country, date, regulator or court record is given for any case, and none was found. The page is the vendor's own account." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Vyntra (NetGuardians) AI risk models for real-time payment and e-banking fraud detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Vyntra (NetGuardians SA, merged with Intix in June 2025)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Vyntra's (formerly NetGuardians) AI risk models may have blocked fraudulent payments at unnamed banks, including a USD 500,000 ATM scheme", "headline": "Vyntra's AI risk models may have stopped a USD 500,000 ATM fraud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0242cb63702bd620f893f6be7eb51974757fa5552e2abf436018f8573cdcbf56" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Vyntra's “Fraud Cases” page was published 4 April 2022 per its page metadata; the cases themselves are undated. It states that “Our solutions stop the fraud before the money leaves the bank” and lists about two dozen short case studies at unnamed banks. One is an account takeover after pharming stopped before a CHF 19,990 payment. Another is an impersonation that led a bank employee to validate a CHF 1,000,000 transaction, blocked because the beneficiary was unusual at bank level. There are vishing, tech-support, CEO-fraud, business-email-compromise, romance and invoice-redirection cases. Their payments of EUR 22,114, EUR 33,170, USD 7,500, GBP 21,000, EUR 125,000, EUR 170,000, USD 100,000, USD 170,000 and USD 850,000 were stopped. Transaction or e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language) did not match the customer's or bank's usual profile. There are two internal-fraud cases. In one case “fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank's ATMs”. The models had flagged unusual timing and frequency of withdrawals. One entry still names “NetGuardians solution”, the Swiss company that merged with Belgium's Intix to form Vyntra in June 2025 (FinTech Global, 19 June 2025). No customer, bank, country, date, regulator or court record is given for any case, and none was found. The page is the vendor's own account.", "replication": { "produced_by": [ "Behavioral profiles of each customer's and the bank's usual transaction and e-banking session variables (beneficiary, country, amount, currency, browser, screen resolution, language)", "Real-time scoring before a payment is released" ], "must_hold": [ "The fraudulent transaction departs from the profile on several variables; the vendor notes cases with little customer history relied on bank-level anomalies", "The bank acts on the alert before the money leaves" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0112", "case_url": "records/AIBD-0112.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "ASENA (Analiz Sistemleri Narkotik Ağı) is the Turkish National Police's narcotics analysis software, in service since March 2021. Built to flag and investigate drug crime from integrated police and justice databases. The Hakkari flag was worked as a suspected drug shipment and the search found explosives instead, so this benefit was outside the system's design purpose." }, { "label": "AI contribution", "value": "The program supplied the lead and nothing more. The minister said the analysis programs, “what they call artificial intelligence”, had identified a suspicious vehicle thought to be carrying drugs. The Hakkari police chief called him on the evening of 20 March. Officers then stopped the car at a fixed checkpoint, searched the concealed compartment and arrested the driver; prosecutors and the court did the rest. No source describes the program's inputs, the data it matched, or whether an analyst reviewed the flag before the stop. The defendant spoke in court of “those who informed”, but no source reports an informant." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0112", "title": "ASENA analysis program flags a car at Yüksekova carrying 52.5 kg of explosives bound for Istanbul", "summary": "Police analysis software flagged a car at Yüksekova on 20 March 2022; it held 52.5 kg of explosives officials said were bound for Istanbul.", "date_observed": "2022-03-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 20 March 2022 at about 19:00, officers of the Hakkari Provincial Police Directorate stopped a car at the Yüksekova-Van checkpoint exit in Yüksekova, Turkey. Work on the directorate's ASENA analysis program had flagged it as suspicious, initially as a suspected drug shipment. A search of a hidden compartment under the spare-wheel well found 133 packets, 52.5 kg, of factory-made explosive assessed as PETN-RDX. The driver, A.K., a police officer posted in Istanbul, was remanded on 22 March 2022. Interior Minister Süleyman Soylu said on 21 March that the target was Istanbul. On 20 November 2023 the Hakkari 2nd Heavy Penal Court gave A.K. 21 years for supplying weapons to a terrorist organization. Four co-defendants got 17.5 years each.", "category": "harm prevented", "unexpected": true, "purpose": "ASENA (Analiz Sistemleri Narkotik Ağı) is the Turkish National Police's narcotics analysis software, in service since March 2021. Built to flag and investigate drug crime from integrated police and justice databases. The Hakkari flag was worked as a suspected drug shipment and the search found explosives instead, so this benefit was outside the system's design purpose.", "system": "ASENA (Analiz Sistemleri Narkotik Ağı), Turkish National Police narcotics analysis software", "model_version": null, "developer": "Turkish National Police (Emniyet Genel Müdürlüğü), Narcotics Crimes Department; described as written by Turkish engineers, no contractor named", "deployer": "Hakkari Provincial Police Directorate, Turkish National Police", "location": "Yüksekova, Turkey", "domain": "Public safety and counter-terrorism policing", "segment": "Government, security, and defense", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2022-03-21-22fd" }, "ratings": [ { "group": "societal", "depth": 4, "reason": "52.5 kg of explosive assessed as PETN-RDX was seized before use. The minister said the target was Istanbul and the court convicted five men of supplying weapons to a terrorist organization. So the averted harm was potentially mass-casualty, though the specific plan was not established in the reporting." }, { "group": "organizational", "depth": 2, "reason": "The Hakkari police obtained a lead that produced a seizure, five arrests and five convictions. The minister counted it among 15 similar actions stopped that year." } ], "ai_role": { "status": "assessed", "statement": "The program supplied the lead and nothing more. The minister said the analysis programs, “what they call artificial intelligence”, had identified a suspicious vehicle thought to be carrying drugs. The Hakkari police chief called him on the evening of 20 March. Officers then stopped the car at a fixed checkpoint, searched the concealed compartment and arrested the driver; prosecutors and the court did the rest. No source describes the program's inputs, the data it matched, or whether an analyst reviewed the flag before the stop. The defendant spoke in court of “those who informed”, but no source reports an informant." }, "evidence": [ { "kind": "news report", "citation": "Cumhuriyet (2022). Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı. Cumhuriyet.", "title": "Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı", "url": "https://www.cumhuriyet.com.tr/siyaset/goruntuler-ortaya-cikti-aracinda-52-kilo-patlayici-cikan-polis-tutuklandi-1918432", "date": "2022-03-22", "written_by": "independent", "quote": "20 Mart 2022 günü saat 19.00 sıralarında Yüksekova ilçesi Yüksekova-Van Uygulama Noktası çıkışında bir araç durdurulduğu belirtildi" }, { "kind": "news report", "citation": "Hürriyet (2022). Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du. Hürriyet.", "title": "Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du", "url": "https://www.hurriyet.com.tr/gundem/son-dakika-bakan-soyludan-onemli-aciklamalar-42027018", "date": "2022-03-21", "written_by": "independent", "quote": "Bu yapay zeka dedikleri ve birçok bilgiyi üst üste koyarak oluşturdukları programlar. Buradan şüpheli bir aracı tespit ettiklerini söylediler. Uyuşturucu sevkiyatı olduğunu söylediler." }, { "kind": "news report", "citation": "Son Dakika (2022). Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du. Sondakika.com.", "title": "Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du", "url": "https://www.sondakika.com/haber/haber-polis-memurunun-52-kilo-patlayiciyla-yakalanmasi-14811802", "date": "2022-03-21", "written_by": "independent", "quote": "Hakkari İl Emniyet Müdürlüğünce Asena analiz programı üzerinden yapılan çalışmalar neticesinde, bir polis memurunun sürücülüğünü yaptığı araçta 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirilmişti." }, { "kind": "news report", "citation": "NTV (2022). Polis memuru 52,5 kilo patlayıcıyla yakalandı. NTV Haber.", "title": "Polis memuru 52,5 kilo patlayıcıyla yakalandı", "url": "https://www.ntv.com.tr/turkiye/polis-memuru-52-5-kilo-patlayiciyla-yakalandi,FnN59Nq0G0GEzin9tpURRQ", "date": "2022-03-21", "written_by": "independent", "quote": "stepne havuzunun alt kısmına yapılmış zula içerisine gizlenmiş vaziyette 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirildi" }, { "kind": "news report", "citation": "Duvar English (2022). Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari. Duvar English.", "title": "Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari", "url": "https://www.duvarenglish.com/opposition-mp-ahmet-sik-inquires-about-police-officer-caught-with-52kg-of-explosives-in-hakkari-news-60703", "date": "2022-03-23", "written_by": "independent", "quote": "The police found 133 pieces of 52 kilograms and 500 grams of explosives in the spare wheel carrier of the car." }, { "kind": "news report", "citation": "Anadolu Ajansı, Bulur, S. (2022). Yapay zeka ASENA uyuşturucunun izini sürüyor. Anadolu Ajansı.", "title": "Yapay zeka ASENA uyuşturucunun izini sürüyor", "url": "https://www.aa.com.tr/tr/gundem/yapay-zeka-asena-uyusturucunun-izini-suruyor/2603769", "date": "2022-06-02", "written_by": "independent", "quote": "e-Devlet ve UYAP ile İçişleri ve Adalet bakanlıklarının veri tabanlarıyla entegre çalışabilen yapay zeka formatında bir yazılım olan ASENA, yaklaşık 14 ay önce faaliyete geçirildi" }, { "kind": "news report", "citation": "Demirören Haber Ajansı, Özkan, M. (2023). 52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun. DHA.", "title": "52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun", "url": "https://www.dha.com.tr/yerel-haberler/hakkari/52-kilo-patlayiciyla-yakalanan-polis-memuru-ih-2208374", "date": "2023-02-17", "written_by": "independent", "quote": "Hakkari'de İl Emniyet Müdürlüğü'nce Asena analiz programı üzerinden yapılan çalışmalar ile İstanbul'da görev yapan polis memurunun kullandığı otomobil, 20 Mart'ta Yüksekova çıkışındaki polis kontrol noktasında durduruldu." }, { "kind": "news report", "citation": "Kısa Dalga, from DHA (2023). Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası. Kısa Dalga.", "title": "Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası", "url": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "date": "2023-11-20", "written_by": "independent", "quote": "Savunmaları dinleyen mahkeme heyeti, \"terör örgütüne silah sağlamak\" suçundan polis memuru A.K. için 21 yıl, diğer 4 sanık için de 17 yıl 6'şar ay hapis cezası verdi." } ], "replication": { "produced_by": [ "A police analysis program integrated with national identity, justice and interior databases that ranks vehicles or persons for interdiction", "A fixed checkpoint on the Yüksekova-Van road at which a flagged car could be stopped and searched the same evening", "Officers prepared to search a concealed compartment on a narcotics lead rather than a terrorism lead" ], "must_hold": [ "The flag must be followed by a lawful stop and physical search; the software only prioritizes", "Seized material must be forensically assessed and the case prosecuted, as here with a 2023 conviction, for the counterfactual to stand", "Analytic leads must be checked before a stop; the sources do not report the program's false-positive rate" ], "echoes": [] }, "paired_incident": null, "headline": "ASENA flags a car at Yüksekova carrying 52.5 kg of explosives", "metric": "133 packets, 52.5 kg, of factory-made explosive", "comparator": null, "later": { "status": "litigated", "date": "2023-11-20", "note": "On 20 November 2023 the Hakkari 2nd Heavy Penal Court sentenced the police officer A.K. to 21 years in prison and the four co-defendants to 17 years 6 months each.", "quote": "Sanık polis A.K. 21 yıl, diğer 4 sanık ise 17 yıl 6'şar ay hapis cezasına çarptırıldı.", "url": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842" }, "value": null, "developer_country": "Turkey", "deployer_country": "Turkey", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-03-21-22fd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey); https://en.wikipedia.org/wiki/Hakk%C3%A2ri_Province", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: eight independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0112", "catalog_version": "1.0.0", "source_fingerprint": "00409ff3142ba7d3d534b52a19f5ab154a50c3cad385e94162da4361e0c4870a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0112-source-0001", "AIBD-0112-source-0002", "AIBD-0112-source-0003", "AIBD-0112-source-0004", "AIBD-0112-source-0005", "AIBD-0112-source-0006", "AIBD-0112-source-0007", "AIBD-0112-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Turkish National Police (Emniyet Genel Müdürlüğü), Narcotics Crimes Department; described as written by Turkish engineers, no contractor named", "deployer": "Hakkari Provincial Police Directorate, Turkish National Police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The program supplied the lead and nothing more. The minister said the analysis programs, “what they call artificial intelligence”, had identified a suspicious vehicle thought to be carrying drugs. The Hakkari police chief called him on the evening of 20 March. Officers then stopped the car at a fixed checkpoint, searched the concealed compartment and arrested the driver; prosecutors and the court did the rest. No source describes the program's inputs, the data it matched, or whether an analyst reviewed the flag before the stop. The defendant spoke in court of “those who informed”, but no source reports an informant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The program supplied the lead and nothing more. The minister said the analysis programs, “what they call artificial intelligence”, had identified a suspicious vehicle thought to be carrying drugs. The Hakkari police chief called him on the evening of 20 March. Officers then stopped the car at a fixed checkpoint, searched the concealed compartment and arrested the driver; prosecutors and the court did the rest. No source describes the program's inputs, the data it matched, or whether an analyst reviewed the flag before the stop. The defendant spoke in court of “those who informed”, but no source reports an informant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "52.5 kg of explosive assessed as PETN-RDX was seized before use. The minister said the target was Istanbul and the court convicted five men of supplying weapons to a terrorist organization. So the averted harm was potentially mass-casualty, though the specific plan was not established in the reporting." }, { "group": "organizational", "depth": 2, "reason": "The Hakkari police obtained a lead that produced a seizure, five arrests and five convictions. The minister counted it among 15 similar actions stopped that year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: eight independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "52.5 kg of explosive assessed as PETN-RDX was seized before use. The minister said the target was Istanbul and the court convicted five men of supplying weapons to a terrorist organization. So the averted harm was potentially mass-casualty, though the specific plan was not established in the reporting." }, { "group": "organizational", "depth": 2, "reason": "The Hakkari police obtained a lead that produced a seizure, five arrests and five convictions. The minister counted it among 15 similar actions stopped that year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "52.5 kg of explosive assessed as PETN-RDX was seized before use. The minister said the target was Istanbul and the court convicted five men of supplying weapons to a terrorist organization. So the averted harm was potentially mass-casualty, though the specific plan was not established in the reporting." }, { "group": "organizational", "depth": 2, "reason": "The Hakkari police obtained a lead that produced a seizure, five arrests and five convictions. The minister counted it among 15 similar actions stopped that year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "133 packets, 52.5 kg, of factory-made explosive", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2022-03-21-22fd" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2023-11-20", "note": "On 20 November 2023 the Hakkari 2nd Heavy Penal Court sentenced the police officer A.K. to 21 years in prison and the four co-defendants to 17 years 6 months each.", "quote": "Sanık polis A.K. 21 yıl, diğer 4 sanık ise 17 yıl 6'şar ay hapis cezasına çarptırıldı.", "url": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0112" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 20 March 2022 at about 19:00, officers of the Hakkari Provincial Police Directorate stopped a car at the Yüksekova-Van checkpoint exit in Yüksekova, Turkey. Work on the directorate's ASENA analysis program had flagged it as suspicious, initially as a suspected drug shipment. A search of a hidden compartment under the spare-wheel well found 133 packets, 52.5 kg, of factory-made explosive assessed as PETN-RDX. The driver, A.K., a police officer posted in Istanbul, was remanded on 22 March 2022. Interior Minister Süleyman Soylu said on 21 March that the target was Istanbul. On 20 November 2023 the Hakkari 2nd Heavy Penal Court gave A.K. 21 years for supplying weapons to a terrorist organization. Four co-defendants got 17.5 years each.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "52.5 kg of explosive assessed as PETN-RDX was seized before use. The minister said the target was Istanbul and the court convicted five men of supplying weapons to a terrorist organization. So the averted harm was potentially mass-casualty, though the specific plan was not established in the reporting." }, { "group": "organizational", "depth": 2, "reason": "The Hakkari police obtained a lead that produced a seizure, five arrests and five convictions. The minister counted it among 15 similar actions stopped that year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": true, "purpose": "ASENA (Analiz Sistemleri Narkotik Ağı) is the Turkish National Police's narcotics analysis software, in service since March 2021. Built to flag and investigate drug crime from integrated police and justice databases. The Hakkari flag was worked as a suspected drug shipment and the search found explosives instead, so this benefit was outside the system's design purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Yüksekova, Turkey", "developer_country": "Turkey", "deployer_country": "Turkey" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "ASENA (Analiz Sistemleri Narkotik Ağı) is the Turkish National Police's narcotics analysis software, in service since March 2021. Built to flag and investigate drug crime from integrated police and justice databases. The Hakkari flag was worked as a suspected drug shipment and the search found explosives instead, so this benefit was outside the system's design purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-03-20", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-03-21-22fd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey); https://en.wikipedia.org/wiki/Hakk%C3%A2ri_Province", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A police analysis program integrated with national identity, justice and interior databases that ranks vehicles or persons for interdiction", "A fixed checkpoint on the Yüksekova-Van road at which a flagged car could be stopped and searched the same evening", "Officers prepared to search a concealed compartment on a narcotics lead rather than a terrorism lead" ], "must_hold": [ "The flag must be followed by a lawful stop and physical search; the software only prioritizes", "Seized material must be forensically assessed and the case prosecuted, as here with a 2023 conviction, for the counterfactual to stand", "Analytic leads must be checked before a stop; the sources do not report the program's false-positive rate" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-03-21-22fd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey); https://en.wikipedia.org/wiki/Hakk%C3%A2ri_Province", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public safety and counter-terrorism policing", "segment": "Government, security, and defense" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Cumhuriyet (2022). Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı. Cumhuriyet.", "title": "Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı", "url": "https://www.cumhuriyet.com.tr/siyaset/goruntuler-ortaya-cikti-aracinda-52-kilo-patlayici-cikan-polis-tutuklandi-1918432", "date": "2022-03-22", "written_by": "independent", "quote": "20 Mart 2022 günü saat 19.00 sıralarında Yüksekova ilçesi Yüksekova-Van Uygulama Noktası çıkışında bir araç durdurulduğu belirtildi" }, { "kind": "news report", "citation": "Hürriyet (2022). Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du. Hürriyet.", "title": "Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du", "url": "https://www.hurriyet.com.tr/gundem/son-dakika-bakan-soyludan-onemli-aciklamalar-42027018", "date": "2022-03-21", "written_by": "independent", "quote": "Bu yapay zeka dedikleri ve birçok bilgiyi üst üste koyarak oluşturdukları programlar. Buradan şüpheli bir aracı tespit ettiklerini söylediler. Uyuşturucu sevkiyatı olduğunu söylediler." }, { "kind": "news report", "citation": "Son Dakika (2022). Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du. Sondakika.com.", "title": "Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du", "url": "https://www.sondakika.com/haber/haber-polis-memurunun-52-kilo-patlayiciyla-yakalanmasi-14811802", "date": "2022-03-21", "written_by": "independent", "quote": "Hakkari İl Emniyet Müdürlüğünce Asena analiz programı üzerinden yapılan çalışmalar neticesinde, bir polis memurunun sürücülüğünü yaptığı araçta 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirilmişti." }, { "kind": "news report", "citation": "NTV (2022). Polis memuru 52,5 kilo patlayıcıyla yakalandı. NTV Haber.", "title": "Polis memuru 52,5 kilo patlayıcıyla yakalandı", "url": "https://www.ntv.com.tr/turkiye/polis-memuru-52-5-kilo-patlayiciyla-yakalandi,FnN59Nq0G0GEzin9tpURRQ", "date": "2022-03-21", "written_by": "independent", "quote": "stepne havuzunun alt kısmına yapılmış zula içerisine gizlenmiş vaziyette 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirildi" }, { "kind": "news report", "citation": "Duvar English (2022). Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari. Duvar English.", "title": "Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari", "url": "https://www.duvarenglish.com/opposition-mp-ahmet-sik-inquires-about-police-officer-caught-with-52kg-of-explosives-in-hakkari-news-60703", "date": "2022-03-23", "written_by": "independent", "quote": "The police found 133 pieces of 52 kilograms and 500 grams of explosives in the spare wheel carrier of the car." }, { "kind": "news report", "citation": "Anadolu Ajansı, Bulur, S. (2022). Yapay zeka ASENA uyuşturucunun izini sürüyor. Anadolu Ajansı.", "title": "Yapay zeka ASENA uyuşturucunun izini sürüyor", "url": "https://www.aa.com.tr/tr/gundem/yapay-zeka-asena-uyusturucunun-izini-suruyor/2603769", "date": "2022-06-02", "written_by": "independent", "quote": "e-Devlet ve UYAP ile İçişleri ve Adalet bakanlıklarının veri tabanlarıyla entegre çalışabilen yapay zeka formatında bir yazılım olan ASENA, yaklaşık 14 ay önce faaliyete geçirildi" }, { "kind": "news report", "citation": "Demirören Haber Ajansı, Özkan, M. (2023). 52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun. DHA.", "title": "52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun", "url": "https://www.dha.com.tr/yerel-haberler/hakkari/52-kilo-patlayiciyla-yakalanan-polis-memuru-ih-2208374", "date": "2023-02-17", "written_by": "independent", "quote": "Hakkari'de İl Emniyet Müdürlüğü'nce Asena analiz programı üzerinden yapılan çalışmalar ile İstanbul'da görev yapan polis memurunun kullandığı otomobil, 20 Mart'ta Yüksekova çıkışındaki polis kontrol noktasında durduruldu." }, { "kind": "news report", "citation": "Kısa Dalga, from DHA (2023). Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası. Kısa Dalga.", "title": "Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası", "url": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "date": "2023-11-20", "written_by": "independent", "quote": "Savunmaları dinleyen mahkeme heyeti, \"terör örgütüne silah sağlamak\" suçundan polis memuru A.K. için 21 yıl, diğer 4 sanık için de 17 yıl 6'şar ay hapis cezası verdi." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0112-source-0001", "AIBD-0112-source-0002", "AIBD-0112-source-0003", "AIBD-0112-source-0004", "AIBD-0112-source-0005", "AIBD-0112-source-0006", "AIBD-0112-source-0007", "AIBD-0112-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Cumhuriyet (2022). Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı. Cumhuriyet.", "title": "Görüntüler ortaya çıktı: Aracında 52 kilo patlayıcı çıkan polis tutuklandı, Valilik açıklama yaptı", "url": "https://www.cumhuriyet.com.tr/siyaset/goruntuler-ortaya-cikti-aracinda-52-kilo-patlayici-cikan-polis-tutuklandi-1918432", "date": "2022-03-22", "written_by": "independent", "quote": "20 Mart 2022 günü saat 19.00 sıralarında Yüksekova ilçesi Yüksekova-Van Uygulama Noktası çıkışında bir araç durdurulduğu belirtildi" }, { "kind": "news report", "citation": "Hürriyet (2022). Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du. Hürriyet.", "title": "Bakan Soylu'dan polis aracında bulunan patlayıcıyla ilgili flaş açıklama: Hedef İstanbul'du", "url": "https://www.hurriyet.com.tr/gundem/son-dakika-bakan-soyludan-onemli-aciklamalar-42027018", "date": "2022-03-21", "written_by": "independent", "quote": "Bu yapay zeka dedikleri ve birçok bilgiyi üst üste koyarak oluşturdukları programlar. Buradan şüpheli bir aracı tespit ettiklerini söylediler. Uyuşturucu sevkiyatı olduğunu söylediler." }, { "kind": "news report", "citation": "Son Dakika (2022). Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du. Sondakika.com.", "title": "Polis memurunun 52 kilo patlayıcıyla yakalanması hakkında konuşan Bakan Soylu: Hedef İstanbul'du", "url": "https://www.sondakika.com/haber/haber-polis-memurunun-52-kilo-patlayiciyla-yakalanmasi-14811802", "date": "2022-03-21", "written_by": "independent", "quote": "Hakkari İl Emniyet Müdürlüğünce Asena analiz programı üzerinden yapılan çalışmalar neticesinde, bir polis memurunun sürücülüğünü yaptığı araçta 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirilmişti." }, { "kind": "news report", "citation": "NTV (2022). Polis memuru 52,5 kilo patlayıcıyla yakalandı. NTV Haber.", "title": "Polis memuru 52,5 kilo patlayıcıyla yakalandı", "url": "https://www.ntv.com.tr/turkiye/polis-memuru-52-5-kilo-patlayiciyla-yakalandi,FnN59Nq0G0GEzin9tpURRQ", "date": "2022-03-21", "written_by": "independent", "quote": "stepne havuzunun alt kısmına yapılmış zula içerisine gizlenmiş vaziyette 133 parça halinde 52 kilo 500 gram patlayıcı madde ele geçirildi" }, { "kind": "news report", "citation": "Duvar English (2022). Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari. Duvar English.", "title": "Opposition MP Ahmet Şık inquires about police officer caught with 52kg of explosives in Hakkari", "url": "https://www.duvarenglish.com/opposition-mp-ahmet-sik-inquires-about-police-officer-caught-with-52kg-of-explosives-in-hakkari-news-60703", "date": "2022-03-23", "written_by": "independent", "quote": "The police found 133 pieces of 52 kilograms and 500 grams of explosives in the spare wheel carrier of the car." }, { "kind": "news report", "citation": "Anadolu Ajansı, Bulur, S. (2022). Yapay zeka ASENA uyuşturucunun izini sürüyor. Anadolu Ajansı.", "title": "Yapay zeka ASENA uyuşturucunun izini sürüyor", "url": "https://www.aa.com.tr/tr/gundem/yapay-zeka-asena-uyusturucunun-izini-suruyor/2603769", "date": "2022-06-02", "written_by": "independent", "quote": "e-Devlet ve UYAP ile İçişleri ve Adalet bakanlıklarının veri tabanlarıyla entegre çalışabilen yapay zeka formatında bir yazılım olan ASENA, yaklaşık 14 ay önce faaliyete geçirildi" }, { "kind": "news report", "citation": "Demirören Haber Ajansı, Özkan, M. (2023). 52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun. DHA.", "title": "52 kilo patlayıcıyla yakalanan polis memuru: İhbar edenlerden Allah razı olsun", "url": "https://www.dha.com.tr/yerel-haberler/hakkari/52-kilo-patlayiciyla-yakalanan-polis-memuru-ih-2208374", "date": "2023-02-17", "written_by": "independent", "quote": "Hakkari'de İl Emniyet Müdürlüğü'nce Asena analiz programı üzerinden yapılan çalışmalar ile İstanbul'da görev yapan polis memurunun kullandığı otomobil, 20 Mart'ta Yüksekova çıkışındaki polis kontrol noktasında durduruldu." }, { "kind": "news report", "citation": "Kısa Dalga, from DHA (2023). Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası. Kısa Dalga.", "title": "Aracında 52 kilo patlayıcı bulunan polise 21 yıl hapis cezası", "url": "https://kisadalga.net/haber/detay/aracinda-52-kilo-patlayici-bulunan-polise-21-yil-hapis-cezasi_86842", "date": "2023-11-20", "written_by": "independent", "quote": "Savunmaları dinleyen mahkeme heyeti, \"terör örgütüne silah sağlamak\" suçundan polis memuru A.K. için 21 yıl, diğer 4 sanık için de 17 yıl 6'şar ay hapis cezası verdi." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Police analysis software flagged a car at Yüksekova on 20 March 2022; it held 52.5 kg of explosives officials said were bound for Istanbul.", "account": "On 20 March 2022 at about 19:00, officers of the Hakkari Provincial Police Directorate stopped a car at the Yüksekova-Van checkpoint exit in Yüksekova, Turkey. Work on the directorate's ASENA analysis program had flagged it as suspicious, initially as a suspected drug shipment. A search of a hidden compartment under the spare-wheel well found 133 packets, 52.5 kg, of factory-made explosive assessed as PETN-RDX. The driver, A.K., a police officer posted in Istanbul, was remanded on 22 March 2022. Interior Minister Süleyman Soylu said on 21 March that the target was Istanbul. On 20 November 2023 the Hakkari 2nd Heavy Penal Court gave A.K. 21 years for supplying weapons to a terrorist organization. Four co-defendants got 17.5 years each." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ASENA (Analiz Sistemleri Narkotik Ağı), Turkish National Police narcotics analysis software" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Turkish National Police (Emniyet Genel Müdürlüğü), Narcotics Crimes Department; described as written by Turkish engineers, no contractor named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ASENA analysis program flags a car at Yüksekova carrying 52.5 kg of explosives bound for Istanbul", "headline": "ASENA flags a car at Yüksekova carrying 52.5 kg of explosives" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "00409ff3142ba7d3d534b52a19f5ab154a50c3cad385e94162da4361e0c4870a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 20 March 2022 at about 19:00, officers of the Hakkari Provincial Police Directorate stopped a car at the Yüksekova-Van checkpoint exit in Yüksekova, Turkey. Work on the directorate's ASENA analysis program had flagged it as suspicious, initially as a suspected drug shipment. A search of a hidden compartment under the spare-wheel well found 133 packets, 52.5 kg, of factory-made explosive assessed as PETN-RDX. The driver, A.K., a police officer posted in Istanbul, was remanded on 22 March 2022. Interior Minister Süleyman Soylu said on 21 March that the target was Istanbul. On 20 November 2023 the Hakkari 2nd Heavy Penal Court gave A.K. 21 years for supplying weapons to a terrorist organization. Four co-defendants got 17.5 years each.", "replication": { "produced_by": [ "A police analysis program integrated with national identity, justice and interior databases that ranks vehicles or persons for interdiction", "A fixed checkpoint on the Yüksekova-Van road at which a flagged car could be stopped and searched the same evening", "Officers prepared to search a concealed compartment on a narcotics lead rather than a terrorism lead" ], "must_hold": [ "The flag must be followed by a lawful stop and physical search; the software only prioritizes", "Seized material must be forensically assessed and the case prosecuted, as here with a 2023 conviction, for the counterfactual to stand", "Analytic leads must be checked before a stop; the sources do not report the program's false-positive rate" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0365", "case_url": "records/AIBD-0365.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "The reminder was built to warn WeChat Pay users during risky transactions and connect them to online customer service that helps judge the risk. Stopping a transfer to a suspected scammer was that purpose." }, { "label": "AI contribution", "value": "The automated part the sources describe is the flagging of a risky transaction. That triggers the on-screen reminder and connects the payer to Tencent Cloud Zhifu, a service the Monitor calls AI. ChinaZ says the WeChat Pay customer service judged “from experience” that this was a dating scam, which reads as a human agent. Neither report says how the transaction was flagged or whether any model was involved. The advice, not the flag alone, led Zhang to cancel." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "China" ] }, "record": { "id": "AIBD-0365", "title": "WeChat Pay's “anti-fraud customer service reminder” interrupts a Yunnan man's 190,000 yuan transfer to a suspected romance scammer, and he cancels the payment (14 December 2021)", "summary": "WeChat says its anti-fraud reminder, with Tencent Cloud online service, led a Yunnan man to cancel a 190,000 yuan transfer to a “goddess” he had met online; reported by ChinaZ, 14 December 2021.", "date_observed": "2021-12-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "According to WeChat (ChinaZ, 14 December 2021; ZOL, 16 December 2021), WeChat Pay launched an “anti-fraud customer service reminder”. When a payment is flagged as a risky transaction, the screen shows an “answer the reminder” button and the system connects automatically. Tencent Cloud Zhifu then provides online customer service to help judge the risk. ChinaZ reports that a Mr. Zhang in Yunnan had met a woman on a video platform. Within a week she asked to borrow 190,000 yuan for medical treatment. As he initiated the transfer, a WeChat Pay voice reminder appeared. The customer service, judging from experience that this looked like a dating scam, explained the pattern and advised him to cancel. He avoided a loss of 190,000 yuan. The Monitor bundles this with Shanghai police figures for 2021 (115,000 scam calls blocked daily; 510 million yuan directly recovered). Xinmin attributes those to police, the communications administration and carriers without naming AI.", "category": "harm prevented", "unexpected": false, "purpose": "The reminder was built to warn WeChat Pay users during risky transactions and connect them to online customer service that helps judge the risk. Stopping a transfer to a suspected scammer was that purpose.", "system": "WeChat Pay 'anti-fraud customer service reminder' (防骗客服提醒) with Tencent Cloud Zhifu (腾讯云智服) online customer service", "model_version": null, "developer": "Tencent (WeChat Pay and Tencent Cloud)", "deployer": "WeChat Pay", "location": "Yunnan, China", "domain": "Payments fraud prevention", "segment": "Consumer mobile payments", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2021-12-15-3cd7" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "A 190,000-yuan transfer to a suspected romance scammer was canceled on the reminder, a serious financial loss for one person averted on WeChat's account. The man is identified only by surname and province, no police or bank confirmation is reported, and the case date is not given." } ], "ai_role": { "status": "assessed", "statement": "The automated part the sources describe is the flagging of a risky transaction. That triggers the on-screen reminder and connects the payer to Tencent Cloud Zhifu, a service the Monitor calls AI. ChinaZ says the WeChat Pay customer service judged “from experience” that this was a dating scam, which reads as a human agent. Neither report says how the transaction was flagged or whether any model was involved. The advice, not the flag alone, led Zhang to cancel." }, "evidence": [ { "kind": "news report", "citation": "ChinaZ (2021). 微信支付上线“防骗客服提醒”功能 为一男子保住19万元. 站长之家, 14 December 2021.", "title": "微信支付上线“防骗客服提醒”功能 为一男子保住19万元", "url": "https://www.chinaz.com/2021/1214/1341135.shtml", "date": "2021-12-14", "written_by": "independent", "quote": "凭借经验判断张先生可能陷入了交友诈骗的骗局" }, { "kind": "news report", "citation": "ZOL (2021). 年底了 微信支付正式上线“防骗客服提醒”功能. 中关村在线, 16 December 2021.", "title": "年底了 微信支付正式上线“防骗客服提醒”功能", "url": "https://mobile.zol.com.cn/783/7832465.html", "date": "2021-12-16", "written_by": "independent", "quote": "腾讯云智服提供实时在线客服，协助大家判断当前交易风险" }, { "kind": "news report", "citation": "Yang J (2021). 上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元. 新民晚报 / 新民网, 17 December 2021.", "title": "上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元", "url": "http://newsxmwb.xinmin.cn/shizheng/szt/2021/12/17/32082134.html", "date": "2021-12-17", "written_by": "independent", "quote": "今年以来，日均阻断诈骗电话11.5万通，较去年同比增加6倍" }, { "kind": "registry label", "citation": "OECD.AI (2021). AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud. OECD AI Incidents and Hazards Monitor, entry 2021-12-15-3cd7.", "title": "AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud", "url": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "date": null, "written_by": "independent", "quote": "AI-based risk detection in banks and WeChat Pay helped identify suspects and stop fraudulent transactions, safeguarding users' financial assets." } ], "replication": { "produced_by": [ "A WeChat Pay transfer flagged as a risky transaction. This shows an “answer the reminder” button and connects the payer to Tencent Cloud Zhifu online customer service.", "A customer-service exchange in which the agent asks about the transaction context and recognizes a known scam pattern" ], "must_hold": [ "The flag fires before the transfer completes", "The payer answers the reminder and accepts the advice to cancel", "The scam matches a pattern the service recognizes; here a week-old online acquaintance asking for medical money" ], "echoes": [] }, "paired_incident": null, "headline": "WeChat Pay reminder stops a 190,000 yuan romance-scam transfer", "metric": "avoided a loss of 190,000 yuan", "comparator": null, "later": null, "value": null, "developer_country": "China", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.tencent.com/en-us/about.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0365", "catalog_version": "1.0.0", "source_fingerprint": "b8e436804e34cb9b917c3559cd62e5fc210aaec49a628459e11f18a6d559816a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0365-source-0001", "AIBD-0365-source-0002", "AIBD-0365-source-0003", "AIBD-0365-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Tencent (WeChat Pay and Tencent Cloud)", "deployer": "WeChat Pay" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The automated part the sources describe is the flagging of a risky transaction. That triggers the on-screen reminder and connects the payer to Tencent Cloud Zhifu, a service the Monitor calls AI. ChinaZ says the WeChat Pay customer service judged “from experience” that this was a dating scam, which reads as a human agent. Neither report says how the transaction was flagged or whether any model was involved. The advice, not the flag alone, led Zhang to cancel." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The automated part the sources describe is the flagging of a risky transaction. That triggers the on-screen reminder and connects the payer to Tencent Cloud Zhifu, a service the Monitor calls AI. ChinaZ says the WeChat Pay customer service judged “from experience” that this was a dating scam, which reads as a human agent. Neither report says how the transaction was flagged or whether any model was involved. The advice, not the flag alone, led Zhang to cancel." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "A 190,000-yuan transfer to a suspected romance scammer was canceled on the reminder, a serious financial loss for one person averted on WeChat's account. The man is identified only by surname and province, no police or bank confirmation is reported, and the case date is not given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "A 190,000-yuan transfer to a suspected romance scammer was canceled on the reminder, a serious financial loss for one person averted on WeChat's account. The man is identified only by surname and province, no police or bank confirmation is reported, and the case date is not given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "A 190,000-yuan transfer to a suspected romance scammer was canceled on the reminder, a serious financial loss for one person averted on WeChat's account. The man is identified only by surname and province, no police or bank confirmation is reported, and the case date is not given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "avoided a loss of 190,000 yuan", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2021-12-15-3cd7" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0365" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "According to WeChat (ChinaZ, 14 December 2021; ZOL, 16 December 2021), WeChat Pay launched an “anti-fraud customer service reminder”. When a payment is flagged as a risky transaction, the screen shows an “answer the reminder” button and the system connects automatically. Tencent Cloud Zhifu then provides online customer service to help judge the risk. ChinaZ reports that a Mr. Zhang in Yunnan had met a woman on a video platform. Within a week she asked to borrow 190,000 yuan for medical treatment. As he initiated the transfer, a WeChat Pay voice reminder appeared. The customer service, judging from experience that this looked like a dating scam, explained the pattern and advised him to cancel. He avoided a loss of 190,000 yuan. The Monitor bundles this with Shanghai police figures for 2021 (115,000 scam calls blocked daily; 510 million yuan directly recovered). Xinmin attributes those to police, the communications administration and carriers without naming AI.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 4, "reason": "A 190,000-yuan transfer to a suspected romance scammer was canceled on the reminder, a serious financial loss for one person averted on WeChat's account. The man is identified only by surname and province, no police or bank confirmation is reported, and the case date is not given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The reminder was built to warn WeChat Pay users during risky transactions and connect them to online customer service that helps judge the risk. Stopping a transfer to a suspected scammer was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Yunnan, China", "developer_country": "China" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The reminder was built to warn WeChat Pay users during risky transactions and connect them to online customer service that helps judge the risk. Stopping a transfer to a suspected scammer was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2021-12-14", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.tencent.com/en-us/about.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A WeChat Pay transfer flagged as a risky transaction. This shows an “answer the reminder” button and connects the payer to Tencent Cloud Zhifu online customer service.", "A customer-service exchange in which the agent asks about the transaction context and recognizes a known scam pattern" ], "must_hold": [ "The flag fires before the transfer completes", "The payer answers the reminder and accepts the advice to cancel", "The scam matches a pattern the service recognizes; here a week-old online acquaintance asking for medical money" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.tencent.com/en-us/about.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Payments fraud prevention", "segment": "Consumer mobile payments" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "ChinaZ (2021). 微信支付上线“防骗客服提醒”功能 为一男子保住19万元. 站长之家, 14 December 2021.", "title": "微信支付上线“防骗客服提醒”功能 为一男子保住19万元", "url": "https://www.chinaz.com/2021/1214/1341135.shtml", "date": "2021-12-14", "written_by": "independent", "quote": "凭借经验判断张先生可能陷入了交友诈骗的骗局" }, { "kind": "news report", "citation": "ZOL (2021). 年底了 微信支付正式上线“防骗客服提醒”功能. 中关村在线, 16 December 2021.", "title": "年底了 微信支付正式上线“防骗客服提醒”功能", "url": "https://mobile.zol.com.cn/783/7832465.html", "date": "2021-12-16", "written_by": "independent", "quote": "腾讯云智服提供实时在线客服，协助大家判断当前交易风险" }, { "kind": "news report", "citation": "Yang J (2021). 上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元. 新民晚报 / 新民网, 17 December 2021.", "title": "上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元", "url": "http://newsxmwb.xinmin.cn/shizheng/szt/2021/12/17/32082134.html", "date": "2021-12-17", "written_by": "independent", "quote": "今年以来，日均阻断诈骗电话11.5万通，较去年同比增加6倍" }, { "kind": "registry label", "citation": "OECD.AI (2021). AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud. OECD AI Incidents and Hazards Monitor, entry 2021-12-15-3cd7.", "title": "AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud", "url": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "written_by": "independent", "quote": "AI-based risk detection in banks and WeChat Pay helped identify suspects and stop fraudulent transactions, safeguarding users' financial assets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0365-source-0001", "AIBD-0365-source-0002", "AIBD-0365-source-0003", "AIBD-0365-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "ChinaZ (2021). 微信支付上线“防骗客服提醒”功能 为一男子保住19万元. 站长之家, 14 December 2021.", "title": "微信支付上线“防骗客服提醒”功能 为一男子保住19万元", "url": "https://www.chinaz.com/2021/1214/1341135.shtml", "date": "2021-12-14", "written_by": "independent", "quote": "凭借经验判断张先生可能陷入了交友诈骗的骗局" }, { "kind": "news report", "citation": "ZOL (2021). 年底了 微信支付正式上线“防骗客服提醒”功能. 中关村在线, 16 December 2021.", "title": "年底了 微信支付正式上线“防骗客服提醒”功能", "url": "https://mobile.zol.com.cn/783/7832465.html", "date": "2021-12-16", "written_by": "independent", "quote": "腾讯云智服提供实时在线客服，协助大家判断当前交易风险" }, { "kind": "news report", "citation": "Yang J (2021). 上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元. 新民晚报 / 新民网, 17 December 2021.", "title": "上海日均阻断诈骗电话11.5万通 今年以来直接挽损5.1亿余元", "url": "http://newsxmwb.xinmin.cn/shizheng/szt/2021/12/17/32082134.html", "date": "2021-12-17", "written_by": "independent", "quote": "今年以来，日均阻断诈骗电话11.5万通，较去年同比增加6倍" }, { "kind": "registry label", "citation": "OECD.AI (2021). AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud. OECD AI Incidents and Hazards Monitor, entry 2021-12-15-3cd7.", "title": "AI Systems in Shanghai and Banking Sector Prevent Large-Scale Financial Fraud", "url": "https://oecd.ai/en/incidents/2021-12-15-3cd7", "written_by": "independent", "quote": "AI-based risk detection in banks and WeChat Pay helped identify suspects and stop fraudulent transactions, safeguarding users' financial assets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "WeChat says its anti-fraud reminder, with Tencent Cloud online service, led a Yunnan man to cancel a 190,000 yuan transfer to a “goddess” he had met online; reported by ChinaZ, 14 December 2021.", "account": "According to WeChat (ChinaZ, 14 December 2021; ZOL, 16 December 2021), WeChat Pay launched an “anti-fraud customer service reminder”. When a payment is flagged as a risky transaction, the screen shows an “answer the reminder” button and the system connects automatically. Tencent Cloud Zhifu then provides online customer service to help judge the risk. ChinaZ reports that a Mr. Zhang in Yunnan had met a woman on a video platform. Within a week she asked to borrow 190,000 yuan for medical treatment. As he initiated the transfer, a WeChat Pay voice reminder appeared. The customer service, judging from experience that this looked like a dating scam, explained the pattern and advised him to cancel. He avoided a loss of 190,000 yuan. The Monitor bundles this with Shanghai police figures for 2021 (115,000 scam calls blocked daily; 510 million yuan directly recovered). Xinmin attributes those to police, the communications administration and carriers without naming AI." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "WeChat Pay 'anti-fraud customer service reminder' (防骗客服提醒) with Tencent Cloud Zhifu (腾讯云智服) online customer service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Tencent (WeChat Pay and Tencent Cloud)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "WeChat Pay's “anti-fraud customer service reminder” interrupts a Yunnan man's 190,000 yuan transfer to a suspected romance scammer, and he cancels the payment (14 December 2021)", "headline": "WeChat Pay reminder stops a 190,000 yuan romance-scam transfer" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "b8e436804e34cb9b917c3559cd62e5fc210aaec49a628459e11f18a6d559816a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "According to WeChat (ChinaZ, 14 December 2021; ZOL, 16 December 2021), WeChat Pay launched an “anti-fraud customer service reminder”. When a payment is flagged as a risky transaction, the screen shows an “answer the reminder” button and the system connects automatically. Tencent Cloud Zhifu then provides online customer service to help judge the risk. ChinaZ reports that a Mr. Zhang in Yunnan had met a woman on a video platform. Within a week she asked to borrow 190,000 yuan for medical treatment. As he initiated the transfer, a WeChat Pay voice reminder appeared. The customer service, judging from experience that this looked like a dating scam, explained the pattern and advised him to cancel. He avoided a loss of 190,000 yuan. The Monitor bundles this with Shanghai police figures for 2021 (115,000 scam calls blocked daily; 510 million yuan directly recovered). Xinmin attributes those to police, the communications administration and carriers without naming AI.", "replication": { "produced_by": [ "A WeChat Pay transfer flagged as a risky transaction. This shows an “answer the reminder” button and connects the payer to Tencent Cloud Zhifu online customer service.", "A customer-service exchange in which the agent asks about the transaction context and recognizes a known scam pattern" ], "must_hold": [ "The flag fires before the transfer completes", "The payer answers the reminder and accepts the advice to cancel", "The scam matches a pattern the service recognizes; here a week-old online acquaintance asking for medical money" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0230", "case_url": "records/AIBD-0230.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Help law-enforcement investigators find underage sex-trafficking victims by matching faces and text across online escort advertisements." }, { "label": "AI contribution", "value": "The face match that surfaced the ads is attributed to Amazon Rekognition inside Spotlight. The recovery itself came from conventional police work. Everything about this case rests on Thorn's CEO. Snopes documents that Thorn's aggregate figures derive from user surveys and were widely misreported." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0230", "title": "Thorn's Spotlight matches a missing child's NCMEC photo to online sex ads; she may have been recovered within weeks", "summary": "In April 2019 a California officer ran a NCMEC missing-child photo through Thorn's Spotlight; Rekognition matching returned sex ads with the girl and, per Thorn's CEO, she was recovered within weeks.", "date_observed": "2019-06-19", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The source places the case in April 2019 without a day. Thorn CEO Julie Cordua, speaking at an Amazon conference, was quoted by WIRED's Tom Simonite. She said a California law enforcement officer screenshotted a missing child's picture from a National Center for Missing and Exploited Children post. The officer ran it through Spotlight, Thorn's investigator tool that matches faces and other clues in online sex ads. Using Amazon's facial recognition, Spotlight returned a list of ads featuring the girl, who had been sold for weeks. More traditional police work followed and, she said, the child was recovered within weeks. Snopes notes Thorn's 2017 impact report claimed its tools helped identify 5,791 child victims and rescue 103. The figures are from Thorn's surveys of police users, and viral claims conflated identification with rescue. No police or court record of this case is among the sources.", "category": "benefit created", "unexpected": false, "purpose": "Help law-enforcement investigators find underage sex-trafficking victims by matching faces and text across online escort advertisements.", "system": "Spotlight (Thorn), using Amazon Rekognition", "model_version": null, "developer": "Thorn (with Amazon Rekognition for face matching)", "deployer": "An unnamed California law enforcement officer and agency", "location": null, "domain": "Anti-trafficking / child protection", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": "The recovery is reported only by the maker's CEO, and the AI's documented part was returning the ads.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 4, "reason": "A child sold for weeks was recovered and removed from trauma, per Thorn's CEO. No independent confirmation of the recovery is among the sources." } ], "ai_role": { "status": "assessed", "statement": "The face match that surfaced the ads is attributed to Amazon Rekognition inside Spotlight. The recovery itself came from conventional police work. Everything about this case rests on Thorn's CEO. 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0230", "catalog_version": "1.0.0", "source_fingerprint": "f171181cd195b42057a829879705dc56f1e3a4a602258fc3d7c3279d89dea80a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0230-source-0001", "AIBD-0230-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Thorn (with Amazon Rekognition for face matching)", "deployer": "An unnamed California law enforcement officer and agency" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The face match that surfaced the ads is attributed to Amazon Rekognition inside Spotlight. 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facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Anti-trafficking / child protection", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Tom Simonite, WIRED, 19 Jun 2019", "title": "How Facial Recognition Is Fighting Child Sex Trafficking", "url": "https://www.wired.com/story/how-facial-recognition-fighting-child-sex-trafficking/", "date": "2019-06-19", "written_by": "independent", "quote": "Using Amazon’s facial recognition technology, Spotlight quickly returned a list of online sex ads featuring the girl’s photo." }, { "kind": "other", "citation": "Dan MacGuill, Snopes fact check, 4 Sep 2018", "title": "Did Ashton Kutcher's Software Help Rescue 6,000 Children from Sex Trafficking?", "url": "https://www.snopes.com/fact-check/kutcher-software-child-trafficking/", "date": "2018-09-04", "written_by": "independent", "quote": "As stated by Thorn themselves, the organization's digital tools had helped identify -- not rescue -- almost 6,000 child victims, a distinction that has sometimes been overlooked." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0230-source-0001", "AIBD-0230-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Tom Simonite, WIRED, 19 Jun 2019", "title": "How Facial Recognition Is Fighting Child Sex Trafficking", "url": "https://www.wired.com/story/how-facial-recognition-fighting-child-sex-trafficking/", "date": "2019-06-19", "written_by": "independent", "quote": "Using Amazon’s facial recognition technology, Spotlight quickly returned a list of online sex ads featuring the girl’s photo." }, { "kind": "other", "citation": "Dan MacGuill, Snopes fact check, 4 Sep 2018", "title": "Did Ashton Kutcher's Software Help Rescue 6,000 Children from Sex Trafficking?", "url": "https://www.snopes.com/fact-check/kutcher-software-child-trafficking/", "date": "2018-09-04", "written_by": "independent", "quote": "As stated by Thorn themselves, the organization's digital tools had helped identify -- not rescue -- almost 6,000 child victims, a distinction that has sometimes been overlooked." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "In April 2019 a California officer ran a NCMEC missing-child photo through Thorn's Spotlight; Rekognition matching returned sex ads with the girl and, per Thorn's CEO, she was recovered within weeks.", "account": "The source places the case in April 2019 without a day. Thorn CEO Julie Cordua, speaking at an Amazon conference, was quoted by WIRED's Tom Simonite. She said a California law enforcement officer screenshotted a missing child's picture from a National Center for Missing and Exploited Children post. The officer ran it through Spotlight, Thorn's investigator tool that matches faces and other clues in online sex ads. Using Amazon's facial recognition, Spotlight returned a list of ads featuring the girl, who had been sold for weeks. More traditional police work followed and, she said, the child was recovered within weeks. Snopes notes Thorn's 2017 impact report claimed its tools helped identify 5,791 child victims and rescue 103. The figures are from Thorn's surveys of police users, and viral claims conflated identification with rescue. No police or court record of this case is among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Spotlight (Thorn), using Amazon Rekognition" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Thorn (with Amazon Rekognition for face matching)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Thorn's Spotlight matches a missing child's NCMEC photo to online sex ads; she may have been recovered within weeks", "headline": "Spotlight may have led to a missing child's recovery in weeks" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f171181cd195b42057a829879705dc56f1e3a4a602258fc3d7c3279d89dea80a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The source places the case in April 2019 without a day. Thorn CEO Julie Cordua, speaking at an Amazon conference, was quoted by WIRED's Tom Simonite. She said a California law enforcement officer screenshotted a missing child's picture from a National Center for Missing and Exploited Children post. The officer ran it through Spotlight, Thorn's investigator tool that matches faces and other clues in online sex ads. Using Amazon's facial recognition, Spotlight returned a list of ads featuring the girl, who had been sold for weeks. More traditional police work followed and, she said, the child was recovered within weeks. Snopes notes Thorn's 2017 impact report claimed its tools helped identify 5,791 child victims and rescue 103. The figures are from Thorn's surveys of police users, and viral claims conflated identification with rescue. No police or court record of this case is among the sources.", "replication": { "produced_by": [ "A reference photo of the missing child", "An archive of scraped escort ads with faces indexed for similarity search", "An officer with Spotlight access who follows the matches with investigation" ], "must_hold": [ "Ads with usable face images must exist for the victim", "Legal access to the ad archive and face matching", "Investigative capacity to act on matches quickly" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0239", "case_url": "records/AIBD-0239.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant." }, { "label": "AI contribution", "value": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. The system did not predict a crime or act on its own. The warrants, the arrest and the evidence-gathering were police and court actions. OpenAI sent only the user's prompts, not ChatGPT's replies. What the assistant said to the user, and whether it helped or hindered him, is unknown." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Brazil" ] }, "record": { "id": "AIBD-0239", "title": "OpenAI alert relayed through the FBI leads Espírito Santo police to detain a father a day before his stated date to kill his son", "summary": "OpenAI flagged a user's plan to kill his 8-year-old son; relayed via the FBI, Brazilian police held him a day before his stated date.", "date_observed": "2026-06-19", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "category": "protective response", "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant.", "system": "ChatGPT with OpenAI's safety-monitoring pipeline (automated classifiers plus human review and law-enforcement referral)", "model_version": null, "developer": "OpenAI", "deployer": "OpenAI", "location": "São Gabriel da Palha, Brazil", "domain": "Public safety", "segment": "Law enforcement and child protection", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-09-16-2a33" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ], "ai_role": { "status": "assessed", "statement": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. The system did not predict a crime or act on its own. The warrants, the arrest and the evidence-gathering were police and court actions. OpenAI sent only the user's prompts, not ChatGPT's replies. What the assistant said to the user, and whether it helped or hindered him, is unknown." }, "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ], "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] }, "paired_incident": null, "headline": "OpenAI flag leads Brazilian police to hold a father a day early", "metric": "held him a day before his stated date", "comparator": null, "later": { "status": "litigated", "date": "2026-08-13", "note": "He was released on 13 August 2026 after a court granted habeas corpus, under an ankle monitor and a no-contact order, with the inquiry continuing.", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports, the makers' press release and the beneficiary's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0239", "catalog_version": "1.0.0", "source_fingerprint": "40a6b91f6da11e7b61e17b845b9c4d3689ceaeaf7a17db97c70ddc003eb24a85", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0239-source-0001", "AIBD-0239-source-0002", "AIBD-0239-source-0003", "AIBD-0239-source-0004", "AIBD-0239-source-0005", "AIBD-0239-source-0006", "AIBD-0239-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "OpenAI", "deployer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. The system did not predict a crime or act on its own. The warrants, the arrest and the evidence-gathering were police and court actions. OpenAI sent only the user's prompts, not ChatGPT's replies. What the assistant said to the user, and whether it helped or hindered him, is unknown." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "OpenAI's automated classifiers and human reviewers flagged the user's messages, and the company chose to refer the account to the FBI. The system did not predict a crime or act on its own. The warrants, the arrest and the evidence-gathering were police and court actions. OpenAI sent only the user's prompts, not ChatGPT's replies. What the assistant said to the user, and whether it helped or hindered him, is unknown." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports, the makers' press release and the beneficiary's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. 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He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "held him a day before his stated date", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-09-16-2a33" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2026-08-13", "note": "He was released on 13 August 2026 after a court granted habeas corpus, under an ankle monitor and a no-contact order, with the inquiry continuing.", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0239" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The 8-year-old, and his mother who was placed under a no-contact order, were shielded from a plan the father had dated to 20 June. Police say the platform's information was corroborated when they arrived. He denies intent and no charge has been filed, so a life-saving effect is not established." }, { "group": "organizational", "depth": 2, "reason": "The Polícia Civil do Espírito Santo obtained a lead through the FBI and the Ministry of Justice CyberLab. It says it had no other way to get it. Its cybercrime chief called the alert another path for police work. The Ministry rated it the gravest of three such alerts in Brazil." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "São Gabriel da Palha, Brazil", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "ChatGPT is a general-purpose conversational assistant. OpenAI's moderation layer is built to route conversations that indicate plans to harm others to human reviewers. Those reviewers may refer an imminent threat to law enforcement, under a policy published 26 August 2025. The referral that triggered the arrest was that layer doing what it was built to do, not an emergent use of the assistant." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-19", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-09-16-2a33", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public safety", "segment": "Law enforcement and child protection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0239-source-0001", "AIBD-0239-source-0002", "AIBD-0239-source-0003", "AIBD-0239-source-0004", "AIBD-0239-source-0005", "AIBD-0239-source-0006", "AIBD-0239-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Polícia Civil do Espírito Santo (2026). Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial. pc.es.gov.br.", "title": "Alerta do FBI e do Ministério da Justiça auxiliam PCES na prisão de investigado por ameaças contra o próprio filho em Inteligência Artificial", "url": "https://pc.es.gov.br/Not%C3%ADcia/alerta-do-fbi-e-do-ministerio-da-justica-auxiliam-pces-na-prisao-de-investigado-por-ameacas-contra-o-proprio-filho-em-inteligencia-artificial", "date": "2026-06-26", "written_by": "beneficiary", "quote": "Recebemos a denúncia no dia 16 de junho. Nas conversas analisadas, ele mencionava que os atos ocorreriam no dia 20." }, { "kind": "news report", "citation": "g1 Espírito Santo (2026). Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT. g1.globo.com.", "title": "Pai é preso por planejar a morte do filho para não pagar pensão e confessar crime ao ChatGPT", "url": "https://g1.globo.com/es/espirito-santo/norte-noroeste-es/noticia/2026/06/25/pai-e-preso-por-planejar-a-morte-do-filho-para-nao-pagar-pensao-e-confessar-crime-ao-chatgpt.ghtml", "date": "2026-06-25", "written_by": "independent", "quote": "no último dia 19, um dia antes da data em que o crime aconteceria, segundo o delegado adjunto" }, { "kind": "news report", "citation": "Freitas, C. / A Gazeta (2026). Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES. agazeta.com.br.", "title": "Conversa com IA ajuda polícia a prender pai por planejar morte de filho no ES", "url": "https://www.agazeta.com.br/es/policia/conversa-com-ia-ajuda-policia-a-prender-pai-por-planejar-morte-de-filho-no-es-0626", "date": "2026-06-25", "written_by": "independent", "quote": "Os elementos foram fornecidos pela própria empresa responsável por essa inteligência artificial." }, { "kind": "news report", "citation": "Folha de S.Paulo (2026). Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta. folha.uol.com.br.", "title": "Homem é preso sob suspeita de usar IA para planejar morte de filho de 8 anos; plataforma emitiu alerta", "url": "https://www1.folha.uol.com.br/cotidiano/2026/06/homem-e-preso-sob-suspeita-de-usar-ia-para-planejar-morte-de-filho-de-8-anos-plataforma-emitiu-alerta.shtml", "date": "2026-06-26", "written_by": "independent", "quote": "a OpenAI, desenvolvedora do ChatGPT, foi quem reportou ao FBI que um usuário realizava pesquisas frequentes relacionadas à intenção de matar o próprio filho" }, { "kind": "news report", "citation": "g1 Tecnologia (2026). Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades. g1.globo.com.", "title": "Como ChatGPT identificou plano de pai para matar o filho e levou alerta para autoridades", "url": "https://g1.globo.com/tecnologia/noticia/2026/06/27/como-chatgpt-identificou-plano-de-pai-para-matar-o-filho-e-levou-alerta-para-autoridades.ghtml", "date": "2026-06-27", "written_by": "independent", "quote": "Quando identificamos conversas que indicam um risco iminente e crível de dano a outras pessoas, podemos notificar as autoridades competentes" }, { "kind": "news report", "citation": "BBC News Brasil (2026). Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil? bbc.com/portuguese.", "title": "Ele contou ao ChatGPT que queria matar o filho e foi preso — conversas com IA podem ser prova de crime no Brasil?", "url": "https://www.bbc.com/portuguese/articles/c8dn1l7vr6ro", "date": "2026-08-14", "written_by": "independent", "quote": "Na quinta-feira (13/8), ele foi solto após a Justiça conceder um habeas corpus apresentado pela defesa" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. openai.com.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": "2025-08-26", "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "OpenAI flagged a user's plan to kill his 8-year-old son; relayed via the FBI, Brazilian police held him a day before his stated date.", "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ChatGPT with OpenAI's safety-monitoring pipeline (automated classifiers plus human review and law-enforcement referral)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "OpenAI alert relayed through the FBI leads Espírito Santo police to detain a father a day before his stated date to kill his son", "headline": "OpenAI flag leads Brazilian police to hold a father a day early" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "40a6b91f6da11e7b61e17b845b9c4d3689ceaeaf7a17db97c70ddc003eb24a85" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2026 a 36-year-old farmer in São Gabriel da Palha, Espírito Santo, Brazil, told ChatGPT about a plan to kill his 8-year-old son. He said he had offered R$50,000 to a hitman and set 20 June as the date. OpenAI's safety systems flagged the account, and the company referred it to the FBI. The FBI passed it to the Ministry of Justice's CyberLab. The Polícia Civil received it on 16 June and, under warrants, arrested him on 19 June. Officers seized his phone, four bottles of an unidentified substance and a knotted rope. He admitted the searches but denied intent. He was freed on habeas corpus on 13 August 2026 after 54 days, under an ankle monitor and a no-contact order. The inquiry continues.", "replication": { "produced_by": [ "The user typed explicit, dated threats against an identifiable child into ChatGPT over about two months, naming means and a hired killer", "OpenAI's moderation process, automated classifiers plus human review, flagged the account. The company chose to refer it to the FBI under its policy on imminent threats to others", "A standing FBI-to-Brazil channel through the Ministry of Justice CyberLab relayed the file to the state police within days", "The Polícia Civil do Espírito Santo prioritized the case, identified the user, confirmed the child existed and obtained warrants before the stated date" ], "must_hold": [ "The user discloses the plan to the assistant rather than keeping it offline", "The vendor's reviewers judge the threat credible and imminent and choose referral rather than only an account ban", "A cross-border law-enforcement channel exists and acts within days", "Local police can identify the user from the vendor's data and secure judicial warrants before the stated date" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0209", "case_url": "records/AIBD-0209.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point." }, { "label": "AI contribution", "value": "Hawkins identified an unmanned Corsair as the recovery vessel. DefenseScoop describes the platform’s autonomous capabilities, but its suggestion that passive sensing helped locate the crew is an inference. The official sources do not identify the navigation mode, human supervision, or sensor contribution during this rescue. A crewed helicopter completed the recovery." } ], "filters": { "topic": "Defense and borders", "benefit": "reported", "jurisdiction": [ "Oman" ] }, "record": { "id": "AIBD-0209", "title": "Saronic Corsair sea drone picks up two Apache crew off Oman and carries them to a helicopter hoist", "summary": "A Navy Corsair sea drone picked up two Apache crew off Oman and carried them to a helicopter hoist; CENTCOM reported that both were rescued within about two hours.", "date_observed": "2026-06-09", "date_basis": "event", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "category": "protective response", "unexpected": true, "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point.", "system": "Saronic Corsair unmanned (autonomous) surface vessel operated by U.S. 5th Fleet's Task Force 59", "model_version": "Corsair; navigation-software version not reported", "developer": "Saronic Technologies (Texas)", "deployer": "U.S. Navy, 5th Fleet Task Force 59 (U.S. Naval Forces Central Command)", "location": "Off the coast of Oman, near the Strait of Hormuz", "domain": "Military search and rescue / personnel recovery at sea", "segment": "Government, security, and defense (naval unmanned surface vessels)", "benefit_type": "Improved Physical Health", "taxonomy_note": "An at-sea rescue of aircrew is read as a health outcome because no rescue type exists.", "registry_ids": { "oecd_aim": "2026-06-09-8f45" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. The sources do not establish the counterfactual risk of death or whether another rescue asset could have reached them." }, { "group": "organizational", "depth": 2, "reason": "The military recovered two crew members using an unmanned vessel and a helicopter. The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ], "ai_role": { "status": "unassessed", "statement": "Hawkins identified an unmanned Corsair as the recovery vessel. DefenseScoop describes the platform’s autonomous capabilities, but its suggestion that passive sensing helped locate the crew is an inference. The official sources do not identify the navigation mode, human supervision, or sensor contribution during this rescue. A crewed helicopter completed the recovery." }, "evidence": [ { "kind": "press release", "citation": "U.S. Central Command Public Affairs, “U.S. Army Crew Safely Rescued After Helicopter Lost at Sea,” June 9, 2026, DVIDS story 567214.", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "date": "2026-06-09", "written_by": "deployer", "quote": "The Soldiers were safely rescued within approximately two hours and are in stable condition." }, { "kind": "news report", "citation": "Brandi Vincent, “Autonomous Corsair maritime drone rescues US military pilots after crash near Oman,” DefenseScoop, June 9, 2026; direct interview with CENTCOM spokesperson Capt. Tim Hawkins.", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "date": "2026-06-09", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] }, "paired_incident": null, "headline": "Saronic Corsair sea drone picks up two Apache crew off Oman", "metric": "both were rescued within approximately two hours", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development after the 9-10 June 2026 reports was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-09-8f45", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.saronic.com/careers; https://en.wikipedia.org/wiki/United_States_Navy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-23", "change": "Reviewed the occurrence date, day precision, and benefit status against ABC News and DefenseScoop. The reported time of June 8, 2026 at 7:33 p.m. EDT is June 9 in Oman. The rescue is a reported benefit. The AI contribution and depth ratings were not reassessed.", "source": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326; https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "source": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0209", "catalog_version": "1.0.0", "source_fingerprint": "590f2c708df195209bc2591a071f233b3686c1830447724b39f9f4b64ba670e0", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Saronic Technologies (Texas)", "deployer": "U.S. Navy, 5th Fleet Task Force 59 (U.S. Naval Forces Central Command)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "Hawkins identified an unmanned Corsair as the recovery vessel. DefenseScoop describes the platform’s autonomous capabilities, but its suggestion that passive sensing helped locate the crew is an inference. The official sources do not identify the navigation mode, human supervision, or sensor contribution during this rescue. A crewed helicopter completed the recovery." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. The sources do not establish the counterfactual risk of death or whether another rescue asset could have reached them." }, { "group": "organizational", "depth": 2, "reason": "The military recovered two crew members using an unmanned vessel and a helicopter. The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. 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The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "CENTCOM reports that the two aviators reached safety within about two hours and were in stable condition. The rescue restored their safety and demonstrated an operational use of the vessel. The sources do not establish a risk of death or measure the AI’s separate contribution.", "editor_note": "" } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "Reported benefit" }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "benefit_status" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. 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"economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": 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"partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "both were rescued within approximately two hours", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-06-09-8f45" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No development after the 9-10 June 2026 reports was found in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0209" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "An at-sea rescue of aircrew is read as a health outcome because no rescue type exists." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Both aviators were rescued within approximately two hours.", "editor_note": "" } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Two aviators were recovered from the water and transferred to a helicopter within approximately two hours, according to CENTCOM. Their condition was stable. The sources do not establish the counterfactual risk of death or whether another rescue asset could have reached them." }, { "group": "organizational", "depth": 2, "reason": "The military recovered two crew members using an unmanned vessel and a helicopter. The sources describe a successful operational recovery but do not isolate the AI’s contribution or quantify an organizational saving." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].status": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_status": "reported" } }, "provenance": { "kind": "source_review", "basis": "ABC News and DefenseScoop report that the Corsair recovered the two crew members and transported them to a helicopter hoist. The classification concerns a reported realized rescue. This scoped review did not reassess the AI contribution, the depth ratings, or whether the rescue prevented otherwise unavoidable deaths.", "record_fields": [ "benefit_status" ], "source_refs": [ "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326", "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/" ], "reviewed_on": "2026-10-23" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": true, "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Off the coast of Oman, near the Strait of Hormuz", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The Corsair is an autonomous maritime platform that Task Force 59 had been fielding since late March 2026. In this case, the military used it to carry stranded crew members to a helicopter recovery point." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "purpose" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Task Force 59 used a Corsair vessel to carry the stranded aviators to a helicopter recovery point.", "editor_note": "" } }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-09", "date_basis": "event", "date_precision": "day", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-09-8f45", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.saronic.com/careers; https://en.wikipedia.org/wiki/United_States_Navy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-23", "change": "Reviewed the occurrence date, day precision, and benefit status against ABC News and DefenseScoop. The reported time of June 8, 2026 at 7:33 p.m. EDT is June 9 in Oman. The rescue is a reported benefit. The AI contribution and depth ratings were not reassessed.", "source": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326; https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "source": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "June 9, 2026 is the Oman calendar date of the rescue. The official timestamp is 7:33 p.m. ET on June 8. The previous review incorrectly labeled it the crash time. Revision history is preserved separately from case chronology.", "record_fields": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "replication" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The vessel needs to reach and carry people safely, with navigation and communications that suit the conditions. The vessel’s operators and helicopter crew need to coordinate the transfer.", "editor_note": "" } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-09-8f45", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.saronic.com/careers; https://en.wikipedia.org/wiki/United_States_Navy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-23", "change": "Reviewed the occurrence date, day precision, and benefit status against ABC News and DefenseScoop. The reported time of June 8, 2026 at 7:33 p.m. EDT is June 9 in Oman. The rescue is a reported benefit. The AI contribution and depth ratings were not reassessed.", "source": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326; https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "source": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Military search and rescue / personnel recovery at sea", "segment": "Government, security, and defense (naval unmanned surface vessels)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "value_present", "value": { "kind": "structured", "data": { "sources": [ { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "access": "full official release read" }, { "url": "https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4511869/us-army-crew-safely-rescued-after-helicopter-lost-at-sea/", "access": "403; official DVIDS copy read instead" }, { "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "access": "full article read" }, { "url": "https://arstechnica.com/gadgets/2026/06/us-military-claims-first-drone-boat-rescue-of-downed-helicopter-crew/", "access": "full article read and original-release link followed" } ] } }, "provenance": { "kind": "source_review", "basis": "Recorded which versions were inspected and which origins or supplements could not be read.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The complete CENTCOM release is available through the official DVIDS copy. The Navy’s vessel identification and linked reporting are also available.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Central Command Public Affairs, “U.S. Army Crew Safely Rescued After Helicopter Lost at Sea,” June 9, 2026, DVIDS story 567214.", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "date": "2026-06-09", "written_by": "deployer", "quote": "The Soldiers were safely rescued within approximately two hours and are in stable condition." }, { "kind": "news report", "citation": "Brandi Vincent, “Autonomous Corsair maritime drone rescues US military pilots after crash near Oman,” DefenseScoop, June 9, 2026; direct interview with CENTCOM spokesperson Capt. Tim Hawkins.", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "date": "2026-06-09", "written_by": "independent" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "role": "Retained evidence entries in reviewed order for this fingerprinted record version" }, "provenance": { "kind": "source_review", "basis": "Assigned to the retained evidence entries after this source review.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Central Command Public Affairs, “U.S. Army Crew Safely Rescued After Helicopter Lost at Sea,” June 9, 2026, DVIDS story 567214.", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "date": "2026-06-09", "written_by": "deployer", "quote": "The Soldiers were safely rescued within approximately two hours and are in stable condition." }, { "kind": "news report", "citation": "Brandi Vincent, “Autonomous Corsair maritime drone rescues US military pilots after crash near Oman,” DefenseScoop, June 9, 2026; direct interview with CENTCOM spokesperson Capt. Tim Hawkins.", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "date": "2026-06-09", "written_by": "independent" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0209-source-0001" ], "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "title": "U.S. Army Crew Safely Rescued After Helicopter Lost at Sea", "published_on": "2026-06-09", "relationship": "CENTCOM Public Affairs supplies the original official rescue chronology and crew condition." }, { "source_refs": [ "AIBD-0209-source-0002" ], "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "title": "Autonomous Corsair maritime drone rescues US military pilots after crash near Oman", "published_on": "2026-06-09", "relationship": "DefenseScoop contributes a direct interview with Capt. Tim Hawkins that identifies the Corsair and the transfer to a helicopter. It is original journalism, although the rescue details remain military testimony." } ], "excluded_republications": [ { "url": "https://abcnews.com/Politics/unmanned-drone-boat-rescues-2-us-crew-members/story?id=133713326", "citation": "Martinez L (2026). Unmanned drone boat rescues 2 US crew members from downed Apache helicopter near Strait of Hormuz. ABC News, 9 June 2026, 3:11 PM.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://gcaptain.com/billion-dollar-usv-builder-saronic-scores-operational-milestone-in-oman-rescue/", "citation": "Schuler M (2026). Billion-Dollar USV Builder Saronic Scores Military Rescue Milestone Near Hormuz. gCaptain, 9 June 2026; quotes CENTCOM spokesman Capt. Tim Hawkins via Reuters and a Saronic statement.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.csmonitor.com/USA/Military/2026/0610/navy-drones-apache-crew-rescue", "citation": "Mulrine Grobe A (2026). Troop rescue near Iran points to rising use of sea drones in combat. The Christian Science Monitor, 10 June 2026, 11:50 a.m. ET.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.dailywire.com/news/the-robot-that-beat-iran-to-two-american-pilots", "citation": "Akiva K (2026). The Robot That Beat Iran To Two American Pilots. The Daily Wire, 9 June 2026; relays Capt. Tim Hawkins's remarks to the Wall Street Journal.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." }, { "url": "https://www.saronic.com/", "citation": "Saronic Technologies (2026). Corsair Rescues Downed Pilots in the Strait of Hormuz. News caption on saronic.com, linking to the Wall Street Journal; undated.", "relationship": "ABC repeats the official timing; gCaptain and Daily Wire relay statements available through the originals; the generic Saronic homepage is not additional evidence. The Monitor’s previously quoted proximity statement is not needed to support the retained benefit and was not freshly verified." } ], "lead_source_ref": "AIBD-0209-source-0001", "retained_source_order": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "ordering_basis": "The first source directly supports the reported benefit or is the closest accessible original account. Each additional source has a distinct evidentiary contribution described in its relationship. Documented inaccessible origins are not represented as having been read.", "claim_lineage": [ { "claim": "Both crew members were rescued at 7:33 p.m. ET on June 8 within approximately two hours, and were stable.", "source_ref": "AIBD-0209-source-0001", "source_chain": [ { "url": "https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4511869/us-army-crew-safely-rescued-after-helicopter-lost-at-sea/", "role": "original command website, access denied" }, { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "role": "same command’s full official distribution, read" } ], "locator": "Official release paragraphs 1–3", "method": {}, "limitations": [ "No crash time is supplied. The cause was under investigation." ] }, { "claim": "Task Force 59’s Saronic Corsair picked up the crew and carried them to a helicopter hoist.", "source_ref": "AIBD-0209-source-0002", "source_chain": [ { "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "locator": "Hawkins interview, opening and paragraphs 3–4", "role": "DefenseScoop contributes a direct interview with Capt. Tim Hawkins that identifies the Corsair and the transfer to a helicopter. It is original journalism, although the rescue details remain military testimony." } ], "locator": "Hawkins interview, opening and paragraphs 3–4", "method": {}, "limitations": [ "The source does not specify the autonomy mode or the exact contribution of AI." ] }, { "claim": "The local event date is June 9, 2026 in Oman.", "source_ref": "AIBD-0209-source-0001", "source_chain": [ { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "locator": "Official rescue timestamp", "role": "CENTCOM Public Affairs supplies the original official rescue chronology and crew condition." } ], "locator": "Official rescue timestamp", "method": { "calculation": "7:33 p.m. EDT June 8 plus eight hours equals 3:33 a.m. Gulf Standard Time June 9" }, "limitations": [ "The conversion refers to the rescue, not the helicopter’s loss." ] } ], "limitations": [ "No mission log, operator account, sensor record, or independent evaluation establishes the AI’s particular role.", "Ars Technica links the original CENTCOM release and directly reproduces Hawkins’ platform identification, but that overlaps the retained sources.", "ABC and Christian Science Monitor pages were inaccessible in this review; no claim of fresh full-text review is made." ], "access_audit": [ { "url": "https://www.dvidshub.net/news/567214/us-army-crew-safely-rescued-after-helicopter-lost-sea", "access": "full official release read" }, { "url": "https://www.centcom.mil/MEDIA/PUBLIC-RELEASES/Article/4511869/us-army-crew-safely-rescued-after-helicopter-lost-at-sea/", "access": "403; official DVIDS copy read instead" }, { "url": "https://defensescoop.com/2026/06/09/autonomous-maritime-drone-rescues-apache-pilots-after-crash/", "access": "full article read" }, { "url": "https://arstechnica.com/gadgets/2026/06/us-military-claims-first-drone-boat-rescue-of-downed-helicopter-crew/", "access": "full article read and original-release link followed" } ], "related_material": [], "scope": "Complete review of retained-source lineage and access status. This does not mean that inaccessible upstream documents were inspected, that every required case field is complete, or that the original claims were independently verified." } }, "provenance": { "kind": "source_review", "basis": "Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down. Access limits are enumerated in the review.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "CENTCOM describes the rescue, and the Navy identifies the vessel. Mission logs, sensor records, and an operator account of the AI’s role are unavailable.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "A Navy Corsair sea drone picked up two Apache crew off Oman and carried them to a helicopter hoist; CENTCOM reported that both were rescued within about two hours.", "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A Navy Corsair sea drone carried two Apache crew members off Oman to a helicopter recovery point. CENTCOM reported that both were rescued within about two hours.", "editor_note": "" } }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The deployment used a Saronic Corsair vessel." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Corsair; navigation-software version not reported" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "model_version" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The Corsair’s navigation software release is not disclosed.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Saronic Corsair unmanned (autonomous) surface vessel operated by U.S. 5th Fleet's Task Force 59" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Saronic Technologies (Texas)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Saronic Corsair sea drone picks up two Apache crew off Oman and carries them to a helicopter hoist", "headline": "Saronic Corsair sea drone picks up two Apache crew off Oman" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "590f2c708df195209bc2591a071f233b3686c1830447724b39f9f4b64ba670e0" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "U.S. Central Command reported that two Army AH-64 Apache crew members were rescued at 7:33 p.m. Eastern time on June 8, 2026, or 3:33 a.m. on June 9 in Oman. That is the rescue time, not the time that the helicopter went down. CENTCOM said that recovery took approximately two hours and that both crew members were stable. In a direct interview with DefenseScoop, Capt. Tim Hawkins identified the vessel as a Saronic Corsair operated by the Navy’s Task Force 59. He said that it picked up the crew and carried them to another point on the water for a helicopter hoist. The official release said that the cause of the helicopter’s loss was under investigation. The sources do not identify which autonomous functions were used during the rescue.", "replication": { "produced_by": [ "A Corsair already deployed with Task Force 59, which began fielding these vessels in late March 2026", "A vessel able to carry the crew to a location that a rescue helicopter could reach", "A crewed helicopter and coordinated military rescue effort" ], "must_hold": [ "The vessel must reach and carry the people safely", "The human and unmanned parts of the rescue must coordinate", "Navigation, sensing, and communications must be adequate for the conditions; their exact use here is not documented" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Recovered CENTCOM’s original release via its official DVIDS distribution. Corrected the rescue time previously described as the crash time and removed the unsupported statement that the helicopter was definitively shot down.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0209-source-0001", "AIBD-0209-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A Corsair vessel reached the crew and carried them to a point where a helicopter could recover them. Task Force 59 had begun fielding these vessels in late March 2026.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 34, "reviewed": 21, "record_mapped": 13, "not_assessed": 57, "complete": false, "by_state": { "value_present": 34, "not_reported": 0, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0234", "case_url": "records/AIBD-0234.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Detect behavior indicating suicide risk (and illness, immobility or violence) in security-camera footage and alert staff." }, { "label": "AI contribution", "value": "Kyodo attributes both interventions to Asilla’s account. The company’s original product documentation supports the existence and intended mechanism of jump-risk alerts, but does not independently verify either intervention. The facilities, dates, alert logs, and subsequent outcomes are not supplied." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0234", "title": "Asilla's behavior-analysis AI at about 40 Japanese stations and buildings may have prompted two suicide interventions", "summary": "Asilla told Kyodo that camera alerts prompted guards to intervene in two apparent suicide risks. The facilities and dates were not identified, and the company is the sole source of those accounts.", "date_observed": "2026-06-01", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The two interventions are undated. Tokyo-based Asilla's system assesses the likelihood that a person will jump by analyzing behavior on security-camera footage. It looks for behavior such as pacing or lingering near a platform edge or rooftop. It alerts security guards and station staff, in some cases with loudspeaker warnings. In one case it detected a man loitering in a non-customer area of a commercial facility. A guard found him, and he later said he had intended to kill himself by jumping. In another, a child lingering near an upper-floor railing was found to have been writing a suicide note. Asilla says it trained the system since 2022 with around 200 facilities on about seven million pieces of footage. All details come from the company via Kyodo, and no operator, police or academic confirmation is among the sources. Asilla’s earlier product release identifies a jump-risk detection function in AI Security asilla version 2.4, using movement trajectories, position, and body-motion patterns. It does not identify the version used in these two interventions.", "category": "protective response", "unexpected": false, "purpose": "Detect behavior indicating suicide risk (and illness, immobility or violence) in security-camera footage and alert staff.", "system": "Asilla AI behavior-analysis system for security cameras", "model_version": null, "developer": "Asilla Inc.", "deployer": "About 40 railway stations and commercial buildings in Japan (operators not named)", "location": null, "domain": "Public safety / suicide prevention", "segment": "Transport and real estate", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Two people in apparent crisis were reached by guards after alerts, per the developer; no independent account of either case." }, { "group": "organizational", "depth": null, "reason": "No operator-reported outcome or false-alert data in the sources." } ], "ai_role": { "status": "assessed", "statement": "Kyodo attributes both interventions to Asilla’s account. The company’s original product documentation supports the existence and intended mechanism of jump-risk alerts, but does not independently verify either intervention. 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Tokyo-based Asilla's system assesses the likelihood that a person will jump by analyzing behavior on security-camera footage. It looks for behavior such as pacing or lingering near a platform edge or rooftop. It alerts security guards and station staff, in some cases with loudspeaker warnings. In one case it detected a man loitering in a non-customer area of a commercial facility. A guard found him, and he later said he had intended to kill himself by jumping. In another, a child lingering near an upper-floor railing was found to have been writing a suicide note. Asilla says it trained the system since 2022 with around 200 facilities on about seven million pieces of footage. All details come from the company via Kyodo, and no operator, police or academic confirmation is among the sources. Asilla’s earlier product release identifies a jump-risk detection function in AI Security asilla version 2.4, using movement trajectories, position, and body-motion patterns. It does not identify the version used in these two interventions.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Asilla reported two interventions after camera alerts. 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Neither intervention is dated. Revision history is preserved separately from case chronology.", "record_fields": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Existing security cameras in areas with jump risk", "A behavior model trained on large volumes of site footage", "Guards or staff positioned to respond within minutes" ], "must_hold": [ "Staff presence to act on alerts", "Acceptable false-alert rates for operators", "Camera coverage of the risk zones" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.wantedly.com/companies/asilla", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "source": "https://www.straitstimes.com/asia/east-asia/japan-stations-facilities-use-ai-system-to-prevent-suicide-by-jumping", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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Asilla’s original product release is available.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Kyodo News via The Straits Times, 1 Jun 2026", "title": "Japan stations, facilities use AI system to prevent suicide by jumping", "url": "https://www.straitstimes.com/asia/east-asia/japan-stations-facilities-use-ai-system-to-prevent-suicide-by-jumping", "date": "2026-06-01", "written_by": "independent", "quote": "The man later said he had intended to kill himself by jumping, according to the company." }, { "kind": "press release", "citation": "Asilla, “アジラ、最先端のAI警備システム『AI Security asilla』をバージョンアップ。ver.2.4のリリースを開始！,” January 26, 2024, “飛び降り予兆検知機能.”", "title": "アジラ、最先端のAI警備システム『AI Security asilla』をバージョンアップ。ver.2.4のリリースを開始！", "url": "https://jp.asilla.com/post/version-2-4-20240126", "date": "2024-01-26", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0234-source-0001" ], "url": "https://www.straitstimes.com/asia/east-asia/japan-stations-facilities-use-ai-system-to-prevent-suicide-by-jumping", "title": "Japan stations, facilities use AI system to prevent suicide by jumping", "published_on": "2026-06-01", "relationship": "Kyodo’s report is original journalism carried by The Straits Times. The two intervention accounts are explicitly attributed to Asilla, not independently confirmed by the reporter." }, { "source_refs": [ "AIBD-0234-source-0002" ], "url": "https://jp.asilla.com/post/version-2-4-20240126", "title": "アジラ、最先端のAI警備システム『AI Security asilla』をバージョンアップ。ver.2.4のリリースを開始！", "published_on": "2024-01-26", "relationship": "Asilla’s January 2024 release identifies the product function and technical inputs. It is separate primary documentation of the mechanism, not corroboration of the two events." } ], "excluded_republications": [ { "url": "https://oecd.ai/en/incidents/2026-06-01-db16", "citation": "OECD AI Incidents Monitor, incident 2026-06-01-db16", "relationship": "The OECD entry repeats the reported interventions and adds no independent evidence." } ], "lead_source_ref": "AIBD-0234-source-0001", "retained_source_order": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "ordering_basis": "The first source directly supports the reported benefit or is the closest accessible original account. Each additional source has a distinct evidentiary contribution described in its relationship. Documented inaccessible origins are not represented as having been read.", "claim_lineage": [ { "claim": "Asilla said that alerts prompted guards to reach two people who appeared to be at risk of jumping.", "source_ref": "AIBD-0234-source-0001", "source_chain": [ { "url": "https://www.straitstimes.com/asia/east-asia/japan-stations-facilities-use-ai-system-to-prevent-suicide-by-jumping", "locator": "Two case paragraphs following the alert workflow", "role": "Kyodo’s report is original journalism carried by The Straits Times. The two intervention accounts are explicitly attributed to Asilla, not independently confirmed by the reporter." } ], "locator": "Two case paragraphs following the alert workflow", "method": {}, "limitations": [ "The developer is the sole identified origin of both cases.", "No facility, date, operator statement, or intervention log is given." ] }, { "claim": "AI Security asilla version 2.4 introduced jump-risk detection based on trajectories, position, and body movement.", "source_ref": "AIBD-0234-source-0002", "source_chain": [ { "url": "https://jp.asilla.com/post/version-2-4-20240126", "locator": "“飛び降り予兆検知機能”", "role": "Asilla’s January 2024 release identifies the product function and technical inputs. It is separate primary documentation of the mechanism, not corroboration of the two events." } ], "locator": "“飛び降り予兆検知機能”", "method": {}, "limitations": [ "The version deployed in the two 2026-reported cases is unspecified." ] } ], "limitations": [ "The original Kyodo distribution address was traced through Chiba TV to https://news.jp/i/1433984055470228190; that endpoint was inaccessible. The complete identified Kyodo syndication in The Straits Times was read.", "The company’s claim that lives were saved is not independent evidence of the counterfactual.", "A related Osaka report describes false alarms and response gaps, but cannot be matched to either anonymous case." ], "access_audit": [ { "url": "https://www.straitstimes.com/asia/east-asia/japan-stations-facilities-use-ai-system-to-prevent-suicide-by-jumping", "access": "complete identified Kyodo syndication read" }, { "url": "https://www.chiba-tv.com/plus/detail/amp/2026061691278", "access": "Kyodo attribution, date, and original distribution link read" }, { "url": "https://news.jp/i/1433984055470228190", "access": "inaccessible; not read" }, { "url": "https://jp.asilla.com/post/version-2-4-20240126", "access": "complete original product release read" } ], "related_material": [ { "url": "https://www.mlit.go.jp/toshi/tosiko/content/001892574.pdf", "title": "スマートシティ実装化支援事業 調査報告書", "author": "うめきた２期地区等スマートシティ形成協議会", "date": "March 2025", "locators": [ "pp. 26–31, AI camera deployment using asilla and icetana" ], "access": "relevant operational results and limitations read, not represented as a complete audit of all 46 pages", "finding": "Reports worker-related false alerts, difficulty responding promptly to detections, and the limitation of jump-risk criteria for immediate jumps.", "relationship": "A separate deployment and useful operational caution. It is not attributed to the two anonymous interventions and is not counted as corroboration." } ], "scope": "Complete review of retained-source lineage and access status. This does not mean that inaccessible upstream documents were inspected, that every required case field is complete, or that the original claims were independently verified." } }, "provenance": { "kind": "source_review", "basis": "Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration. Access limits are enumerated in the review.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Kyodo reports Asilla’s account of two interventions. The complete Straits Times republication is available, but the original distribution page is inaccessible. A separate Osaka report describes false alarms and gaps in response, without identifying either of these incidents.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Asilla told Kyodo that camera alerts prompted guards to intervene in two apparent suicide risks. The facilities and dates were not identified, and the company is the sole source of those accounts.", "account": "The two interventions are undated. Tokyo-based Asilla's system assesses the likelihood that a person will jump by analyzing behavior on security-camera footage. It looks for behavior such as pacing or lingering near a platform edge or rooftop. It alerts security guards and station staff, in some cases with loudspeaker warnings. In one case it detected a man loitering in a non-customer area of a commercial facility. A guard found him, and he later said he had intended to kill himself by jumping. In another, a child lingering near an upper-floor railing was found to have been writing a suicide note. Asilla says it trained the system since 2022 with around 200 facilities on about seven million pieces of footage. All details come from the company via Kyodo, and no operator, police or academic confirmation is among the sources. Asilla’s earlier product release identifies a jump-risk detection function in AI Security asilla version 2.4, using movement trajectories, position, and body-motion patterns. It does not identify the version used in these two interventions." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Asilla AI behavior-analysis system for security cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Asilla Inc." } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Asilla's behavior-analysis AI at about 40 Japanese stations and buildings may have prompted two suicide interventions", "headline": "Asilla's camera AI may have prompted two suicide interventions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f901188e7feedf658f44ab9f117672015b105d67cca14325d6cd760c68c7535e" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The two interventions are undated. Tokyo-based Asilla's system assesses the likelihood that a person will jump by analyzing behavior on security-camera footage. It looks for behavior such as pacing or lingering near a platform edge or rooftop. It alerts security guards and station staff, in some cases with loudspeaker warnings. In one case it detected a man loitering in a non-customer area of a commercial facility. A guard found him, and he later said he had intended to kill himself by jumping. In another, a child lingering near an upper-floor railing was found to have been writing a suicide note. Asilla says it trained the system since 2022 with around 200 facilities on about seven million pieces of footage. All details come from the company via Kyodo, and no operator, police or academic confirmation is among the sources. Asilla’s earlier product release identifies a jump-risk detection function in AI Security asilla version 2.4, using movement trajectories, position, and body-motion patterns. It does not identify the version used in these two interventions.", "replication": { "produced_by": [ "Existing security cameras in areas with jump risk", "A behavior model trained on large volumes of site footage", "Guards or staff positioned to respond within minutes" ], "must_hold": [ "Staff presence to act on alerts", "Acceptable false-alert rates for operators", "Camera coverage of the risk zones" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The system analyzed security-camera footage for behavior that suggested a risk of jumping. Guards or other staff responded to its alerts.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 13, "record_mapped": 17, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0260", "case_url": "records/AIBD-0260.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "OpenAI says that when it detects users planning to harm others, it routes the conversations to a small team trained on its usage policies. It says that if human reviewers find an imminent threat of serious physical harm to others, it may refer the case to law enforcement. This referral is that designed use. ChatGPT itself is a general-purpose assistant, which Zhou used to rehearse his plans." }, { "label": "AI contribution", "value": "The reporting identifies an OpenAI disclosure as the route that brought Zhou’s messages to police attention. OpenAI’s general policy describes detection followed by human review before possible referral to law enforcement. That policy does not document the exact classifier, trigger, reviewer decisions, or ChatGPT replies in this case." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0260", "title": "OpenAI refers Darren Zhou’s threats to the FBI; local police investigate and he pleads guilty to stalking and threat charges", "summary": "An OpenAI disclosure brought Darren Zhou’s threats to police attention; he pleaded guilty to stalking and threat charges and received probation, GPS monitoring, and contact and weapons restrictions.", "date_observed": "2026-05-29", "date_basis": "event", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Court reporting describes an OpenAI emergency disclosure that reached the Palm Beach County Sheriff’s Office through the FBI after Darren Zhou wrote threats against his ex-girlfriend in ChatGPT. Deputies compared those messages with anonymous threats that she had preserved. The sheriff’s booking entry lists May 29, 2026 as his arrest date. BocaNewsNow reports that he pleaded guilty on August 13 to written threats, aggravated stalking, and unlawful use of a two-way communication device. Adjudication was withheld, with eight years of probation, two years of GPS monitoring, and restrictions on contact and weapons. The accessible reporting cites court documents but does not reproduce the complete affidavit or plea order. It supports a police intervention and court restrictions, rather than proof that an attack would otherwise have occurred.", "category": "protective response", "unexpected": false, "purpose": "OpenAI says that when it detects users planning to harm others, it routes the conversations to a small team trained on its usage policies. It says that if human reviewers find an imminent threat of serious physical harm to others, it may refer the case to law enforcement. 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BocaNewsNow.com, 27 August 2026; cites court records.", "title": "Man Flagged by ChatGPT Pleads Guilty in Stalking Case", "url": "https://bocanewsnow.com/2026/08/27/man-flagged-by-chatgpt-pleads-guilty-in-stalking-case/", "date": "2026-08-27", "written_by": "independent", "quote": null }, { "kind": "other", "citation": "Palm Beach County Sheriff's Office (2026). Booking Blotter, record for ZHOU, DARREN SHIDA, booking number 2026014226, arresting agency 01-PBSO. Retrieved 20 October 2026 by searching the blotter for last name Zhou between 20 May and 5 June 2026; the search form returns results without a stable record URL. The page rejected access during the October 24 source audit; the earlier record identification is preserved without a fresh verification claim.", "title": "Palm Beach County Sheriff's Office - Booking Blotter", "url": "https://www3.pbso.org/blotter/", "date": "2026-05-29", "written_by": "independent", "quote": null }, { "kind": "news report", "citation": "Moolten S (2026). Ex-financial analyst sentenced to probation after telling ChatGPT about plans to rape and kill his ex. South Florida Sun Sentinel, 14 August 2026; cites the PBSO probable cause affidavit and the State Attorney's Office.", "title": "Ex-financial analyst sentenced to probation after telling ChatGPT about plans to rape and kill his ex", "url": "https://www.sun-sentinel.com/2026/08/14/financial-analyst-sentenced-to-probation-after-telling-chatgpt-about-plans-to-rape-and-kill-his-ex/", "date": "2026-08-14", "written_by": "independent", "quote": null }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. OpenAI blog; Hoodline dates the policy to August 2025.", "title": "Helping people when they need it most", "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "date": null, "written_by": "makers", "quote": "If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement." } ], "replication": { "produced_by": [ "A reported OpenAI emergency disclosure of threatening messages to the FBI", "Referral to local police and comparison with messages that the threatened person had preserved", "Investigation and court restrictions following a guilty plea" ], "must_hold": [ "A disclosure must reach an agency able to investigate", "Investigators must establish identity and assess the threat using supporting evidence", "A general detection-and-review policy does not establish the details of a particular referral", "Arrest and court restrictions do not prove that an attack would otherwise have occurred" ], "echoes": [] }, "paired_incident": null, "headline": "OpenAI refers Darren Zhou's threats to the FBI; he pleads guilty", "metric": "arrest on May 29, 2026; guilty plea on August 13", "comparator": null, "later": { "status": "litigated", "date": "2026-08-13", "note": "Zhou pleaded guilty on 13 August 2026 to written threats to kill, aggravated stalking and misuse of a two-way device, with adjudication withheld and eight years of probation.", "quote": null, "url": "https://bocanewsnow.com/2026/08/27/man-flagged-by-chatgpt-pleads-guilty-in-stalking-case/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-08-15-e71a", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.sun-sentinel.com/2026/08/14/financial-analyst-sentenced-to-probation-after-telling-chatgpt-about-plans-to-rape-and-kill-his-ex/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI; https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed a corrupted quotation from the booking entry and reduced eight sources to the original accessible court reporting, the previously identified official booking, material local reporting, and the provider’s original policy. The court documents remain an explicit access gap.", "source": "https://bocanewsnow.com/2026/08/27/man-flagged-by-chatgpt-pleads-guilty-in-stalking-case/", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, one other independent source and the makers' press release. 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The page rejected access during the October 24 source audit; the earlier record identification is preserved without a fresh verification claim.", "title": "Palm Beach County Sheriff's Office - Booking Blotter", "url": "https://www3.pbso.org/blotter/", "date": "2026-05-29", "written_by": "independent" }, { "kind": "news report", "citation": "Moolten S (2026). Ex-financial analyst sentenced to probation after telling ChatGPT about plans to rape and kill his ex. South Florida Sun Sentinel, 14 August 2026; cites the PBSO probable cause affidavit and the State Attorney's Office.", "title": "Ex-financial analyst sentenced to probation after telling ChatGPT about plans to rape and kill his ex", "url": "https://www.sun-sentinel.com/2026/08/14/financial-analyst-sentenced-to-probation-after-telling-chatgpt-about-plans-to-rape-and-kill-his-ex/", "date": "2026-08-14", "written_by": "independent" }, { "kind": "press release", "citation": "OpenAI (2025). Helping people when they need it most. 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Its court documents are not linked or reproduced in full." }, { "source_refs": [ "AIBD-0260-source-0002" ], "url": "https://www3.pbso.org/blotter/", "title": "Palm Beach County Sheriff's Office - Booking Blotter", "published_on": "2026-05-29", "relationship": "The PBSO blotter is an official origin for the booking identity and date, previously recorded as checked October 20. It was inaccessible October 24 and is not freshly verified." }, { "source_refs": [ "AIBD-0260-source-0003" ], "url": "https://www.sun-sentinel.com/2026/08/14/financial-analyst-sentenced-to-probation-after-telling-chatgpt-about-plans-to-rape-and-kill-his-ex/", "title": "Ex-financial analyst sentenced to probation after telling ChatGPT about plans to rape and kill his ex", "published_on": "2026-08-14", "relationship": "The Sun Sentinel’s earlier local reporting is retained for its affidavit reporting and reported prosecutor/defense context. Its full text was inaccessible in this review." }, { "source_refs": [ "AIBD-0260-source-0004" ], "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "title": "Helping people when they need it most", "relationship": "OpenAI’s original policy explains the general human-review step. It does not verify this particular referral." } ], "excluded_republications": [ { "url": "https://www.wpbf.com/article/south-palm-beach-man-cyberstalking-ex-girlfriend-threats-chatgpt/73434967", "citation": "Laurent V (2026). Man accused of cyberstalking ex after alleged threats flagged through ChatGPT. WPBF 25 News, 14 August 2026 (updated 15 August); cites the arrest affidavit and court documents.", "relationship": "IBTimes and MediaNama relay reporting by other outlets. WPBF and Palm Beach Post supply overlapping case accounts whose additional evidentiary contribution could not be verified here. The retained Sun Sentinel source preserves potentially material prosecutor and defense context rather than discarding it as mere duplication." }, { "url": "https://www.palmbeachpost.com/story/news/crime/2026/08/14/openai-reported-florida-man-darren-zhou-chatgpt-murder-messages-fbi/91285875007/", "citation": "Phillips H (2026). ChatGPT reported South Palm Beach man's rape and murder threats to FBI. The Palm Beach Post, 14 August 2026 (updated 17 August).", "relationship": "IBTimes and MediaNama relay reporting by other outlets. WPBF and Palm Beach Post supply overlapping case accounts whose additional evidentiary contribution could not be verified here. The retained Sun Sentinel source preserves potentially material prosecutor and defense context rather than discarding it as mere duplication." }, { "url": "https://www.ibtimes.co.uk/florida-man-chatgpt-threats-fbi-probation-1814510", "citation": "Jaen R (2026). 'If I Can't Have Her, Nobody Can': ChatGPT Flags Florida Man's Chilling Threat to FBI. International Business Times UK, 17 August 2026.", "relationship": "IBTimes and MediaNama relay reporting by other outlets. WPBF and Palm Beach Post supply overlapping case accounts whose additional evidentiary contribution could not be verified here. The retained Sun Sentinel source preserves potentially material prosecutor and defense context rather than discarding it as mere duplication." }, { "url": "https://www.medianama.com/2026/08/223-openai-reports-chatgpt-murder-threats-to-fbi/", "citation": "Peter AM (2026). OpenAI Reports Florida Man's ChatGPT Murder Threats to FBI. MediaNama, 17 August 2026; draws on The Detroit News and court records.", "relationship": "IBTimes and MediaNama relay reporting by other outlets. WPBF and Palm Beach Post supply overlapping case accounts whose additional evidentiary contribution could not be verified here. 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The audit does not claim that their complete texts were freshly read.", "The original booking quotation contained “specific harm prevented” inside statutory charge text, which is not credible source wording. It was removed.", "The earlier six-day timeline and detailed defense statements should not be elevated beyond the accessible evidence. The public account now avoids those unsupported specifics." ], "access_audit": [ { "url": "https://bocanewsnow.com/2026/08/27/man-flagged-by-chatgpt-pleads-guilty-in-stalking-case/", "access": "full original court-reporting article read" }, { "url": "https://www3.pbso.org/blotter/", "access": "request rejected; earlier October 20 record identification preserved" }, { "url": "https://www.sun-sentinel.com/2026/08/14/financial-analyst-sentenced-to-probation-after-telling-chatgpt-about-plans-to-rape-and-kill-his-ex/", "access": "full text inaccessible; earlier citation preserved for material legal context" }, { "url": "https://www.mypalmbeachclerk.com/records/court-records", "access": "403; underlying filings not retrieved" }, { "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "access": "full original policy article read" } ], "related_material": [], "scope": "Complete review of retained-source lineage and access status. This does not mean that inaccessible upstream documents were inspected, that every required case field is complete, or that the original claims were independently verified." } }, "provenance": { "kind": "source_review", "basis": "Removed a corrupted quotation from the booking entry and reduced eight sources to the original accessible court reporting, the previously identified official booking, material local reporting, and the provider’s original policy. The court documents remain an explicit access gap. Access limits are enumerated in the review.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0260-source-0001", "AIBD-0260-source-0002", "AIBD-0260-source-0003", "AIBD-0260-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The accessible reporting describes the disclosure, arrest, and guilty plea. The affidavit, chats, emergency disclosure, and court order were inaccessible, so the account does not independently establish their full contents.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "An OpenAI disclosure brought Darren Zhou’s threats to police attention; he pleaded guilty to stalking and threat charges and received probation, GPS monitoring, and contact and weapons restrictions.", "account": "Court reporting describes an OpenAI emergency disclosure that reached the Palm Beach County Sheriff’s Office through the FBI after Darren Zhou wrote threats against his ex-girlfriend in ChatGPT. Deputies compared those messages with anonymous threats that she had preserved. The sheriff’s booking entry lists May 29, 2026 as his arrest date. BocaNewsNow reports that he pleaded guilty on August 13 to written threats, aggravated stalking, and unlawful use of a two-way communication device. Adjudication was withheld, with eight years of probation, two years of GPS monitoring, and restrictions on contact and weapons. The accessible reporting cites court documents but does not reproduce the complete affidavit or plea order. It supports a police intervention and court restrictions, rather than proof that an attack would otherwise have occurred." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Removed a corrupted quotation from the booking entry and reduced eight sources to the original accessible court reporting, the previously identified official booking, material local reporting, and the provider’s original policy. The court documents remain an explicit access gap.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0260-source-0001", "AIBD-0260-source-0002", "AIBD-0260-source-0003", "AIBD-0260-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "An OpenAI disclosure brought Darren Zhou’s threats to police attention. He pleaded guilty and received probation, GPS monitoring, and restrictions on contact and weapons.", "editor_note": "" } }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ChatGPT (OpenAI), with OpenAI's automated detection and human review pipeline for users planning to harm others" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "OpenAI refers Darren Zhou’s threats to the FBI; local police investigate and he pleads guilty to stalking and threat charges", "headline": "OpenAI refers Darren Zhou's threats to the FBI; he pleads guilty" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Removed a corrupted quotation from the booking entry and reduced eight sources to the original accessible court reporting, the previously identified official booking, material local reporting, and the provider’s original policy. The court documents remain an explicit access gap.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0260-source-0001", "AIBD-0260-source-0002", "AIBD-0260-source-0003", "AIBD-0260-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "1305768f24ac19a795ee53d018884bb83754966afae529eb2e6116f1de72b313" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Court reporting describes an OpenAI emergency disclosure that reached the Palm Beach County Sheriff’s Office through the FBI after Darren Zhou wrote threats against his ex-girlfriend in ChatGPT. Deputies compared those messages with anonymous threats that she had preserved. The sheriff’s booking entry lists May 29, 2026 as his arrest date. BocaNewsNow reports that he pleaded guilty on August 13 to written threats, aggravated stalking, and unlawful use of a two-way communication device. Adjudication was withheld, with eight years of probation, two years of GPS monitoring, and restrictions on contact and weapons. The accessible reporting cites court documents but does not reproduce the complete affidavit or plea order. It supports a police intervention and court restrictions, rather than proof that an attack would otherwise have occurred.", "replication": { "produced_by": [ "A reported OpenAI emergency disclosure of threatening messages to the FBI", "Referral to local police and comparison with messages that the threatened person had preserved", "Investigation and court restrictions following a guilty plea" ], "must_hold": [ "A disclosure must reach an agency able to investigate", "Investigators must establish identity and assess the threat using supporting evidence", "A general detection-and-review policy does not establish the details of a particular referral", "Arrest and court restrictions do not prove that an attack would otherwise have occurred" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Removed a corrupted quotation from the booking entry and reduced eight sources to the original accessible court reporting, the previously identified official booking, material local reporting, and the provider’s original policy. The court documents remain an explicit access gap.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0260-source-0001", "AIBD-0260-source-0002", "AIBD-0260-source-0003", "AIBD-0260-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "OpenAI reportedly disclosed threatening messages to the FBI. Local police compared them with messages that the threatened person had preserved, investigated, and brought charges. A guilty plea led to court restrictions.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 19, "record_mapped": 12, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0149", "case_url": "records/AIBD-0149.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The Bahnhofsviertel Videoschutzanlage's AI performs real-time biometric remote identification. The Hessian police law amended in February 2025 allows this for narrow purposes such as finding missing persons. Locating a missing minor was that intended use. The police press release called it the system's first genuine positive hit." }, { "label": "AI contribution", "value": "The police press release documents the system's contribution step by step. After a judge-confirmed emergency order, the girl's photo was entered into the database. Late on 9 October the AI produced an automated match alert for Taunusstraße. A video operator checked it and a patrol confirmed it in person. The police had only indications that she might be in the district, so the AI supplied the place and the moment. Human verification and the patrol did the rest. Whether and how soon conventional patrols would have found her is not reported." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Germany" ] }, "record": { "id": "AIBD-0149", "title": "Frankfurt police found a missing 16-year-old who had left a farewell letter on Taunusstraße within a day of her disappearance, after a judge-confirmed emergency order let the Bahnhofsviertel camera system's AI search for her face", "summary": "Frankfurt police found a missing 16-year-old in crisis within a day via a court-backed AI face search of Bahnhofsviertel cameras.", "date_observed": "2025-10-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 8 October 2025 a 16-year-old was reported missing after failing to return to the psychiatric facility in Hesse where she lived. A farewell letter pointed to an acute psychological crisis. Indications placed her in Frankfurt's Bahnhofsviertel. Citing imminent danger, Frankfurt's police president issued an emergency order, confirmed by a judge. So her photograph could be entered into the AI-assisted camera system running in the district since July 2025. It operates under the Hessian police law amended in February 2025. Late on 9 October the system flagged a match on Taunusstraße. A video operator checked it and a patrol confirmed it. Officers took the girl into protective custody and returned her to the child and adolescent psychiatric facility.", "category": "protective response", "unexpected": false, "purpose": "The Bahnhofsviertel Videoschutzanlage's AI performs real-time biometric remote identification. The Hessian police law amended in February 2025 allows this for narrow purposes such as finding missing persons. Locating a missing minor was that intended use. The police press release called it the system's first genuine positive hit.", "system": "AI-assisted video analysis with real-time biometric remote identification in the Frankfurt Bahnhofsviertel police video surveillance system (Videoschutzanlage), monitored from the police Video-Operation-Center", "model_version": "Not reported; none of the sources names a vendor, product or version for the facial-recognition component, and netzpolitik.org (April 2026) says only that it matches against the BKA facial-recognition database", "developer": null, "deployer": "Polizeipräsidium Frankfurt am Main (Hessian police)", "location": "Frankfurt am Main, Germany", "domain": "Policing and missing-person search", "segment": "Public safety", "benefit_type": "Improved Physical Health", "taxonomy_note": "Finding a missing minor in crisis is read as a rescue; no injury or treatment is reported.", "registry_ids": { "oecd_aim": "2025-10-16-7e48" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A 16-year-old whose farewell letter led police to assume an acute psychological crisis was located in the Bahnhofsviertel within a day. She was returned to psychiatric care. The police frame this as averting danger to life and limb. But no source reports what would otherwise have happened, so life-saving is not established." }, { "group": "organizational", "depth": 1, "reason": "The Frankfurt police recorded the first confirmed success of a system in use since July 2025. The police president and the interior minister cited it as validation. netzpolitik.org reported in April 2026 that it was still the only success among a low double-digit number of searches. The operational gain is modest." } ], "ai_role": { "status": "assessed", "statement": "The police press release documents the system's contribution step by step. After a judge-confirmed emergency order, the girl's photo was entered into the database. Late on 9 October the AI produced an automated match alert for Taunusstraße. A video operator checked it and a patrol confirmed it in person. The police had only indications that she might be in the district, so the AI supplied the place and the moment. Human verification and the patrol did the rest. Whether and how soon conventional patrols would have found her is not reported." }, "evidence": [ { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main, reproduced by Heßberger K (2025). Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel. Rhein Main Verlag, 16 October 2025; the page is marked 'Text: PM PP Frankfurt', i.e. the police press release text.", "title": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel", "url": "https://www.rheinmainverlag.de/2025/10/16/kuenstliche-intelligenz-lokalisiert-16-jaehrige-vermisste-im-frankfurter-bahnhofsviertel/", "date": "2025-10-16", "written_by": "deployer", "quote": "Bereits am 9. Oktober, nur einen Tag nach der Vermisstenmeldung, wurde in den späten Abendstunden im Video-Operation-Center der Frankfurter Polizei eine automatisierte Treffermeldung im Bereich der Taunusstraße angezeigt." }, { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main (2025). POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen. Presseportal, 16 October 2025. The page returned HTTP 503 on 20 October 2026; its title was read on the OECD Monitor entry and its text in the Rhein Main Verlag reproduction cited above.", "title": "POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen", "url": "https://www.presseportal.de/blaulicht/pm/4970/6138810", "date": "2025-10-16", "written_by": "deployer", "quote": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen" }, { "kind": "news report", "citation": "Euler R (2025). Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI. Frankfurter Allgemeine Zeitung, 16 October 2025, 15:42; paywalled beyond the opening paragraphs.", "title": "Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI", "url": "https://www.faz.net/aktuell/rhein-main/frankfurt/frankfurter-polizei-findet-vermisstes-maedchen-mithilfe-von-ki-110735027.html", "date": "2025-10-16", "written_by": "independent", "quote": "Mithilfe künstlicher Intelligenz hat die Polizei in der vergangenen Woche eine vermisste Sechzehnjährige im Frankfurter Bahnhofsviertel lokalisiert und in Obhut genommen." }, { "kind": "news report", "citation": "Pfeiffer-Goldmann D (2025). KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden. Frankfurter Neue Presse, 17 October 2025, 14:10; the OECD Monitor lists the same URL under the earlier headline 'KI in Frankfurt erstmals erfolgreich bei Suche nach Mensch per Videoüberwachung'. Quotes police spokesman Patrick Rüter and Interior Minister Roman Poseck.", "title": "KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden", "url": "https://www.fnp.de/frankfurt/ki-technik-hilft-polizei-in-frankfurt-bei-suche-nach-vermisster-93990652.html", "date": "2025-10-17", "written_by": "independent", "quote": "Einen Tag später habe die KI automatisch einen Treffer in der Taunusstraße gemeldet. Ein Videooperator habe das überprüft" }, { "kind": "news report", "citation": "Schröder D (2025). KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt. Bild, 17 October 2025; the quoted sentence is attributed to Police President Stefan Müller.", "title": "KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt", "url": "https://www.bild.de/regional/frankfurt/ki-rettet-vermisstes-maedchen-16-aus-drogen-sumpf-von-frankfurt-68f0b438672b2f7280fac3b4", "date": "2025-10-17", "written_by": "independent", "quote": "Der erste echte positive Treffer zeigt eindrucksvoll, wie uns auf Basis des neuen Polizeigesetzes modernste Technik zuverlässig und erfolgreich bei der Abwehr von Gefahren für Leib und Leben hilft." }, { "kind": "news report", "citation": "Schwarzbeck M (2026). Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht. netzpolitik.org, 22 April 2026; reports 29 cameras in the Bahnhofsviertel, 19 of them usable for automated face recognition, a low double-digit number of persons searched since July 2025, and this case as the only success.", "title": "Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "date": "2026-04-22", "written_by": "independent", "quote": "Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde" }, { "kind": "news report", "citation": "Kissel L, Pfeuffer-Rooschüz L (2026). Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI. hessenschau.de, 25 March 2026; cites this case as the pilot's one reported success and carries criticism from the Chaos Computer Club and the Hessian data protection commissioner.", "title": "Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI", "url": "https://www.hessenschau.de/gesellschaft/hessen-erfand-den-datenschutz--und-ist-heute-vorreiter-bei-videoueberwachung-mit-ki-v1,erfindung-datenschutz-hessen-100.html", "date": "2026-03-25", "written_by": "independent", "quote": "Diese Anlagen können von einem Hilfsmittel zu einem Herrschaftsmittel werden" } ], "replication": { "produced_by": [ "A missing-person report with concrete signs of danger to life (a farewell letter) and indications of the person's whereabouts inside the camera-covered district", "A legal basis, the Hessian police law amended in February 2025, plus a police-president emergency order confirmed by a judge. These let the girl's photo be entered into the system", "An AI-assisted camera network already running in the Bahnhofsviertel since July 2025, with a staffed Video-Operation-Center. Patrols close enough to verify and act on a match the same evening" ], "must_hold": [ "The person passes in front of cameras suitable for automated recognition. netzpolitik.org counted 19 such cameras among the district's 29", "A human operator and a patrol verify the automated match before anyone is taken into custody", "The search stays within the narrow legal purposes the law allows, which Bild lists as missing persons, kidnapping victims and victims of human trafficking" ], "echoes": [] }, "paired_incident": null, "headline": "Frankfurt camera AI finds a missing 16-year-old within a day", "metric": "found a missing 16-year-old in crisis within a day", "comparator": { "against": "all persons searched for with the Bahnhofsviertel face-recognition system since July 2025, a low double-digit number", "before": null, "after": "Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde.", "basis": "measured", "quote": "Das System läuft seit Juli 2025, seitdem wurde „eine niedrige zweistellige Anzahl an Personen“ damit gesucht. Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde.", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/" }, "later": { "status": "litigated", "date": "2026-04-22", "note": "By April 2026 the Hessian Greens had asked the Staatsgerichtshof to review the police-law amendment behind real-time face recognition, and Doña Carmen's Juanita Henning with the GFF had sued at the Frankfurt administrative court asking for referral to the Federal Constitutional Court; the system kept running, with an evaluation phase due from July 2026.", "quote": "Die lassen mit einem Normenkontrollantrag beim Staatsgerichtshof klären , ob die der Echtzeit-Fernidentifizierung zugrunde liegende Polizeigesetzänderung rechtens war. Eine Überprüfung der Rechtsgrundlage streben auch Juanita Henning und die GFF an. Sie haben beim Verwaltungsgericht Frankfurt beantragt, dass es die Abschnitte aus dem Polizeigesetz, die die Fernidentifizierung erlauben, dem Bundesverfassungsgericht vorlegt.", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/" }, "value": null, "developer_country": null, "deployer_country": "Germany", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-16-7e48", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/; https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://de.wikipedia.org/wiki/Polizeipräsidium_Frankfurt_am_Main", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0149", "catalog_version": "1.0.0", "source_fingerprint": "a26560751e2527e5a9f380a895ed0cfdf3dd7b6dd096325e1fc47c76618cf190", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0149-source-0001", "AIBD-0149-source-0002", "AIBD-0149-source-0003", "AIBD-0149-source-0004", "AIBD-0149-source-0005", "AIBD-0149-source-0006", "AIBD-0149-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Polizeipräsidium Frankfurt am Main (Hessian police)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The police press release documents the system's contribution step by step. After a judge-confirmed emergency order, the girl's photo was entered into the database. Late on 9 October the AI produced an automated match alert for Taunusstraße. A video operator checked it and a patrol confirmed it in person. The police had only indications that she might be in the district, so the AI supplied the place and the moment. Human verification and the patrol did the rest. Whether and how soon conventional patrols would have found her is not reported." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The police press release documents the system's contribution step by step. After a judge-confirmed emergency order, the girl's photo was entered into the database. Late on 9 October the AI produced an automated match alert for Taunusstraße. A video operator checked it and a patrol confirmed it in person. The police had only indications that she might be in the district, so the AI supplied the place and the moment. Human verification and the patrol did the rest. Whether and how soon conventional patrols would have found her is not reported." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A 16-year-old whose farewell letter led police to assume an acute psychological crisis was located in the Bahnhofsviertel within a day. She was returned to psychiatric care. The police frame this as averting danger to life and limb. But no source reports what would otherwise have happened, so life-saving is not established." }, { "group": "organizational", "depth": 1, "reason": "The Frankfurt police recorded the first confirmed success of a system in use since July 2025. The police president and the interior minister cited it as validation. netzpolitik.org reported in April 2026 that it was still the only success among a low double-digit number of searches. The operational gain is modest." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A 16-year-old whose farewell letter led police to assume an acute psychological crisis was located in the Bahnhofsviertel within a day. She was returned to psychiatric care. The police frame this as averting danger to life and limb. But no source reports what would otherwise have happened, so life-saving is not established." }, { "group": "organizational", "depth": 1, "reason": "The Frankfurt police recorded the first confirmed success of a system in use since July 2025. The police president and the interior minister cited it as validation. netzpolitik.org reported in April 2026 that it was still the only success among a low double-digit number of searches. The operational gain is modest." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The girl received help during an apparent acute crisis. The police found her within a day, but no source establishes that the search saved her life. The system had one confirmed success among a low double-digit number of searches by April 2026." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A 16-year-old whose farewell letter led police to assume an acute psychological crisis was located in the Bahnhofsviertel within a day. She was returned to psychiatric care. The police frame this as averting danger to life and limb. But no source reports what would otherwise have happened, so life-saving is not established." }, { "group": "organizational", "depth": 1, "reason": "The Frankfurt police recorded the first confirmed success of a system in use since July 2025. The police president and the interior minister cited it as validation. netzpolitik.org reported in April 2026 that it was still the only success among a low double-digit number of searches. The operational gain is modest." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Police located the girl within a day. By April 2026, this remained the system’s only confirmed success among a low double-digit number of searches." } }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Time to locate the missing person and the number of confirmed successful searches." } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The police described a risk to life, but the sources do not establish what would have happened without the search. In April 2026, netzpolitik.org reported that this remained the only confirmed success among a low double-digit number of searches." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-10-16-7e48" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2026-04-22", "note": "By April 2026 the Hessian Greens had asked the Staatsgerichtshof to review the police-law amendment behind real-time face recognition, and Doña Carmen's Juanita Henning with the GFF had sued at the Frankfurt administrative court asking for referral to the Federal Constitutional Court; the system kept running, with an evaluation phase due from July 2026.", "quote": "Die lassen mit einem Normenkontrollantrag beim Staatsgerichtshof klären , ob die der Echtzeit-Fernidentifizierung zugrunde liegende Polizeigesetzänderung rechtens war. Eine Überprüfung der Rechtsgrundlage streben auch Juanita Henning und die GFF an. Sie haben beim Verwaltungsgericht Frankfurt beantragt, dass es die Abschnitte aus dem Polizeigesetz, die die Fernidentifizierung erlauben, dem Bundesverfassungsgericht vorlegt.", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0149" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "The missing girl and the Frankfurt officers who searched for her." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The missing girl and the Frankfurt officers who searched for her." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "One girl." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "One girl." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "Finding a missing minor in crisis is read as a rescue; no injury or treatment is reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 8 October 2025 a 16-year-old was reported missing after failing to return to the psychiatric facility in Hesse where she lived. A farewell letter pointed to an acute psychological crisis. Indications placed her in Frankfurt's Bahnhofsviertel. Citing imminent danger, Frankfurt's police president issued an emergency order, confirmed by a judge. So her photograph could be entered into the AI-assisted camera system running in the district since July 2025. It operates under the Hessian police law amended in February 2025. Late on 9 October the system flagged a match on Taunusstraße. A video operator checked it and a patrol confirmed it. Officers took the girl into protective custody and returned her to the child and adolescent psychiatric facility.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Police found a missing 16-year-old within a day and returned her to psychiatric care." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A 16-year-old whose farewell letter led police to assume an acute psychological crisis was located in the Bahnhofsviertel within a day. She was returned to psychiatric care. The police frame this as averting danger to life and limb. But no source reports what would otherwise have happened, so life-saving is not established." }, { "group": "organizational", "depth": 1, "reason": "The Frankfurt police recorded the first confirmed success of a system in use since July 2025. The police president and the interior minister cited it as validation. netzpolitik.org reported in April 2026 that it was still the only success among a low double-digit number of searches. The operational gain is modest." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The Bahnhofsviertel Videoschutzanlage's AI performs real-time biometric remote identification. The Hessian police law amended in February 2025 allows this for narrow purposes such as finding missing persons. Locating a missing minor was that intended use. The police press release called it the system's first genuine positive hit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Frankfurt am Main, Germany", "deployer_country": "Germany" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The Bahnhofsviertel Videoschutzanlage's AI performs real-time biometric remote identification. The Hessian police law amended in February 2025 allows this for narrow purposes such as finding missing persons. Locating a missing minor was that intended use. The police press release called it the system's first genuine positive hit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-10-09", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-16-7e48", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/; https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://de.wikipedia.org/wiki/Polizeipräsidium_Frankfurt_am_Main", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A missing-person report with concrete signs of danger to life (a farewell letter) and indications of the person's whereabouts inside the camera-covered district", "A legal basis, the Hessian police law amended in February 2025, plus a police-president emergency order confirmed by a judge. These let the girl's photo be entered into the system", "An AI-assisted camera network already running in the Bahnhofsviertel since July 2025, with a staffed Video-Operation-Center. Patrols close enough to verify and act on a match the same evening" ], "must_hold": [ "The person passes in front of cameras suitable for automated recognition. netzpolitik.org counted 19 such cameras among the district's 29", "A human operator and a patrol verify the automated match before anyone is taken into custody", "The search stays within the narrow legal purposes the law allows, which Bild lists as missing persons, kidnapping victims and victims of human trafficking" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Suitable cameras must cover the person’s route. Operators and police patrols must verify the match, and the search needs a lawful basis." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-16-7e48", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/; https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://de.wikipedia.org/wiki/Polizeipräsidium_Frankfurt_am_Main", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing and missing-person search", "segment": "Public safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main, reproduced by Heßberger K (2025). Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel. Rhein Main Verlag, 16 October 2025; the page is marked 'Text: PM PP Frankfurt', i.e. the police press release text.", "title": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel", "url": "https://www.rheinmainverlag.de/2025/10/16/kuenstliche-intelligenz-lokalisiert-16-jaehrige-vermisste-im-frankfurter-bahnhofsviertel/", "date": "2025-10-16", "written_by": "deployer", "quote": "Bereits am 9. Oktober, nur einen Tag nach der Vermisstenmeldung, wurde in den späten Abendstunden im Video-Operation-Center der Frankfurter Polizei eine automatisierte Treffermeldung im Bereich der Taunusstraße angezeigt." }, { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main (2025). POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen. Presseportal, 16 October 2025. The page returned HTTP 503 on 20 October 2026; its title was read on the OECD Monitor entry and its text in the Rhein Main Verlag reproduction cited above.", "title": "POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen", "url": "https://www.presseportal.de/blaulicht/pm/4970/6138810", "date": "2025-10-16", "written_by": "deployer", "quote": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen" }, { "kind": "news report", "citation": "Euler R (2025). Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI. Frankfurter Allgemeine Zeitung, 16 October 2025, 15:42; paywalled beyond the opening paragraphs.", "title": "Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI", "url": "https://www.faz.net/aktuell/rhein-main/frankfurt/frankfurter-polizei-findet-vermisstes-maedchen-mithilfe-von-ki-110735027.html", "date": "2025-10-16", "written_by": "independent", "quote": "Mithilfe künstlicher Intelligenz hat die Polizei in der vergangenen Woche eine vermisste Sechzehnjährige im Frankfurter Bahnhofsviertel lokalisiert und in Obhut genommen." }, { "kind": "news report", "citation": "Pfeiffer-Goldmann D (2025). KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden. Frankfurter Neue Presse, 17 October 2025, 14:10; the OECD Monitor lists the same URL under the earlier headline 'KI in Frankfurt erstmals erfolgreich bei Suche nach Mensch per Videoüberwachung'. Quotes police spokesman Patrick Rüter and Interior Minister Roman Poseck.", "title": "KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden", "url": "https://www.fnp.de/frankfurt/ki-technik-hilft-polizei-in-frankfurt-bei-suche-nach-vermisster-93990652.html", "date": "2025-10-17", "written_by": "independent", "quote": "Einen Tag später habe die KI automatisch einen Treffer in der Taunusstraße gemeldet. Ein Videooperator habe das überprüft" }, { "kind": "news report", "citation": "Schröder D (2025). KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt. Bild, 17 October 2025; the quoted sentence is attributed to Police President Stefan Müller.", "title": "KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt", "url": "https://www.bild.de/regional/frankfurt/ki-rettet-vermisstes-maedchen-16-aus-drogen-sumpf-von-frankfurt-68f0b438672b2f7280fac3b4", "date": "2025-10-17", "written_by": "independent", "quote": "Der erste echte positive Treffer zeigt eindrucksvoll, wie uns auf Basis des neuen Polizeigesetzes modernste Technik zuverlässig und erfolgreich bei der Abwehr von Gefahren für Leib und Leben hilft." }, { "kind": "news report", "citation": "Schwarzbeck M (2026). Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht. netzpolitik.org, 22 April 2026; reports 29 cameras in the Bahnhofsviertel, 19 of them usable for automated face recognition, a low double-digit number of persons searched since July 2025, and this case as the only success.", "title": "Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "date": "2026-04-22", "written_by": "independent", "quote": "Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde" }, { "kind": "news report", "citation": "Kissel L, Pfeuffer-Rooschüz L (2026). Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI. hessenschau.de, 25 March 2026; cites this case as the pilot's one reported success and carries criticism from the Chaos Computer Club and the Hessian data protection commissioner.", "title": "Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI", "url": "https://www.hessenschau.de/gesellschaft/hessen-erfand-den-datenschutz--und-ist-heute-vorreiter-bei-videoueberwachung-mit-ki-v1,erfindung-datenschutz-hessen-100.html", "date": "2026-03-25", "written_by": "independent", "quote": "Diese Anlagen können von einem Hilfsmittel zu einem Herrschaftsmittel werden" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0149-source-0001", "AIBD-0149-source-0002", "AIBD-0149-source-0003", "AIBD-0149-source-0004", "AIBD-0149-source-0005", "AIBD-0149-source-0006", "AIBD-0149-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main, reproduced by Heßberger K (2025). Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel. Rhein Main Verlag, 16 October 2025; the page is marked 'Text: PM PP Frankfurt', i.e. the police press release text.", "title": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Frankfurter Bahnhofsviertel", "url": "https://www.rheinmainverlag.de/2025/10/16/kuenstliche-intelligenz-lokalisiert-16-jaehrige-vermisste-im-frankfurter-bahnhofsviertel/", "date": "2025-10-16", "written_by": "deployer", "quote": "Bereits am 9. Oktober, nur einen Tag nach der Vermisstenmeldung, wurde in den späten Abendstunden im Video-Operation-Center der Frankfurter Polizei eine automatisierte Treffermeldung im Bereich der Taunusstraße angezeigt." }, { "kind": "press release", "citation": "Polizeipräsidium Frankfurt am Main (2025). POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen. Presseportal, 16 October 2025. The page returned HTTP 503 on 20 October 2026; its title was read on the OECD Monitor entry and its text in the Rhein Main Verlag reproduction cited above.", "title": "POL-F: 251016-1068 Frankfurt-Bahnhofsviertel: Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen", "url": "https://www.presseportal.de/blaulicht/pm/4970/6138810", "date": "2025-10-16", "written_by": "deployer", "quote": "Künstliche Intelligenz lokalisiert 16-jährige Vermisste im Bahnhofsviertel - Mädchen durch Polizisten in Obhut genommen" }, { "kind": "news report", "citation": "Euler R (2025). Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI. Frankfurter Allgemeine Zeitung, 16 October 2025, 15:42; paywalled beyond the opening paragraphs.", "title": "Polizei Frankfurt findet vermisstes Mädchen mithilfe von KI", "url": "https://www.faz.net/aktuell/rhein-main/frankfurt/frankfurter-polizei-findet-vermisstes-maedchen-mithilfe-von-ki-110735027.html", "date": "2025-10-16", "written_by": "independent", "quote": "Mithilfe künstlicher Intelligenz hat die Polizei in der vergangenen Woche eine vermisste Sechzehnjährige im Frankfurter Bahnhofsviertel lokalisiert und in Obhut genommen." }, { "kind": "news report", "citation": "Pfeiffer-Goldmann D (2025). KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden. Frankfurter Neue Presse, 17 October 2025, 14:10; the OECD Monitor lists the same URL under the earlier headline 'KI in Frankfurt erstmals erfolgreich bei Suche nach Mensch per Videoüberwachung'. Quotes police spokesman Patrick Rüter and Interior Minister Roman Poseck.", "title": "KI-Einsatz in Frankfurt erstmals erfolgreich – vermisstes Mädchen wiedergefunden", "url": "https://www.fnp.de/frankfurt/ki-technik-hilft-polizei-in-frankfurt-bei-suche-nach-vermisster-93990652.html", "date": "2025-10-17", "written_by": "independent", "quote": "Einen Tag später habe die KI automatisch einen Treffer in der Taunusstraße gemeldet. Ein Videooperator habe das überprüft" }, { "kind": "news report", "citation": "Schröder D (2025). KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt. Bild, 17 October 2025; the quoted sentence is attributed to Police President Stefan Müller.", "title": "KI rettet vermisstes Mädchen (16) aus Drogen-Sumpf von Frankfurt", "url": "https://www.bild.de/regional/frankfurt/ki-rettet-vermisstes-maedchen-16-aus-drogen-sumpf-von-frankfurt-68f0b438672b2f7280fac3b4", "date": "2025-10-17", "written_by": "independent", "quote": "Der erste echte positive Treffer zeigt eindrucksvoll, wie uns auf Basis des neuen Polizeigesetzes modernste Technik zuverlässig und erfolgreich bei der Abwehr von Gefahren für Leib und Leben hilft." }, { "kind": "news report", "citation": "Schwarzbeck M (2026). Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht. netzpolitik.org, 22 April 2026; reports 29 cameras in the Bahnhofsviertel, 19 of them usable for automated face recognition, a low double-digit number of persons searched since July 2025, and this case as the only success.", "title": "Rotlichtviertel Frankfurt am Main: Hier analysiert die Polizei jedes Gesicht", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/", "date": "2026-04-22", "written_by": "independent", "quote": "Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde" }, { "kind": "news report", "citation": "Kissel L, Pfeuffer-Rooschüz L (2026). Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI. hessenschau.de, 25 March 2026; cites this case as the pilot's one reported success and carries criticism from the Chaos Computer Club and the Hessian data protection commissioner.", "title": "Hessen erfand den Datenschutz – und ist heute Vorreiter bei Videoüberwachung mit KI", "url": "https://www.hessenschau.de/gesellschaft/hessen-erfand-den-datenschutz--und-ist-heute-vorreiter-bei-videoueberwachung-mit-ki-v1,erfindung-datenschutz-hessen-100.html", "date": "2026-03-25", "written_by": "independent", "quote": "Diese Anlagen können von einem Hilfsmittel zu einem Herrschaftsmittel werden" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Frankfurt police found a missing 16-year-old in crisis within a day via a court-backed AI face search of Bahnhofsviertel cameras.", "account": "On 8 October 2025 a 16-year-old was reported missing after failing to return to the psychiatric facility in Hesse where she lived. A farewell letter pointed to an acute psychological crisis. Indications placed her in Frankfurt's Bahnhofsviertel. Citing imminent danger, Frankfurt's police president issued an emergency order, confirmed by a judge. So her photograph could be entered into the AI-assisted camera system running in the district since July 2025. It operates under the Hessian police law amended in February 2025. Late on 9 October the system flagged a match on Taunusstraße. A video operator checked it and a patrol confirmed it. Officers took the girl into protective custody and returned her to the child and adolescent psychiatric facility." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The software matched a photograph against surveillance video and sent an alert for human verification." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The facial-recognition component compared images with the BKA’s database of facial images." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Not reported; none of the sources names a vendor, product or version for the facial-recognition component, and netzpolitik.org (April 2026) says only that it matches against the BKA facial-recognition database" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI-assisted video analysis with real-time biometric remote identification in the Frankfurt Bahnhofsviertel police video surveillance system (Videoschutzanlage), monitored from the police Video-Operation-Center" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Frankfurt police found a missing 16-year-old who had left a farewell letter on Taunusstraße within a day of her disappearance, after a judge-confirmed emergency order let the Bahnhofsviertel camera system's AI search for her face", "headline": "Frankfurt camera AI finds a missing 16-year-old within a day" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a26560751e2527e5a9f380a895ed0cfdf3dd7b6dd096325e1fc47c76618cf190" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 8 October 2025 a 16-year-old was reported missing after failing to return to the psychiatric facility in Hesse where she lived. A farewell letter pointed to an acute psychological crisis. Indications placed her in Frankfurt's Bahnhofsviertel. Citing imminent danger, Frankfurt's police president issued an emergency order, confirmed by a judge. So her photograph could be entered into the AI-assisted camera system running in the district since July 2025. It operates under the Hessian police law amended in February 2025. Late on 9 October the system flagged a match on Taunusstraße. A video operator checked it and a patrol confirmed it. Officers took the girl into protective custody and returned her to the child and adolescent psychiatric facility.", "replication": { "produced_by": [ "A missing-person report with concrete signs of danger to life (a farewell letter) and indications of the person's whereabouts inside the camera-covered district", "A legal basis, the Hessian police law amended in February 2025, plus a police-president emergency order confirmed by a judge. These let the girl's photo be entered into the system", "An AI-assisted camera network already running in the Bahnhofsviertel since July 2025, with a staffed Video-Operation-Center. Patrols close enough to verify and act on a match the same evening" ], "must_hold": [ "The person passes in front of cameras suitable for automated recognition. netzpolitik.org counted 19 such cameras among the district's 29", "A human operator and a patrol verify the automated match before anyone is taken into custody", "The search stays within the narrow legal purposes the law allows, which Bild lists as missing persons, kidnapping victims and victims of human trafficking" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 0, "record_mapped": 33, "not_assessed": 58, "complete": false, "by_state": { "value_present": 33, "not_reported": 0, "unresolved": 0, "not_assessed": 58, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0122", "case_url": "records/AIBD-0122.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "CBP's model was built to identify potentially suspicious patterns in vehicle border-crossing histories (DHS AI Roadmap 2024). It flags vehicles for secondary review at ports of entry. The San Ysidro seizure was that use, not a side effect." }, { "label": "AI contribution", "value": "The model's contribution was the referral. DHS says it identified a suspicious pattern in the car's border-crossing history and flagged it for review. Mayorkas said the anomaly was identified in 1.4 seconds. The discovery of the narcotics and the arrest were done by officers in secondary review. No source states whether the car would have been referred without the flag. The model's counterfactual contribution is thus asserted by DHS, not demonstrated. The Monitor entry also bundles Mayorkas's claim that AI age-progression imagery has recovered abducted children. That claim names no case, date, or child." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0122", "title": "CBP machine-learning flag at San Ysidro leads to 75 kg narcotics seizure and arrest (2023)", "summary": "CBP ML model flagged a car's crossing history at San Ysidro in 2023; review found 75 kg of narcotics and the driver was arrested.", "date_observed": "2023-11-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2023 a car driving north from Mexico approached the Point of Entry at San Ysidro. A U.S. Customs and Border Protection (CBP) machine-learning model identified a suspicious pattern in its border-crossing history and flagged it for review. The review found 75 kilograms of narcotics hidden in the vehicle and led to the driver's arrest (DHS AI page, 27 November 2023). At the Axios AI+ Summit on 28 November 2023 Secretary Alejandro Mayorkas described a matching case. The Office of Field Operations model flagged a “truck” in 1.4 seconds that held about 75 kilograms of narcotics. DHS's 2024 AI Roadmap repeats it: a secondary-review flag yielding over 75 kg of drugs. No source names the drug or the seizure date.", "category": "harm prevented", "unexpected": false, "purpose": "CBP's model was built to identify potentially suspicious patterns in vehicle border-crossing histories (DHS AI Roadmap 2024). It flags vehicles for secondary review at ports of entry. The San Ysidro seizure was that use, not a side effect.", "system": "U.S. Customs and Border Protection (Office of Field Operations) machine-learning model that scores vehicle border-crossing histories for suspicious patterns; not named in any source", "model_version": null, "developer": null, "deployer": "U.S. Customs and Border Protection, Office of Field Operations (U.S. Department of Homeland Security)", "location": "San Ysidro, United States", "domain": "Border security and law enforcement", "segment": "Narcotics interdiction at a land port of entry", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2023-11-28-9430" }, "ratings": [ { "group": "societal", "depth": 3, "reason": "DHS states that 75 kilograms of narcotics hidden in the car were found and the driver arrested. That load did not reach U.S. streets. The drug is not named in any source, so a life-saving claim is not supported." }, { "group": "organizational", "depth": 2, "reason": "CBP's Office of Field Operations obtained a seizure and an arrest from a flag that Mayorkas said took 1.4 seconds. DHS leads both its AI page and its 2024 AI Roadmap with the case as its example of AI in use." } ], "ai_role": { "status": "assessed", "statement": "The model's contribution was the referral. DHS says it identified a suspicious pattern in the car's border-crossing history and flagged it for review. Mayorkas said the anomaly was identified in 1.4 seconds. The discovery of the narcotics and the arrest were done by officers in secondary review. No source states whether the car would have been referred without the flag. The model's counterfactual contribution is thus asserted by DHS, not demonstrated. The Monitor entry also bundles Mayorkas's claim that AI age-progression imagery has recovered abducted children. That claim names no case, date, or child." }, "evidence": [ { "kind": "press release", "citation": "U.S. Department of Homeland Security (2023). Artificial Intelligence at DHS. dhs.gov (page last updated 27 November 2023; archived 1 December 2023 at web.archive.org/web/20231201224758/https://www.dhs.gov/ai).", "title": "Artificial Intelligence at DHS", "url": "https://www.dhs.gov/ai", "date": "2023-11-27", "written_by": "deployer", "quote": "flagged it for further review, which revealed 75 kilograms of narcotics hidden in the vehicle and led to the arrest of the driver" }, { "kind": "news report", "citation": "Giaritelli, A. (2023). Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children. Washington Examiner.", "title": "Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children", "url": "https://www.washingtonexaminer.com/policy/technology/mayorkas-dhs-ai-fentanyl-border-kidnapped-children", "date": "2023-11-28", "written_by": "independent", "quote": "in 1.4 seconds, we identified an anomaly of a particular truck that ended up having about 75 kilograms of narcotics in it" }, { "kind": "news report", "citation": "Kelley, A. (2023). DHS aims to lead in defense against 'adversarial' AI. Nextgov/FCW.", "title": "DHS aims to lead in defense against 'adversarial' AI", "url": "https://www.nextgov.com/artificial-intelligence/2023/11/dhs-aims-lead-defense-against-adversarial-ai/392321/", "date": "2023-11-28", "written_by": "independent", "quote": "AI systems are currently aiding DHS operations in border patrol efforts and combating drug trafficking and creating age progression estimations of missing children" }, { "kind": "report", "citation": "U.S. Department of Homeland Security (2024). Artificial Intelligence Roadmap 2024. dhs.gov (PDF).", "title": "Department of Homeland Security Artificial Intelligence Roadmap 2024", "url": "https://www.dhs.gov/sites/default/files/2024-03/24_0315_ocio_roadmap_artificialintelligence-ciov3-signed-508.pdf", "date": null, "written_by": "deployer", "quote": "CBP recently used the model to flag a car for secondary review, which yielded the discovery of over 75 kgs of drugs hidden in the automobile" } ], "replication": { "produced_by": [ "A CBP machine-learning model scoring vehicle border-crossing histories was running on traffic at the San Ysidro Point of Entry", "The model's flag reached officers in time to send the car to further (secondary) review before it left the port", "Officers physically searched the car, found the 75 kilograms of narcotics and arrested the driver" ], "must_hold": [ "The crossing-history pattern the model keys on must actually distinguish smuggling loads. No source gives hit rates or false-positive rates", "Officers must have the discretion and capacity to act on a flag within normal crossing processing", "The seizure is documented only by DHS and the Secretary. An independent CBP release or court filing giving the date, drug and quantity has not been found" ], "echoes": [] }, "paired_incident": null, "headline": "CBP machine-learning flag at San Ysidro finds 75 kg of narcotics", "metric": "review found 75 kg of narcotics and the driver was arrested", "comparator": null, "later": { "status": "unknown", "date": null, "note": "DHS's March 2024 AI Roadmap restates the same seizure as an example; no later development, seizure date or drug type was found.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-11-28-9430", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the deployer's report and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0122", "catalog_version": "1.0.0", "source_fingerprint": "b3d5594ab9851e2a212fa5733953fb908641fb42e802ba3b5b44a1e8bb9fce8f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0122-source-0001", "AIBD-0122-source-0002", "AIBD-0122-source-0003", "AIBD-0122-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "U.S. Customs and Border Protection, Office of Field Operations (U.S. Department of Homeland Security)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The model's contribution was the referral. DHS says it identified a suspicious pattern in the car's border-crossing history and flagged it for review. Mayorkas said the anomaly was identified in 1.4 seconds. The discovery of the narcotics and the arrest were done by officers in secondary review. No source states whether the car would have been referred without the flag. The model's counterfactual contribution is thus asserted by DHS, not demonstrated. The Monitor entry also bundles Mayorkas's claim that AI age-progression imagery has recovered abducted children. That claim names no case, date, or child." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The model's contribution was the referral. DHS says it identified a suspicious pattern in the car's border-crossing history and flagged it for review. Mayorkas said the anomaly was identified in 1.4 seconds. The discovery of the narcotics and the arrest were done by officers in secondary review. No source states whether the car would have been referred without the flag. The model's counterfactual contribution is thus asserted by DHS, not demonstrated. The Monitor entry also bundles Mayorkas's claim that AI age-progression imagery has recovered abducted children. That claim names no case, date, or child." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "DHS states that 75 kilograms of narcotics hidden in the car were found and the driver arrested. That load did not reach U.S. streets. 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "DHS states that 75 kilograms of narcotics hidden in the car were found and the driver arrested. That load did not reach U.S. streets. The drug is not named in any source, so a life-saving claim is not supported." }, { "group": "organizational", "depth": 2, "reason": "CBP's Office of Field Operations obtained a seizure and an arrest from a flag that Mayorkas said took 1.4 seconds. DHS leads both its AI page and its 2024 AI Roadmap with the case as its example of AI in use." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "DHS states that 75 kilograms of narcotics hidden in the car were found and the driver arrested. That load did not reach U.S. streets. 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DHS leads both its AI page and its 2024 AI Roadmap with the case as its example of AI in use." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": 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Roadmap restates the same seizure as an example; no later development, seizure date or drug type was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0122" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2023 a car driving north from Mexico approached the Point of Entry at San Ysidro. A U.S. Customs and Border Protection (CBP) machine-learning model identified a suspicious pattern in its border-crossing history and flagged it for review. The review found 75 kilograms of narcotics hidden in the vehicle and led to the driver's arrest (DHS AI page, 27 November 2023). At the Axios AI+ Summit on 28 November 2023 Secretary Alejandro Mayorkas described a matching case. The Office of Field Operations model flagged a “truck” in 1.4 seconds that held about 75 kilograms of narcotics. DHS's 2024 AI Roadmap repeats it: a secondary-review flag yielding over 75 kg of drugs. No source names the drug or the seizure date.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "DHS states that 75 kilograms of narcotics hidden in the car were found and the driver arrested. That load did not reach U.S. streets. The drug is not named in any source, so a life-saving claim is not supported." }, { "group": "organizational", "depth": 2, "reason": "CBP's Office of Field Operations obtained a seizure and an arrest from a flag that Mayorkas said took 1.4 seconds. DHS leads both its AI page and its 2024 AI Roadmap with the case as its example of AI in use." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "CBP's model was built to identify potentially suspicious patterns in vehicle border-crossing histories (DHS AI Roadmap 2024). It flags vehicles for secondary review at ports of entry. The San Ysidro seizure was that use, not a side effect." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "San Ysidro, United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "CBP's model was built to identify potentially suspicious patterns in vehicle border-crossing histories (DHS AI Roadmap 2024). It flags vehicles for secondary review at ports of entry. The San Ysidro seizure was that use, not a side effect." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-11-28", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-11-28-9430", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A CBP machine-learning model scoring vehicle border-crossing histories was running on traffic at the San Ysidro Point of Entry", "The model's flag reached officers in time to send the car to further (secondary) review before it left the port", "Officers physically searched the car, found the 75 kilograms of narcotics and arrested the driver" ], "must_hold": [ "The crossing-history pattern the model keys on must actually distinguish smuggling loads. No source gives hit rates or false-positive rates", "Officers must have the discretion and capacity to act on a flag within normal crossing processing", "The seizure is documented only by DHS and the Secretary. An independent CBP release or court filing giving the date, drug and quantity has not been found" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-11-28-9430", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Border security and law enforcement", "segment": "Narcotics interdiction at a land port of entry" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Department of Homeland Security (2023). Artificial Intelligence at DHS. dhs.gov (page last updated 27 November 2023; archived 1 December 2023 at web.archive.org/web/20231201224758/https://www.dhs.gov/ai).", "title": "Artificial Intelligence at DHS", "url": "https://www.dhs.gov/ai", "date": "2023-11-27", "written_by": "deployer", "quote": "flagged it for further review, which revealed 75 kilograms of narcotics hidden in the vehicle and led to the arrest of the driver" }, { "kind": "news report", "citation": "Giaritelli, A. (2023). Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children. Washington Examiner.", "title": "Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children", "url": "https://www.washingtonexaminer.com/policy/technology/mayorkas-dhs-ai-fentanyl-border-kidnapped-children", "date": "2023-11-28", "written_by": "independent", "quote": "in 1.4 seconds, we identified an anomaly of a particular truck that ended up having about 75 kilograms of narcotics in it" }, { "kind": "news report", "citation": "Kelley, A. (2023). DHS aims to lead in defense against 'adversarial' AI. Nextgov/FCW.", "title": "DHS aims to lead in defense against 'adversarial' AI", "url": "https://www.nextgov.com/artificial-intelligence/2023/11/dhs-aims-lead-defense-against-adversarial-ai/392321/", "date": "2023-11-28", "written_by": "independent", "quote": "AI systems are currently aiding DHS operations in border patrol efforts and combating drug trafficking and creating age progression estimations of missing children" }, { "kind": "report", "citation": "U.S. Department of Homeland Security (2024). Artificial Intelligence Roadmap 2024. dhs.gov (PDF).", "title": "Department of Homeland Security Artificial Intelligence Roadmap 2024", "url": "https://www.dhs.gov/sites/default/files/2024-03/24_0315_ocio_roadmap_artificialintelligence-ciov3-signed-508.pdf", "written_by": "deployer", "quote": "CBP recently used the model to flag a car for secondary review, which yielded the discovery of over 75 kgs of drugs hidden in the automobile" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0122-source-0001", "AIBD-0122-source-0002", "AIBD-0122-source-0003", "AIBD-0122-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Department of Homeland Security (2023). Artificial Intelligence at DHS. dhs.gov (page last updated 27 November 2023; archived 1 December 2023 at web.archive.org/web/20231201224758/https://www.dhs.gov/ai).", "title": "Artificial Intelligence at DHS", "url": "https://www.dhs.gov/ai", "date": "2023-11-27", "written_by": "deployer", "quote": "flagged it for further review, which revealed 75 kilograms of narcotics hidden in the vehicle and led to the arrest of the driver" }, { "kind": "news report", "citation": "Giaritelli, A. (2023). Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children. Washington Examiner.", "title": "Mayorkas: DHS using AI to catch fentanyl at border, track down kidnapped children", "url": "https://www.washingtonexaminer.com/policy/technology/mayorkas-dhs-ai-fentanyl-border-kidnapped-children", "date": "2023-11-28", "written_by": "independent", "quote": "in 1.4 seconds, we identified an anomaly of a particular truck that ended up having about 75 kilograms of narcotics in it" }, { "kind": "news report", "citation": "Kelley, A. (2023). DHS aims to lead in defense against 'adversarial' AI. Nextgov/FCW.", "title": "DHS aims to lead in defense against 'adversarial' AI", "url": "https://www.nextgov.com/artificial-intelligence/2023/11/dhs-aims-lead-defense-against-adversarial-ai/392321/", "date": "2023-11-28", "written_by": "independent", "quote": "AI systems are currently aiding DHS operations in border patrol efforts and combating drug trafficking and creating age progression estimations of missing children" }, { "kind": "report", "citation": "U.S. Department of Homeland Security (2024). Artificial Intelligence Roadmap 2024. dhs.gov (PDF).", "title": "Department of Homeland Security Artificial Intelligence Roadmap 2024", "url": "https://www.dhs.gov/sites/default/files/2024-03/24_0315_ocio_roadmap_artificialintelligence-ciov3-signed-508.pdf", "written_by": "deployer", "quote": "CBP recently used the model to flag a car for secondary review, which yielded the discovery of over 75 kgs of drugs hidden in the automobile" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "CBP ML model flagged a car's crossing history at San Ysidro in 2023; review found 75 kg of narcotics and the driver was arrested.", "account": "In 2023 a car driving north from Mexico approached the Point of Entry at San Ysidro. A U.S. Customs and Border Protection (CBP) machine-learning model identified a suspicious pattern in its border-crossing history and flagged it for review. The review found 75 kilograms of narcotics hidden in the vehicle and led to the driver's arrest (DHS AI page, 27 November 2023). At the Axios AI+ Summit on 28 November 2023 Secretary Alejandro Mayorkas described a matching case. The Office of Field Operations model flagged a “truck” in 1.4 seconds that held about 75 kilograms of narcotics. DHS's 2024 AI Roadmap repeats it: a secondary-review flag yielding over 75 kg of drugs. No source names the drug or the seizure date." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "U.S. Customs and Border Protection (Office of Field Operations) machine-learning model that scores vehicle border-crossing histories for suspicious patterns; not named in any source" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "CBP machine-learning flag at San Ysidro leads to 75 kg narcotics seizure and arrest (2023)", "headline": "CBP machine-learning flag at San Ysidro finds 75 kg of narcotics" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "b3d5594ab9851e2a212fa5733953fb908641fb42e802ba3b5b44a1e8bb9fce8f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2023 a car driving north from Mexico approached the Point of Entry at San Ysidro. A U.S. Customs and Border Protection (CBP) machine-learning model identified a suspicious pattern in its border-crossing history and flagged it for review. The review found 75 kilograms of narcotics hidden in the vehicle and led to the driver's arrest (DHS AI page, 27 November 2023). At the Axios AI+ Summit on 28 November 2023 Secretary Alejandro Mayorkas described a matching case. The Office of Field Operations model flagged a “truck” in 1.4 seconds that held about 75 kilograms of narcotics. DHS's 2024 AI Roadmap repeats it: a secondary-review flag yielding over 75 kg of drugs. No source names the drug or the seizure date.", "replication": { "produced_by": [ "A CBP machine-learning model scoring vehicle border-crossing histories was running on traffic at the San Ysidro Point of Entry", "The model's flag reached officers in time to send the car to further (secondary) review before it left the port", "Officers physically searched the car, found the 75 kilograms of narcotics and arrested the driver" ], "must_hold": [ "The crossing-history pattern the model keys on must actually distinguish smuggling loads. No source gives hit rates or false-positive rates", "Officers must have the discretion and capacity to act on a flag within normal crossing processing", "The seizure is documented only by DHS and the Secretary. An independent CBP release or court filing giving the date, drug and quantity has not been found" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0233", "case_url": "records/AIBD-0233.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Build networks of links among people with criminal records to propose probable members of a group tied to a specific crime. This reduces the case files prosecutors must review." }, { "label": "AI contribution", "value": "The deploying public body credits the AI with the identification of probable participants, complemented by prosecutors' and police work and victim identification. T13 adds the mechanism (link search from three arrested members). No court document or independent evaluation of the model's accuracy is among the sources. The Fiscalía is both the source and the beneficiary." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0233", "title": "Chile's “Fiscal Heredia” AI model may have identified a 10-member “encerrona” robbery gang; a court accredits 8 robberies", "summary": "In Operación Influencer (11 May 2023) prosecutors and PDI detained a gang of 10 aged 15–22 behind eight accredited violent robberies; the Fiscalía credits the university-built AI model Fiscal Heredia.", "date_observed": "2023-05-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Fiscalía de Chile's regional news release describes a joint investigation by Focos Occidente and the PDI's Brigada de Investigación Criminal. In the early hours of Thursday 11 May 2023 it led to 30 search warrants across Maipú, Pudahuel, Cerro Navia and other comunas. There were 17 detentions, ten linked to “encerronas” (robbery with intimidation or violence) whose perpetrators posted their robberies on TikTok. The release says they were identified through the software Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial). It is an AI model developed by several universities and incorporated into the Ministerio Público in March 2023. It proposes probable participants from prosecution and police records for prosecutors and police to work up. The Noveno Juzgado de Garantía accredited the eight robberies. Four adults went to preventive detention and four adolescents remained in provisional internment. T13 quotes Fiscal Nacional Ángel Valencia saying the algorithm accelerates criminal analysis. It reports that after three members were arrested the AI searched their links to raise persons of interest later identified by victims. The OECD AI Incidents Monitor records the case.", "category": "benefit created", "unexpected": false, "purpose": "Build networks of links among people with criminal records to propose probable members of a group tied to a specific crime. This reduces the case files prosecutors must review.", "system": "Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial)", "model_version": null, "developer": "Several universities (not named in the sources)", "deployer": "Ministerio Público de Chile (Fiscalía Regional Occidente, SACFI) with the Policía de Investigaciones", "location": null, "domain": "Criminal investigation / organized robbery", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 3, "reason": "A gang responsible for at least eight accredited violent robberies was detained and formalized, with four adults in preventive detention, per the prosecutor's office." }, { "group": "organizational", "depth": 2, "reason": "Prosecutors state the tool replaces review of hundreds of case files with a short list of proposed links." } ], "ai_role": { "status": "assessed", "statement": "The deploying public body credits the AI with the identification of probable participants, complemented by prosecutors' and police work and victim identification. T13 adds the mechanism (link search from three arrested members). No court document or independent evaluation of the model's accuracy is among the sources. The Fiscalía is both the source and the beneficiary." }, "evidence": [ { "kind": "press release", "citation": "Fiscalía de Chile, noticias regionales, 17 May 2023", "title": "“Operación Influencer”: Uso de IA permitió desarticular a banda de adolescentes que efectuó al menos 8 “encerronas” en Zona Occidente", "url": "https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "date": "2023-05-17", "written_by": "deployer", "quote": "Su identificación fue realizada gracias al uso del software denominado Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial), que es un modelo de IA" }, { "kind": "news report", "citation": "T13 (Teletrece), 12 May 2023", "title": "\"Operación Influencer\": Cómo la Inteligencia Artificial fue clave en la detención de banda dedicada a las encerronas", "url": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023", "date": "2023-05-12", "written_by": "independent", "quote": "El algoritmo acelera el análisis criminal." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2023-05-12-99fe", "title": "AI System Enables Major Criminal Gang Bust in Chile's 'Operación Influencer'", "url": "https://oecd.ai/en/incidents/2023-05-12-99fe", "date": "2023-05-12", "written_by": "independent", "quote": "Chilean authorities used an AI system to analyze criminal data and detect networks, leading to the arrest of 10 individuals involved in violent carjackings and related crimes." } ], "replication": { "produced_by": [ "Linked prosecution, prison and police records (SAF, Gendarmería, Carabineros)", "A few confirmed arrests seeding a link search", "Victim identification to confirm proposed persons of interest" ], "must_hold": [ "Suspects must already appear in the records the model draws on", "Human verification before charging", "Legal basis for cross-agency data linkage" ], "echoes": [] }, "paired_incident": null, "headline": "Fiscal Heredia may have identified a 10-member robbery gang", "metric": "detained a gang of 10 aged 15–22 behind eight accredited violent robberies", "comparator": { "against": "prosecutors manually analyzing hundreds of case folders to find links", "before": "analizar cientos de carpetas para dar con potenciales vínculos", "after": "el sistema realiza un filtro y propone un número acotado de ellos", "basis": "assumed", "quote": "El algoritmo acelera el análisis criminal. Con él, las y los fiscales ya no tendrán que analizar cientos de carpetas para dar con potenciales vínculos, ya que el sistema realiza un filtro y propone un número acotado de ellos, reduciendo el volumen y la complejidad de la búsqueda", "url": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023" }, "later": { "status": "litigated", "date": "2023-05-17", "note": "The Fiscalía's release of 17 May 2023 reports that the court accredited the eight robberies, placed four adults in preventive detention and kept four adolescents in provisional internment.", "quote": "El Tribunal dio por acreditado los 8 robos con modalidad encerronas.", "url": "https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de" }, "value": null, "developer_country": null, "deployer_country": "Chile", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023; https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.fiscaliadechile.cl/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, a registry entry and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0233", "catalog_version": "1.0.0", "source_fingerprint": "ddb3d7fc67e58a23fa2552f7c68b582100dae25b98ad2f8a88a827164b6d5242", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0233-source-0001", "AIBD-0233-source-0002", "AIBD-0233-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Several universities (not named in the sources)", "deployer": "Ministerio Público de Chile (Fiscalía Regional Occidente, SACFI) with the Policía de Investigaciones" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The deploying public body credits the AI with the identification of probable participants, complemented by prosecutors' and police work and victim identification. T13 adds the mechanism (link search from three arrested members). No court document or independent evaluation of the model's accuracy is among the sources. The Fiscalía is both the source and the beneficiary." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The deploying public body credits the AI with the identification of probable participants, complemented by prosecutors' and police work and victim identification. T13 adds the mechanism (link search from three arrested members). No court document or independent evaluation of the model's accuracy is among the sources. The Fiscalía is both the source and the beneficiary." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "A gang responsible for at least eight accredited violent robberies was detained and formalized, with four adults in preventive detention, per the prosecutor's office." }, { "group": "organizational", "depth": 2, "reason": "Prosecutors state the tool replaces review of hundreds of case files with a short list of proposed links." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report, a registry entry and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "A gang responsible for at least eight accredited violent robberies was detained and formalized, with four adults in preventive detention, per the prosecutor's office." }, { "group": "organizational", "depth": 2, "reason": "Prosecutors state the tool replaces review of hundreds of case files with a short list of proposed links." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "A gang responsible for at least eight accredited violent robberies was detained and formalized, with four adults in preventive detention, per the prosecutor's office." }, { "group": "organizational", "depth": 2, "reason": "Prosecutors state the tool replaces review of hundreds of case files with a short list of proposed links." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "detained a gang of 10 aged 15–22 behind eight accredited violent robberies", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2023-05-17", "note": "The Fiscalía's release of 17 May 2023 reports that the court accredited the eight robberies, placed four adults in preventive detention and kept four adolescents in provisional internment.", "quote": "El Tribunal dio por acreditado los 8 robos con modalidad encerronas.", "url": "https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0233" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Fiscalía de Chile's regional news release describes a joint investigation by Focos Occidente and the PDI's Brigada de Investigación Criminal. In the early hours of Thursday 11 May 2023 it led to 30 search warrants across Maipú, Pudahuel, Cerro Navia and other comunas. There were 17 detentions, ten linked to “encerronas” (robbery with intimidation or violence) whose perpetrators posted their robberies on TikTok. The release says they were identified through the software Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial). It is an AI model developed by several universities and incorporated into the Ministerio Público in March 2023. It proposes probable participants from prosecution and police records for prosecutors and police to work up. The Noveno Juzgado de Garantía accredited the eight robberies. Four adults went to preventive detention and four adolescents remained in provisional internment. T13 quotes Fiscal Nacional Ángel Valencia saying the algorithm accelerates criminal analysis. It reports that after three members were arrested the AI searched their links to raise persons of interest later identified by victims. The OECD AI Incidents Monitor records the case.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "A gang responsible for at least eight accredited violent robberies was detained and formalized, with four adults in preventive detention, per the prosecutor's office." }, { "group": "organizational", "depth": 2, "reason": "Prosecutors state the tool replaces review of hundreds of case files with a short list of proposed links." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Build networks of links among people with criminal records to propose probable members of a group tied to a specific crime. This reduces the case files prosecutors must review." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer_country": "Chile" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Build networks of links among people with criminal records to propose probable members of a group tied to a specific crime. This reduces the case files prosecutors must review." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-05-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023; https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.fiscaliadechile.cl/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Linked prosecution, prison and police records (SAF, Gendarmería, Carabineros)", "A few confirmed arrests seeding a link search", "Victim identification to confirm proposed persons of interest" ], "must_hold": [ "Suspects must already appear in the records the model draws on", "Human verification before charging", "Legal basis for cross-agency data linkage" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023; https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.fiscaliadechile.cl/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Criminal investigation / organized robbery", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Fiscalía de Chile, noticias regionales, 17 May 2023", "title": "“Operación Influencer”: Uso de IA permitió desarticular a banda de adolescentes que efectuó al menos 8 “encerronas” en Zona Occidente", "url": "https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "date": "2023-05-17", "written_by": "deployer", "quote": "Su identificación fue realizada gracias al uso del software denominado Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial), que es un modelo de IA" }, { "kind": "news report", "citation": "T13 (Teletrece), 12 May 2023", "title": "\"Operación Influencer\": Cómo la Inteligencia Artificial fue clave en la detención de banda dedicada a las encerronas", "url": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023", "date": "2023-05-12", "written_by": "independent", "quote": "El algoritmo acelera el análisis criminal." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2023-05-12-99fe", "title": "AI System Enables Major Criminal Gang Bust in Chile's 'Operación Influencer'", "url": "https://oecd.ai/en/incidents/2023-05-12-99fe", "date": "2023-05-12", "written_by": "independent", "quote": "Chilean authorities used an AI system to analyze criminal data and detect networks, leading to the arrest of 10 individuals involved in violent carjackings and related crimes." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0233-source-0001", "AIBD-0233-source-0002", "AIBD-0233-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Fiscalía de Chile, noticias regionales, 17 May 2023", "title": "“Operación Influencer”: Uso de IA permitió desarticular a banda de adolescentes que efectuó al menos 8 “encerronas” en Zona Occidente", "url": "https://www.fiscaliadechile.cl/actualidad/noticias/regionales/operacion-influencer-uso-de-ia-permitio-desarticular-banda-de", "date": "2023-05-17", "written_by": "deployer", "quote": "Su identificación fue realizada gracias al uso del software denominado Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial), que es un modelo de IA" }, { "kind": "news report", "citation": "T13 (Teletrece), 12 May 2023", "title": "\"Operación Influencer\": Cómo la Inteligencia Artificial fue clave en la detención de banda dedicada a las encerronas", "url": "https://www.t13.cl/noticia/nacional/operacion-influencer-inteligencia-artificial-clave-detencion-banda-encerronas-12-5-2023", "date": "2023-05-12", "written_by": "independent", "quote": "El algoritmo acelera el análisis criminal." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2023-05-12-99fe", "title": "AI System Enables Major Criminal Gang Bust in Chile's 'Operación Influencer'", "url": "https://oecd.ai/en/incidents/2023-05-12-99fe", "date": "2023-05-12", "written_by": "independent", "quote": "Chilean authorities used an AI system to analyze criminal data and detect networks, leading to the arrest of 10 individuals involved in violent carjackings and related crimes." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "In Operación Influencer (11 May 2023) prosecutors and PDI detained a gang of 10 aged 15–22 behind eight accredited violent robberies; the Fiscalía credits the university-built AI model Fiscal Heredia.", "account": "The Fiscalía de Chile's regional news release describes a joint investigation by Focos Occidente and the PDI's Brigada de Investigación Criminal. In the early hours of Thursday 11 May 2023 it led to 30 search warrants across Maipú, Pudahuel, Cerro Navia and other comunas. There were 17 detentions, ten linked to “encerronas” (robbery with intimidation or violence) whose perpetrators posted their robberies on TikTok. The release says they were identified through the software Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial). It is an AI model developed by several universities and incorporated into the Ministerio Público in March 2023. It proposes probable participants from prosecution and police records for prosecutors and police to work up. The Noveno Juzgado de Garantía accredited the eight robberies. Four adults went to preventive detention and four adolescents remained in provisional internment. T13 quotes Fiscal Nacional Ángel Valencia saying the algorithm accelerates criminal analysis. It reports that after three members were arrested the AI searched their links to raise persons of interest later identified by victims. The OECD AI Incidents Monitor records the case." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Several universities (not named in the sources)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Chile's “Fiscal Heredia” AI model may have identified a 10-member “encerrona” robbery gang; a court accredits 8 robberies", "headline": "Fiscal Heredia may have identified a 10-member robbery gang" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "ddb3d7fc67e58a23fa2552f7c68b582100dae25b98ad2f8a88a827164b6d5242" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Fiscalía de Chile's regional news release describes a joint investigation by Focos Occidente and the PDI's Brigada de Investigación Criminal. In the early hours of Thursday 11 May 2023 it led to 30 search warrants across Maipú, Pudahuel, Cerro Navia and other comunas. There were 17 detentions, ten linked to “encerronas” (robbery with intimidation or violence) whose perpetrators posted their robberies on TikTok. The release says they were identified through the software Fiscal Heredia (Herramienta para el Estudio del Delito Usando Inteligencia Artificial). It is an AI model developed by several universities and incorporated into the Ministerio Público in March 2023. It proposes probable participants from prosecution and police records for prosecutors and police to work up. The Noveno Juzgado de Garantía accredited the eight robberies. Four adults went to preventive detention and four adolescents remained in provisional internment. T13 quotes Fiscal Nacional Ángel Valencia saying the algorithm accelerates criminal analysis. It reports that after three members were arrested the AI searched their links to raise persons of interest later identified by victims. The OECD AI Incidents Monitor records the case.", "replication": { "produced_by": [ "Linked prosecution, prison and police records (SAF, Gendarmería, Carabineros)", "A few confirmed arrests seeding a link search", "Victim identification to confirm proposed persons of interest" ], "must_hold": [ "Suspects must already appear in the records the model draws on", "Human verification before charging", "Legal basis for cross-agency data linkage" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0228", "case_url": "records/AIBD-0228.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "De-duplicate and validate biometric voter registrations (fingerprints and faces) to remove double, multiple, underage and fake entries from the national register." }, { "label": "AI contribution", "value": "The figures come from INEC's own statements (the deployer) relayed by two Nigerian outlets, not from an audit. INEC's website could not be searched. ABIS's matching method, error rates and the share of invalidations due to biometrics versus business rules are not stated. BusinessDay's critique (registrants evading ABIS; 49.3% of invalidations from two regions) is included as a limit." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0228", "title": "INEC's Automated Biometric Identification System invalidates 2,780,756 ineligible voter registrations (22.6%) before Nigeria's 2023 election", "summary": "INEC chairman Mahmood Yakubu reported on 26 Oct 2022 that ABIS cleaning of 2021–22 registrations identified 2,780,756 (22.6%) double, underage or fake entries, leaving 9,518,188 valid new registrants.", "date_observed": "2022-10-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The date is that of Professor Mahmood Yakubu's address to political parties in Abuja, reproduced by the Nigerian Democratic Report. He said the Continuous Voter Registration data (28 June 2021 to 31 July 2022) was cleaned with the Automated Biometric Identification System (ABIS). This identified 2,780,756 registrations (22.6%) as ineligible and invalidated them. Among them were double or multiple registrants, underage persons and fake registrations failing INEC's business rules. That left 9,518,188 valid post-ABIS registrations; 23 registration officers were identified for sanctions. On 12 September 2022, INEC commissioner Festus Okoye said 1,126,359 of 2,523,458 registrants (28 June 2021 to 14 January 2022) were found invalid and delisted. ABIS processing of later registrations was ongoing (Punch). BusinessDay later argued that some fake and underage registrants evaded ABIS and that invalidation rates were regionally lopsided. The vendor of ABIS is not named in the sources.", "category": "benefit created", "unexpected": false, "purpose": "De-duplicate and validate biometric voter registrations (fingerprints and faces) to remove double, multiple, underage and fake entries from the national register.", "system": "Automated Biometric Identification System (ABIS)", "model_version": null, "developer": "Not named in the sources", "deployer": "Independent National Electoral Commission (INEC), Nigeria", "location": null, "domain": "Electoral integrity / voter registration", "segment": "Government", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 3, "reason": "2,780,756 ineligible entries (22.6% of new registrations) were removed from the register used for the 2023 general election, per INEC's chairman. BusinessDay disputes completeness and regional balance." }, { "group": "organizational", "depth": 2, "reason": "INEC obtained a cleaned register and identified 23 of its own officers for sanctions over multiple registrations." } ], "ai_role": { "status": "assessed", "statement": "The figures come from INEC's own statements (the deployer) relayed by two Nigerian outlets, not from an audit. INEC's website could not be searched. ABIS's matching method, error rates and the share of invalidations due to biometrics versus business rules are not stated. BusinessDay's critique (registrants evading ABIS; 49.3% of invalidations from two regions) is included as a limit." }, "evidence": [ { "kind": "news report", "citation": "Akindele Orimolade, Nigerian Democratic Report, 26 Oct 2022 (reproducing INEC chairman's address)", "title": "Nigeria’s Voter Register Hits 93.5 million as 12m New Registrants Are Added", "url": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/", "date": "2022-10-26", "written_by": "independent", "quote": "a total of 2,780,756 (22.6%) were identified as ineligible registrants and invalidated from the record" }, { "kind": "news report", "citation": "Donatus Anichukwueze, Punch, 13 Sep 2022", "title": "Double registration: INEC delists 1.1m newly registered voters", "url": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/", "date": "2022-09-13", "written_by": "independent", "quote": "cleaning up of the register of voters using the Automated Biometric Identification System (ABIS) is ongoing" }, { "kind": "news report", "citation": "BusinessDay, 20 Jan 2023", "title": "Eight questions INEC must answer Before the first ballot is cast", "url": "https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "date": "2023-01-20", "written_by": "independent", "quote": "Yet, those registrants found a way to evade the prying eyes of the ABIS and made it successfully to the voter register." } ], "replication": { "produced_by": [ "Biometric capture (fingerprints, face) at registration with a central de-duplication pass", "Business rules defining ineligible entries applied after matching", "Public display of the register for claims and objections" ], "must_hold": [ "Biometric capture quality adequate for matching", "Independent audit of matching accuracy (absent here)", "Officers unable to bypass capture protocols" ], "echoes": [ { "date": "2022-09-12", "setting": "Earlier CVR phase (28 Jun 2021 to 14 Jan 2022): 1,126,359 of 2,523,458 registrations invalidated", "model_version": null, "outcome": "recurred", "source": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/" } ] }, "paired_incident": null, "headline": "INEC's ABIS invalidates 2,780,756 ineligible voter registrations", "metric": "2,780,756 registrations (22.6%) were identified as ineligible and invalidated", "comparator": { "against": "the raw Continuous Voter Registration data before ABIS cleaning", "before": "12,298,944 Nigerians successfully completed the registration as new voters", "after": "the number of valid registrations (post-ABIS) is 9,518,188", "basis": "measured", "quote": "After a rigorous cleaning-up of the data using the Automated Biometric Identification System (ABIS), a total of 2,780,756 (22.6%) were identified as ineligible registrants and invalidated from the record", "url": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/" }, "later": { "status": "reversed", "date": "2023-01-20", "note": "A BusinessDay analysis argued that fake and underage registrants had nonetheless reached the final register and that invalidation rates were lopsided by region, a press critique rather than an official reversal.", "quote": "Yet, those registrants found a way to evade the prying eyes of the ABIS and made it successfully to the voter register.", "url": "https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/" }, "value": null, "developer_country": null, "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/; https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Independent_National_Electoral_Commission_(Nigeria)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports. 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invalidation rates were lopsided by region, a press critique rather than an official reversal.", "quote": "Yet, those registrants found a way to evade the prying eyes of the ABIS and made it successfully to the voter register.", "url": "https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0228" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The date is that of Professor Mahmood Yakubu's address to political parties in Abuja, reproduced by the Nigerian Democratic Report. He said the Continuous Voter Registration data (28 June 2021 to 31 July 2022) was cleaned with the Automated Biometric Identification System (ABIS). This identified 2,780,756 registrations (22.6%) as ineligible and invalidated them. Among them were double or multiple registrants, underage persons and fake registrations failing INEC's business rules. That left 9,518,188 valid post-ABIS registrations; 23 registration officers were identified for sanctions. On 12 September 2022, INEC commissioner Festus Okoye said 1,126,359 of 2,523,458 registrants (28 June 2021 to 14 January 2022) were found invalid and delisted. ABIS processing of later registrations was ongoing (Punch). BusinessDay later argued that some fake and underage registrants evaded ABIS and that invalidation rates were regionally lopsided. The vendor of ABIS is not named in the sources.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 3, "reason": "2,780,756 ineligible entries (22.6% of new registrations) were removed from the register used for the 2023 general election, per INEC's chairman. BusinessDay disputes completeness and regional balance." }, { "group": "organizational", "depth": 2, "reason": "INEC obtained a cleaned register and identified 23 of its own officers for sanctions over multiple registrations." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "De-duplicate and validate biometric voter registrations (fingerprints and faces) to remove double, multiple, underage and fake entries from the national register." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer_country": "Nigeria" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "De-duplicate and validate biometric voter registrations (fingerprints and faces) to remove double, multiple, underage and fake entries from the national register." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-10-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/; https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Independent_National_Electoral_Commission_(Nigeria)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Biometric capture (fingerprints, face) at registration with a central de-duplication pass", "Business rules defining ineligible entries applied after matching", "Public display of the register for claims and objections" ], "must_hold": [ "Biometric capture quality adequate for matching", "Independent audit of matching accuracy (absent here)", "Officers unable to bypass capture protocols" ], "echoes": [ { "date": "2022-09-12", "setting": "Earlier CVR phase (28 Jun 2021 to 14 Jan 2022): 1,126,359 of 2,523,458 registrations invalidated", "outcome": "recurred", "source": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Biometric capture (fingerprints, face) at registration with a central de-duplication pass", "Business rules defining ineligible entries applied after matching", "Public display of the register for claims and objections" ], "must_hold": [ "Biometric capture quality adequate for matching", "Independent audit of matching accuracy (absent here)", "Officers unable to bypass capture protocols" ], "echoes": [ { "date": "2022-09-12", "setting": "Earlier CVR phase (28 Jun 2021 to 14 Jan 2022): 1,126,359 of 2,523,458 registrations invalidated", "outcome": "recurred", "source": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/; https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Independent_National_Electoral_Commission_(Nigeria)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Electoral integrity / voter registration", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Akindele Orimolade, Nigerian Democratic Report, 26 Oct 2022 (reproducing INEC chairman's address)", "title": "Nigeria’s Voter Register Hits 93.5 million as 12m New Registrants Are Added", "url": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/", "date": "2022-10-26", "written_by": "independent", "quote": "a total of 2,780,756 (22.6%) were identified as ineligible registrants and invalidated from the record" }, { "kind": "news report", "citation": "Donatus Anichukwueze, Punch, 13 Sep 2022", "title": "Double registration: INEC delists 1.1m newly registered voters", "url": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/", "date": "2022-09-13", "written_by": "independent", "quote": "cleaning up of the register of voters using the Automated Biometric Identification System (ABIS) is ongoing" }, { "kind": "news report", "citation": "BusinessDay, 20 Jan 2023", "title": "Eight questions INEC must answer Before the first ballot is cast", "url": "https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "date": "2023-01-20", "written_by": "independent", "quote": "Yet, those registrants found a way to evade the prying eyes of the ABIS and made it successfully to the voter register." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0228-source-0001", "AIBD-0228-source-0002", "AIBD-0228-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Akindele Orimolade, Nigerian Democratic Report, 26 Oct 2022 (reproducing INEC chairman's address)", "title": "Nigeria’s Voter Register Hits 93.5 million as 12m New Registrants Are Added", "url": "https://www.ndr.org.ng/nigerias-voter-register-hits-93-5-million-as-12m-new-registrants-are-added/", "date": "2022-10-26", "written_by": "independent", "quote": "a total of 2,780,756 (22.6%) were identified as ineligible registrants and invalidated from the record" }, { "kind": "news report", "citation": "Donatus Anichukwueze, Punch, 13 Sep 2022", "title": "Double registration: INEC delists 1.1m newly registered voters", "url": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/", "date": "2022-09-13", "written_by": "independent", "quote": "cleaning up of the register of voters using the Automated Biometric Identification System (ABIS) is ongoing" }, { "kind": "news report", "citation": "BusinessDay, 20 Jan 2023", "title": "Eight questions INEC must answer Before the first ballot is cast", "url": "https://businessday.ng/uncategorized/article/eight-questions-inec-must-answer-before-the-first-ballot-is-cast/", "date": "2023-01-20", "written_by": "independent", "quote": "Yet, those registrants found a way to evade the prying eyes of the ABIS and made it successfully to the voter register." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "INEC chairman Mahmood Yakubu reported on 26 Oct 2022 that ABIS cleaning of 2021–22 registrations identified 2,780,756 (22.6%) double, underage or fake entries, leaving 9,518,188 valid new registrants.", "account": "The date is that of Professor Mahmood Yakubu's address to political parties in Abuja, reproduced by the Nigerian Democratic Report. He said the Continuous Voter Registration data (28 June 2021 to 31 July 2022) was cleaned with the Automated Biometric Identification System (ABIS). This identified 2,780,756 registrations (22.6%) as ineligible and invalidated them. Among them were double or multiple registrants, underage persons and fake registrations failing INEC's business rules. That left 9,518,188 valid post-ABIS registrations; 23 registration officers were identified for sanctions. On 12 September 2022, INEC commissioner Festus Okoye said 1,126,359 of 2,523,458 registrants (28 June 2021 to 14 January 2022) were found invalid and delisted. ABIS processing of later registrations was ongoing (Punch). BusinessDay later argued that some fake and underage registrants evaded ABIS and that invalidation rates were regionally lopsided. The vendor of ABIS is not named in the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Automated Biometric Identification System (ABIS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Not named in the sources" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "INEC's Automated Biometric Identification System invalidates 2,780,756 ineligible voter registrations (22.6%) before Nigeria's 2023 election", "headline": "INEC's ABIS invalidates 2,780,756 ineligible voter registrations" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5a044b42ea970ffac0e746064eb4422c05448688f59ddb7a4c03ba5cbc9d0a68" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The date is that of Professor Mahmood Yakubu's address to political parties in Abuja, reproduced by the Nigerian Democratic Report. He said the Continuous Voter Registration data (28 June 2021 to 31 July 2022) was cleaned with the Automated Biometric Identification System (ABIS). This identified 2,780,756 registrations (22.6%) as ineligible and invalidated them. Among them were double or multiple registrants, underage persons and fake registrations failing INEC's business rules. That left 9,518,188 valid post-ABIS registrations; 23 registration officers were identified for sanctions. On 12 September 2022, INEC commissioner Festus Okoye said 1,126,359 of 2,523,458 registrants (28 June 2021 to 14 January 2022) were found invalid and delisted. ABIS processing of later registrations was ongoing (Punch). BusinessDay later argued that some fake and underage registrants evaded ABIS and that invalidation rates were regionally lopsided. The vendor of ABIS is not named in the sources.", "replication": { "produced_by": [ "Biometric capture (fingerprints, face) at registration with a central de-duplication pass", "Business rules defining ineligible entries applied after matching", "Public display of the register for claims and objections" ], "must_hold": [ "Biometric capture quality adequate for matching", "Independent audit of matching accuracy (absent here)", "Officers unable to bypass capture protocols" ], "echoes": [ { "date": "2022-09-12", "setting": "Earlier CVR phase (28 Jun 2021 to 14 Jan 2022): 1,126,359 of 2,523,458 registrations invalidated", "outcome": "recurred", "source": "https://punchng.com/double-registration-inec-delists-1-1m-newly-registered-voters/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0032", "case_url": "records/AIBD-0032.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Darpan was built to match missing-child photographs with children found in institutions so they can be returned to their families. This reunification is that intended benefit." }, { "label": "AI contribution", "value": "The face match was the sole lead. The boy had been in an Assam institution for five years with no identification until Darpan matched his photograph to the Uttar Pradesh missing-child record. Police then contacted the parents. Verification rested on the parents identifying him in person. The Telangana CID news page was not available. So the record relies on news reports quoting the ADGP." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0032", "title": "Telangana Police “Darpan” face-matching reunites Som Soni with his family after five years", "summary": "Darpan matched Som Soni, missing from Allahabad since 14 July 2015, to a boy in a Goalpara, Assam child home; reunited October 2020.", "date_observed": "2020-10-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Darpan, a facial-recognition tool built by Telangana State Police, matches photographs of missing children against photographs of children lodged in child homes across the country. A routine matching run matched Som Soni to a boy police had found on 23 July 2015. He had been admitted to a child welfare center at Goalpara, Assam (spelled “Golapara” in the reports). Soni had gone missing from his home in Allahabad (Uttar Pradesh) on 14 July 2015 aged eight. ETV Bharat describes him as a resident of Handia in Allahabad. The case lay with Handia police station. Telangana Police notified Handia police. His parents traveled to Goalpara and identified him. The case was announced on 9 October 2020 by Swati Lakra, ADGP of the Telangana Police Women Safety Wing. The exact day of the reunion is not given.", "category": "benefit created", "unexpected": false, "purpose": "Darpan was built to match missing-child photographs with children found in institutions so they can be returned to their families. This reunification is that intended benefit.", "system": "Darpan face-recognition portal for missing and found children", "model_version": null, "developer": "Telangana State Police", "deployer": "Telangana State Police, with Goalpara Police (Assam) and Handia Police Station (Allahabad, Uttar Pradesh)", "location": "Goalpara, India", "domain": "Policing / missing persons and child protection", "segment": "Missing children in child-care institutions", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The good is a family reunited, and the type names the identity match that located the boy.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "A child separated from his parents for five years, living in an institution in another state, was identified and returned to his family. The sources describe the match as having “changed the lives of the child and his parents”." } ], "ai_role": { "status": "assessed", "statement": "The face match was the sole lead. The boy had been in an Assam institution for five years with no identification until Darpan matched his photograph to the Uttar Pradesh missing-child record. Police then contacted the parents. Verification rested on the parents identifying him in person. The Telangana CID news page was not available. So the record relies on news reports quoting the ADGP." }, "evidence": [ { "kind": "news report", "citation": "The Tribune (2020). Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software. The Tribune, 9 October 2020.", "title": "Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software", "url": "https://www.tribuneindia.com/news/nation/face-recognition-tool-reunites-up-boy-with-parents-after-5-years-153319", "date": "2020-10-09", "written_by": "independent", "quote": "The software is used in routine to carry out the matching, and often leads to successfully uniting children with their families." }, { "kind": "news report", "citation": "ETV Bharat (2020). 5 years on, missing UP boy traced to Assam through 'DARPAN'. ETV Bharat, 9 October 2020.", "title": "Missing UP boy traced to Assam through Telangana police's face recognition tool", "url": "https://www.etvbharat.com/english/bharat/bharat-news/5-years-on-missing-up-boy-traced-to-assam-through-darpan/na20201009182148536", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana police, using the face recognition tool to match photographs of missing children with those of children found at various child homes across the country, detected the boy" }, { "kind": "news report", "citation": "Telangana Today (2020). TS Police 'Darpan' helps child from UP reunite with parents after 5 years. Telangana Today, 9 October 2020.", "title": "TS Police 'Darpan' helps child from UP reunite with parents after 5 years", "url": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana State Police played a major role in reuniting the missing child with his parents after five years." }, { "kind": "news report", "citation": "The Hans India (2020). Telangana Police's Darpan helps MP boy to reunite with parents after 10 years. The Hans India, 14 December 2020.", "title": "Telangana Police's Darpan helps MP boy to reunite with parents after 10 years", "url": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "date": "2020-12-14", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A pool of missing-child photographs (the reports do not say how these are collected)", "Photographs of children in child-care institutions supplied by welfare departments", "Routine matching runs by Telangana State Police" ], "must_hold": [ "Institutions keep supplying current photographs of found children", "Matches are verified in person by family and local police before handover", "Child's appearance five or ten years after disappearance remains matchable" ], "echoes": [ { "date": "2020-12-12", "setting": "Rahul Srivastava, missing from Jabalpur, Madhya Pradesh since 2010 (7 October 2010 per ETV Bharat; July 2010 per The Hans India), was matched by Darpan in March 2020 to a child welfare center in Howrah, West Bengal, and handed to his parents by West Bengal Police on 12 December 2020", "model_version": null, "outcome": "recurred", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805 ; https://www.etvbharat.com/english/state/telangana/10-years-on-missing-boy-re-united-with-family-by-darpan/na20201214172158693" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "483", "title": "Indian Police Allegedly Tortured and Killed Innocent Man Following Facial Misidentification", "url": "https://incidentdatabase.ai/cite/483", "severity": null, "group": "personal", "basis": "Same deployer and task: Telangana Police facial-recognition matching; Darpan matched a missing boy to his family, while a Medak police face match misidentified Mohammed Khadeer, who died in custody." }, "headline": "Darpan reunites Som Soni with his family after five years", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2020-12-12", "note": "Two months later Darpan matched another missing child, Rahul from Jabalpur, to a Howrah child welfare home; he was handed to his parents on 12 December 2020.", "quote": "The tool helped the police to detect Rahul by his photographs.", "url": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805" }, "value": null, "developer_country": "India", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 483 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/483", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attributed Darpan and its matching runs to Telangana State Police rather than its Women Safety Wing, which the reports give only as ADGP Swati Lakra's title; replaced the inferred “missing from Handia” and the unsourced “FIRs” with what the reports state; and noted that the two echo sources disagree on Rahul Srivastava's disappearance date.", "source": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Telangana_Police; https://en.wikipedia.org/wiki/Telangana_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0032", "catalog_version": "1.0.0", "source_fingerprint": "c3599adb7d26fe935f2a9e8a89fbcd5cf79d3e545b17d1c488a8e6508920580a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0032-source-0001", "AIBD-0032-source-0002", "AIBD-0032-source-0003", "AIBD-0032-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Telangana State Police", "deployer": "Telangana State Police, with Goalpara Police (Assam) and Handia Police Station (Allahabad, Uttar Pradesh)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The face match was the sole lead. The boy had been in an Assam institution for five years with no identification until Darpan matched his photograph to the Uttar Pradesh missing-child record. Police then contacted the parents. Verification rested on the parents identifying him in person. The Telangana CID news page was not available. So the record relies on news reports quoting the ADGP." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The face match was the sole lead. The boy had been in an Assam institution for five years with no identification until Darpan matched his photograph to the Uttar Pradesh missing-child record. Police then contacted the parents. Verification rested on the parents identifying him in person. The Telangana CID news page was not available. So the record relies on news reports quoting the ADGP." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A child separated from his parents for five years, living in an institution in another state, was identified and returned to his family. 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"review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "483", "title": "Indian Police Allegedly Tortured and Killed Innocent Man Following Facial Misidentification", "url": "https://incidentdatabase.ai/cite/483", "group": "personal", "basis": "Same deployer and task: Telangana Police facial-recognition matching; Darpan matched a missing boy to his family, while a Medak police face match misidentified Mohammed Khadeer, who died in custody." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2020-12-12", "note": "Two months later Darpan matched another missing child, Rahul from Jabalpur, to a Howrah child welfare home; he was handed to his parents on 12 December 2020.", "quote": "The tool helped the police to detect Rahul by his photographs.", "url": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0032" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The good is a family reunited, and the type names the identity match that located the boy." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Darpan, a facial-recognition tool built by Telangana State Police, matches photographs of missing children against photographs of children lodged in child homes across the country. A routine matching run matched Som Soni to a boy police had found on 23 July 2015. He had been admitted to a child welfare center at Goalpara, Assam (spelled “Golapara” in the reports). Soni had gone missing from his home in Allahabad (Uttar Pradesh) on 14 July 2015 aged eight. ETV Bharat describes him as a resident of Handia in Allahabad. The case lay with Handia police station. Telangana Police notified Handia police. His parents traveled to Goalpara and identified him. The case was announced on 9 October 2020 by Swati Lakra, ADGP of the Telangana Police Women Safety Wing. The exact day of the reunion is not given.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "A child separated from his parents for five years, living in an institution in another state, was identified and returned to his family. The sources describe the match as having “changed the lives of the child and his parents”." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Darpan was built to match missing-child photographs with children found in institutions so they can be returned to their families. This reunification is that intended benefit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Goalpara, India", "developer_country": "India", "deployer_country": "India" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Darpan was built to match missing-child photographs with children found in institutions so they can be returned to their families. This reunification is that intended benefit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2020-10-09", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 483 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/483", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attributed Darpan and its matching runs to Telangana State Police rather than its Women Safety Wing, which the reports give only as ADGP Swati Lakra's title; replaced the inferred “missing from Handia” and the unsourced “FIRs” with what the reports state; and noted that the two echo sources disagree on Rahul Srivastava's disappearance date.", "source": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Telangana_Police; https://en.wikipedia.org/wiki/Telangana_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pool of missing-child photographs (the reports do not say how these are collected)", "Photographs of children in child-care institutions supplied by welfare departments", "Routine matching runs by Telangana State Police" ], "must_hold": [ "Institutions keep supplying current photographs of found children", "Matches are verified in person by family and local police before handover", "Child's appearance five or ten years after disappearance remains matchable" ], "echoes": [ { "date": "2020-12-12", "setting": "Rahul Srivastava, missing from Jabalpur, Madhya Pradesh since 2010 (7 October 2010 per ETV Bharat; July 2010 per The Hans India), was matched by Darpan in March 2020 to a child welfare center in Howrah, West Bengal, and handed to his parents by West Bengal Police on 12 December 2020", "outcome": "recurred", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805 ; https://www.etvbharat.com/english/state/telangana/10-years-on-missing-boy-re-united-with-family-by-darpan/na20201214172158693" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pool of missing-child photographs (the reports do not say how these are collected)", "Photographs of children in child-care institutions supplied by welfare departments", "Routine matching runs by Telangana State Police" ], "must_hold": [ "Institutions keep supplying current photographs of found children", "Matches are verified in person by family and local police before handover", "Child's appearance five or ten years after disappearance remains matchable" ], "echoes": [ { "date": "2020-12-12", "setting": "Rahul Srivastava, missing from Jabalpur, Madhya Pradesh since 2010 (7 October 2010 per ETV Bharat; July 2010 per The Hans India), was matched by Darpan in March 2020 to a child welfare center in Howrah, West Bengal, and handed to his parents by West Bengal Police on 12 December 2020", "outcome": "recurred", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805 ; https://www.etvbharat.com/english/state/telangana/10-years-on-missing-boy-re-united-with-family-by-darpan/na20201214172158693" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 483 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/483", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attributed Darpan and its matching runs to Telangana State Police rather than its Women Safety Wing, which the reports give only as ADGP Swati Lakra's title; replaced the inferred “missing from Handia” and the unsourced “FIRs” with what the reports state; and noted that the two echo sources disagree on Rahul Srivastava's disappearance date.", "source": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Telangana_Police; https://en.wikipedia.org/wiki/Telangana_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / missing persons and child protection", "segment": "Missing children in child-care institutions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The Tribune (2020). Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software. The Tribune, 9 October 2020.", "title": "Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software", "url": "https://www.tribuneindia.com/news/nation/face-recognition-tool-reunites-up-boy-with-parents-after-5-years-153319", "date": "2020-10-09", "written_by": "independent", "quote": "The software is used in routine to carry out the matching, and often leads to successfully uniting children with their families." }, { "kind": "news report", "citation": "ETV Bharat (2020). 5 years on, missing UP boy traced to Assam through 'DARPAN'. ETV Bharat, 9 October 2020.", "title": "Missing UP boy traced to Assam through Telangana police's face recognition tool", "url": "https://www.etvbharat.com/english/bharat/bharat-news/5-years-on-missing-up-boy-traced-to-assam-through-darpan/na20201009182148536", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana police, using the face recognition tool to match photographs of missing children with those of children found at various child homes across the country, detected the boy" }, { "kind": "news report", "citation": "Telangana Today (2020). TS Police 'Darpan' helps child from UP reunite with parents after 5 years. Telangana Today, 9 October 2020.", "title": "TS Police 'Darpan' helps child from UP reunite with parents after 5 years", "url": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana State Police played a major role in reuniting the missing child with his parents after five years." }, { "kind": "news report", "citation": "The Hans India (2020). Telangana Police's Darpan helps MP boy to reunite with parents after 10 years. The Hans India, 14 December 2020.", "title": "Telangana Police's Darpan helps MP boy to reunite with parents after 10 years", "url": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "date": "2020-12-14", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0032-source-0001", "AIBD-0032-source-0002", "AIBD-0032-source-0003", "AIBD-0032-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The Tribune (2020). Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software. The Tribune, 9 October 2020.", "title": "Boy missing from UP found in Assam after 5 years with help of Telangana Police's facial recognition software", "url": "https://www.tribuneindia.com/news/nation/face-recognition-tool-reunites-up-boy-with-parents-after-5-years-153319", "date": "2020-10-09", "written_by": "independent", "quote": "The software is used in routine to carry out the matching, and often leads to successfully uniting children with their families." }, { "kind": "news report", "citation": "ETV Bharat (2020). 5 years on, missing UP boy traced to Assam through 'DARPAN'. ETV Bharat, 9 October 2020.", "title": "Missing UP boy traced to Assam through Telangana police's face recognition tool", "url": "https://www.etvbharat.com/english/bharat/bharat-news/5-years-on-missing-up-boy-traced-to-assam-through-darpan/na20201009182148536", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana police, using the face recognition tool to match photographs of missing children with those of children found at various child homes across the country, detected the boy" }, { "kind": "news report", "citation": "Telangana Today (2020). TS Police 'Darpan' helps child from UP reunite with parents after 5 years. Telangana Today, 9 October 2020.", "title": "TS Police 'Darpan' helps child from UP reunite with parents after 5 years", "url": "https://telanganatoday.com/ts-police-darpan-helps-child-from-up-reunite-with-parents-after-5-years", "date": "2020-10-09", "written_by": "independent", "quote": "Telangana State Police played a major role in reuniting the missing child with his parents after five years." }, { "kind": "news report", "citation": "The Hans India (2020). Telangana Police's Darpan helps MP boy to reunite with parents after 10 years. The Hans India, 14 December 2020.", "title": "Telangana Police's Darpan helps MP boy to reunite with parents after 10 years", "url": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805", "date": "2020-12-14", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Darpan matched Som Soni, missing from Allahabad since 14 July 2015, to a boy in a Goalpara, Assam child home; reunited October 2020.", "account": "Darpan, a facial-recognition tool built by Telangana State Police, matches photographs of missing children against photographs of children lodged in child homes across the country. A routine matching run matched Som Soni to a boy police had found on 23 July 2015. He had been admitted to a child welfare center at Goalpara, Assam (spelled “Golapara” in the reports). Soni had gone missing from his home in Allahabad (Uttar Pradesh) on 14 July 2015 aged eight. ETV Bharat describes him as a resident of Handia in Allahabad. The case lay with Handia police station. Telangana Police notified Handia police. His parents traveled to Goalpara and identified him. The case was announced on 9 October 2020 by Swati Lakra, ADGP of the Telangana Police Women Safety Wing. The exact day of the reunion is not given." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Darpan face-recognition portal for missing and found children" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Telangana State Police" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Telangana Police “Darpan” face-matching reunites Som Soni with his family after five years", "headline": "Darpan reunites Som Soni with his family after five years" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c3599adb7d26fe935f2a9e8a89fbcd5cf79d3e545b17d1c488a8e6508920580a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Darpan, a facial-recognition tool built by Telangana State Police, matches photographs of missing children against photographs of children lodged in child homes across the country. A routine matching run matched Som Soni to a boy police had found on 23 July 2015. He had been admitted to a child welfare center at Goalpara, Assam (spelled “Golapara” in the reports). Soni had gone missing from his home in Allahabad (Uttar Pradesh) on 14 July 2015 aged eight. ETV Bharat describes him as a resident of Handia in Allahabad. The case lay with Handia police station. Telangana Police notified Handia police. His parents traveled to Goalpara and identified him. The case was announced on 9 October 2020 by Swati Lakra, ADGP of the Telangana Police Women Safety Wing. The exact day of the reunion is not given.", "replication": { "produced_by": [ "A pool of missing-child photographs (the reports do not say how these are collected)", "Photographs of children in child-care institutions supplied by welfare departments", "Routine matching runs by Telangana State Police" ], "must_hold": [ "Institutions keep supplying current photographs of found children", "Matches are verified in person by family and local police before handover", "Child's appearance five or ten years after disappearance remains matchable" ], "echoes": [ { "date": "2020-12-12", "setting": "Rahul Srivastava, missing from Jabalpur, Madhya Pradesh since 2010 (7 October 2010 per ETV Bharat; July 2010 per The Hans India), was matched by Darpan in March 2020 to a child welfare center in Howrah, West Bengal, and handed to his parents by West Bengal Police on 12 December 2020", "outcome": "recurred", "source": "https://www.thehansindia.com/telangana/telangana-polices-darpan-helps-mp-boy-to-reunite-with-parents-after-10-years-661805 ; https://www.etvbharat.com/english/state/telangana/10-years-on-missing-boy-re-united-with-family-by-darpan/na20201214172158693" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0029", "case_url": "records/AIBD-0029.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "PAWS was built to predict where poaching will occur and to prescribe patrol routes that maximize detection and removal of snares. The snare removals are exactly this intended benefit." }, { "label": "AI contribution", "value": "The AI's output (risk-ranked 3 x 3 km blocks) determined where patrols went. Rangers were blinded to risk class. High-risk blocks yielded detections while low-risk blocks yielded none. The paper itself cautions that the first QENP test's increased findings “may be partially explained by factors beyond the predictive model”. The 1,000-versus-200–300 comparison relies on rangers' reported averages, not a controlled baseline. The peer-reviewed paper is written by the system's academic developers together with staff of the operating organizations (WWF, WCS, Uganda Wildlife Authority). There is no commercial vendor." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Cambodia" ] }, "record": { "id": "AIBD-0029", "title": "PAWS patrol-planning field test removes over 1,000 snares in Srepok Wildlife Sanctuary, Cambodia", "summary": "PAWS-directed ranger patrols in Srepok Wildlife Sanctuary removed over 1,000 snares in December 2018, versus 200–300 in a typical month.", "date_observed": "2018-12-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "PAWS (Protection Assistant for Wildlife Security) is a machine-learning pipeline that predicts poaching risk and plans patrols. It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. A second trial ran February to March 2019.", "category": "benefit created", "unexpected": false, "purpose": "PAWS was built to predict where poaching will occur and to prescribe patrol routes that maximize detection and removal of snares. The snare removals are exactly this intended benefit.", "system": "PAWS (Protection Assistant for Wildlife Security), enhanced iWare-E predictive model with Gaussian-process uncertainty and game-theoretic patrol planning", "model_version": "iWare-E with Gaussian-process weak learners trained on January 2015 to April 2018 dry-season data (Xu et al., ICDE 2020)", "developer": "Harvard University and University of Southern California (Milind Tambe and colleagues), with WWF, WCS and Uganda Wildlife Authority co-authors", "deployer": "WWF Cambodia with Cambodia Ministry of Environment rangers, Srepok Wildlife Sanctuary", "location": "Srepok Wildlife Sanctuary (Mondulkiri), Cambodia", "domain": "Conservation / anti-poaching law enforcement", "segment": "Protected-area ranger patrols", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The ranger force's monthly snare removals rose from a reported two or three hundred to over one thousand. A material gain in patrol effectiveness for the operating team. A chi-squared test confirmed predictions discriminated risk (p = 0.023)." }, { "group": "societal", "depth": 3, "reason": "Over one thousand snares were removed from a sanctuary identified as a tiger-reintroduction site. The paper states each snare “represents the life of one animal potentially saved”. A material landscape-scale conservation benefit rather than a documented population-level change." } ], "ai_role": { "status": "assessed", "statement": "The AI's output (risk-ranked 3 x 3 km blocks) determined where patrols went. Rangers were blinded to risk class. High-risk blocks yielded detections while low-risk blocks yielded none. The paper itself cautions that the first QENP test's increased findings “may be partially explained by factors beyond the predictive model”. The 1,000-versus-200–300 comparison relies on rangers' reported averages, not a controlled baseline. The peer-reviewed paper is written by the system's academic developers together with staff of the operating organizations (WWF, WCS, Uganda Wildlife Authority). There is no commercial vendor." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Xu L, Gholami S, Mc Carthy S, Dilkina B, Plumptre A, Tambe M, Singh R, Nsubuga M, Mabonga J, Driciru M, Wanyama F, Rwetsiba A, Okello T, Enyel E (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE ICDE 2020.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations", "url": "https://arxiv.org/pdf/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares" }, { "kind": "peer-reviewed study", "citation": "Xu L et al. (2020). Stay Ahead of Poachers (abstract page; states the work is a short paper published in ICDE 2020). arXiv.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (abstract)", "url": "https://arxiv.org/abs/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "confirm that the predictive power of PAWS extends promisingly to multiple parks" }, { "kind": "news report", "citation": "Geib C (2020). Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions. Mongabay.", "title": "Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "date": "2020-11-05", "written_by": "independent", "quote": "rangers in the park have reported finding between 5 and 20 times more snares since they started using the PAWS system" } ], "replication": { "produced_by": [ "Historical SMART patrol data (2015–2018) sufficient to train a risk model despite 3.6% positive labels", "Rangers willing to patrol the suggested blocks and record findings in SMART", "Dry-season deployment when rivers are passable and poaching concentrates in the north of the sanctuary" ], "must_hold": [ "Patrol capacity exists to act on predictions (SWS ranger numbers had more than doubled since 2016)", "Poacher behavior remains spatially predictable from past detections", "Findings are measured against a comparable baseline. The paper notes increases may partly reflect factors beyond the model" ], "echoes": [ { "date": "2019-02-01", "setting": "Second SWS trial, February-March 2019: high-risk blocks yielded 7 detections in 22 cells, low-risk blocks 0 in 34 cells (Table III of the same paper)", "model_version": "iWare-E predictions for January-February 2019", "outcome": "recurred", "source": "https://arxiv.org/pdf/1903.06669" }, { "date": "2020-11-05", "setting": "Ongoing use in Srepok Wildlife Sanctuary; rangers reported finding 5 to 20 times more snares since adopting PAWS, and PAWS was incorporated into SMART", "model_version": null, "outcome": "recurred", "source": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "severity": null, "group": "societal", "basis": "Same task and domain: AI-directed anti-poaching in a protected area; PAWS patrols removed 1,000 snares, while Ezemvelo KZN Wildlife's AI cameras failed to detect poachers and four rhinos were killed." }, "headline": "PAWS-directed patrols remove over 1,000 snares in Cambodia", "metric": "removed over 1,000 snares in December 2018, versus 200–300 in a typical month", "comparator": { "against": "the rangers' usual monthly snare removal without PAWS", "before": "two or three hundred snares in an average month", "after": "over one thousand snares in the first month of field tests", "basis": "measured", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares—each of which represents the life of one animal potentially saved. The rangers informed us that, by comparison, they typically remove two or three hundred snares in an average month.", "url": "https://arxiv.org/pdf/1903.06669" }, "later": { "status": "scaled", "date": "2020-11-05", "note": "By November 2020 PAWS had been incorporated into the SMART conservation software used in parks worldwide, and Srepok rangers reported finding 5 to 20 times more snares since adopting it.", "quote": "PAWS has been incorporated into the latest update to SMART, which is already used widely by rangers and other park employees to gather data and track observations of illegal activity in parks around the world.", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" }, "value": null, "developer_country": "United States", "deployer_country": "Cambodia", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 219 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://arxiv.org/pdf/1903.06669; https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.harvard.edu/about/; https://www.wikidata.org/wiki/Q117405694", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Harvard University.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two peer-reviewed studies from the makers. 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The snare removals are exactly this intended benefit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2018-12-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 219 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://arxiv.org/pdf/1903.06669; https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.harvard.edu/about/; https://www.wikidata.org/wiki/Q117405694", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Harvard University.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Historical SMART patrol data (2015–2018) sufficient to train a risk model despite 3.6% positive labels", "Rangers willing to patrol the suggested blocks and record findings in SMART", "Dry-season deployment when rivers are passable and poaching concentrates in the north of the sanctuary" ], "must_hold": [ "Patrol capacity exists to act on predictions (SWS ranger numbers had more than doubled since 2016)", "Poacher behavior remains spatially predictable from past detections", "Findings are measured against a comparable baseline. 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The paper notes increases may partly reflect factors beyond the model" ], "echoes": [ { "date": "2019-02-01", "setting": "Second SWS trial, February-March 2019: high-risk blocks yielded 7 detections in 22 cells, low-risk blocks 0 in 34 cells (Table III of the same paper)", "model_version": "iWare-E predictions for January-February 2019", "outcome": "recurred", "source": "https://arxiv.org/pdf/1903.06669" }, { "date": "2020-11-05", "setting": "Ongoing use in Srepok Wildlife Sanctuary; rangers reported finding 5 to 20 times more snares since adopting PAWS, and PAWS was incorporated into SMART", "outcome": "recurred", "source": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 219 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://arxiv.org/pdf/1903.06669; https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.harvard.edu/about/; https://www.wikidata.org/wiki/Q117405694", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Harvard University.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Conservation / anti-poaching law enforcement", "segment": "Protected-area ranger patrols" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Xu L, Gholami S, Mc Carthy S, Dilkina B, Plumptre A, Tambe M, Singh R, Nsubuga M, Mabonga J, Driciru M, Wanyama F, Rwetsiba A, Okello T, Enyel E (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE ICDE 2020.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations", "url": "https://arxiv.org/pdf/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares" }, { "kind": "peer-reviewed study", "citation": "Xu L et al. (2020). Stay Ahead of Poachers (abstract page; states the work is a short paper published in ICDE 2020). arXiv.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (abstract)", "url": "https://arxiv.org/abs/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "confirm that the predictive power of PAWS extends promisingly to multiple parks" }, { "kind": "news report", "citation": "Geib C (2020). Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions. Mongabay.", "title": "Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "date": "2020-11-05", "written_by": "independent", "quote": "rangers in the park have reported finding between 5 and 20 times more snares since they started using the PAWS system" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0029-source-0001", "AIBD-0029-source-0002", "AIBD-0029-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Xu L, Gholami S, Mc Carthy S, Dilkina B, Plumptre A, Tambe M, Singh R, Nsubuga M, Mabonga J, Driciru M, Wanyama F, Rwetsiba A, Okello T, Enyel E (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE ICDE 2020.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations", "url": "https://arxiv.org/pdf/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "During the first month of field tests in SWS, park rangers detected poaching in 32 cells and removed over one thousand snares" }, { "kind": "peer-reviewed study", "citation": "Xu L et al. (2020). Stay Ahead of Poachers (abstract page; states the work is a short paper published in ICDE 2020). arXiv.", "title": "Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (abstract)", "url": "https://arxiv.org/abs/1903.06669", "date": "2019-11-06", "written_by": "makers", "quote": "confirm that the predictive power of PAWS extends promisingly to multiple parks" }, { "kind": "news report", "citation": "Geib C (2020). Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions. Mongabay.", "title": "Where to patrol next: 'Netflix' of ranger AI serves up poaching predictions", "url": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/", "date": "2020-11-05", "written_by": "independent", "quote": "rangers in the park have reported finding between 5 and 20 times more snares since they started using the PAWS system" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "PAWS-directed ranger patrols in Srepok Wildlife Sanctuary removed over 1,000 snares in December 2018, versus 200–300 in a typical month.", "account": "PAWS (Protection Assistant for Wildlife Security) is a machine-learning pipeline that predicts poaching risk and plans patrols. It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. A second trial ran February to March 2019." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The models predicted poaching risk to guide patrol locations." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The iWare-E models were trained on dry-season data from January 2015 through April 2018." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "The models used historical patrol data to estimate where poaching was likely." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "system", "ai_role", "replication", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "Gaussian-process models within iWare-E." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "iWare-E with Gaussian-process weak learners trained on January 2015 to April 2018 dry-season data (Xu et al., ICDE 2020)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "PAWS (Protection Assistant for Wildlife Security), enhanced iWare-E predictive model with Gaussian-process uncertainty and game-theoretic patrol planning" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Harvard University and University of Southern California (Milind Tambe and colleagues), with WWF, WCS and Uganda Wildlife Authority co-authors" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "PAWS patrol-planning field test removes over 1,000 snares in Srepok Wildlife Sanctuary, Cambodia", "headline": "PAWS-directed patrols remove over 1,000 snares in Cambodia" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "faaa16a57c0d26842d7dd6d15debcd4322f41ff602ffdd7fce38bf8542a42d9c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "PAWS (Protection Assistant for Wildlife Security) is a machine-learning pipeline that predicts poaching risk and plans patrols. It was field-tested with WWF Cambodia and Ministry of Environment rangers in Srepok Wildlife Sanctuary, Cambodia, from December 2018. Only the month is given, so the first day is used. Rangers received center coordinates of fifteen 3 x 3 km blocks chosen from PAWS risk predictions, without being told each block's risk class. In the first month, 72 rangers in teams of eight detected poaching in 32 cells and removed over one thousand snares. They had reported removing two or three hundred in an average month. Low-risk blocks yielded no poaching. A second trial ran February to March 2019.", "replication": { "produced_by": [ "Historical SMART patrol data (2015–2018) sufficient to train a risk model despite 3.6% positive labels", "Rangers willing to patrol the suggested blocks and record findings in SMART", "Dry-season deployment when rivers are passable and poaching concentrates in the north of the sanctuary" ], "must_hold": [ "Patrol capacity exists to act on predictions (SWS ranger numbers had more than doubled since 2016)", "Poacher behavior remains spatially predictable from past detections", "Findings are measured against a comparable baseline. The paper notes increases may partly reflect factors beyond the model" ], "echoes": [ { "date": "2019-02-01", "setting": "Second SWS trial, February-March 2019: high-risk blocks yielded 7 detections in 22 cells, low-risk blocks 0 in 34 cells (Table III of the same paper)", "model_version": "iWare-E predictions for January-February 2019", "outcome": "recurred", "source": "https://arxiv.org/pdf/1903.06669" }, { "date": "2020-11-05", "setting": "Ongoing use in Srepok Wildlife Sanctuary; rangers reported finding 5 to 20 times more snares since adopting PAWS, and PAWS was incorporated into SMART", "outcome": "recurred", "source": "https://news.mongabay.com/2020/11/where-to-patrol-next-netflix-of-ranger-ai-serves-up-poaching-predictions/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 36, "reviewed": 0, "record_mapped": 36, "not_assessed": 55, "complete": false, "by_state": { "value_present": 36, "not_reported": 0, "unresolved": 0, "not_assessed": 55, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0196", "case_url": "records/AIBD-0196.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Make commercial fishing activity visible in near real time by processing AIS vessel tracks to identify when vessels are fishing." }, { "label": "AI contribution", "value": "An independent newspaper confirms that Kiribati used the platform to document the illegal operation. Oceana describes the platform's fishing detection as machine learning over AIS data. No cited source says whether the classifier's output, as opposed to the vessel's track, carried the case. Developer (Oceana, SkyTruth, Google) and deployer (Kiribati) are distinct." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Kiribati" ] }, "record": { "id": "AIBD-0196", "title": "Global Fishing Watch data lets Kiribati fine a vessel US$1 million for fishing in the Phoenix Islands Protected Area", "summary": "Kiribati used the Google-Oceana-SkyTruth platform to document Marshalls 203 fishing illegally in PIPA; a US$1 million fine plus a US$1 million goodwill grant followed (reported September 2016).", "date_observed": "2016-09-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine.", "category": "benefit created", "unexpected": false, "purpose": "Make commercial fishing activity visible in near real time by processing AIS vessel tracks to identify when vessels are fishing.", "system": "Global Fishing Watch (AIS-based fishing detection using machine learning)", "model_version": null, "developer": "Oceana, SkyTruth and Google", "deployer": "Government of Kiribati", "location": "Phoenix Islands Protected Area, Kiribati", "domain": "Fisheries enforcement", "segment": "Marine protected area enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The criminal activity penalized is illegal fishing in a protected area, and the fine is the only documented outcome.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 3, "reason": "Kiribati secured a US$1 million fine against Central Pacific Fishing Company plus a US$1 million goodwill grant (Grist). The BBC piece puts the total at US$2 million, about 1% of the country's GDP." }, { "group": "societal", "depth": null, "reason": "No source documents subsequent fishing pressure or deterrence inside the protected area." } ], "ai_role": { "status": "assessed", "statement": "An independent newspaper confirms that Kiribati used the platform to document the illegal operation. Oceana describes the platform's fishing detection as machine learning over AIS data. No cited source says whether the classifier's output, as opposed to the vessel's track, carried the case. Developer (Oceana, SkyTruth, Google) and deployer (Kiribati) are distinct." }, "evidence": [ { "kind": "news report", "citation": "Dennis B. How Google is helping to crack down on illegal fishing - from space. The Washington Post, 15 September 2016.", "title": "How Google is helping to crack down on illegal fishing - from space", "url": "https://www.washingtonpost.com/news/energy-environment/wp/2016/09/15/from-space-a-new-effort-to-crack-down-on-illegal-fishing-across-the-globe/", "date": "2016-09-15", "written_by": "independent", "quote": "The government of the Pacific island nation of Kiribati used it to document how a tuna-fishing vessel had operated illegally inside the Phoenix Islands Protected Area" }, { "kind": "news report", "citation": "Danson T. This new digital tool can help fight overfishing around the world. Grist, 15 September 2016 (author is an Oceana board member).", "title": "This new digital tool can help fight overfishing around the world", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "date": "2016-09-15", "written_by": "makers", "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203." }, { "kind": "news report", "citation": "Savitz J. How to spot the secretive activities of rogue fishing boats. BBC Future, 7 June 2018 (author is Oceana's chief policy officer for North America).", "title": "How to spot the secretive activities of rogue fishing boats", "url": "https://www.bbc.com/future/article/20180607-how-to-spot-the-secretive-activities-of-rogue-fishing-boats", "date": "2018-06-07", "written_by": "makers", "quote": "Then we run them all through the Global Fishing Watch algorithm, and use machine learning and cloud computing to identify when they are fishing." }, { "kind": "press release", "citation": "Cutlip K. Indonesia Makes its Fishing Fleet Visible to the World. Global Fishing Watch, 7 June 2017.", "title": "Indonesia Makes its Fishing Fleet Visible to the World", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/", "date": "2017-06-07", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "A vessel broadcasting AIS inside a no-take area that Kiribati closed to commercial fishing from 1 January 2015", "A government willing to prosecute on third-party satellite-derived evidence" ], "must_hold": [ "AIS transmitters left on (the BBC piece notes vessels can switch them off)", "Admissibility of AIS-derived evidence under the coastal state's law" ], "echoes": [ { "date": "2024-01-03", "setting": "Global coastal waters 2017–2021: Global Fishing Watch deep-learning mapping of untracked industrial vessels", "model_version": "SAR imagery with deep-learning models", "outcome": "unknown", "source": "Paolo FS, Kroodsma D et al. 2024, Nature, doi:10.1038/s41586-023-06825-8" } ] }, "paired_incident": null, "headline": "Global Fishing Watch helps Kiribati fine a vessel US$1 million", "metric": "a US$1 million fine plus a US$1 million goodwill grant followed", "comparator": null, "later": { "status": "scaled", "date": "2017-06-07", "note": "In June 2017 Indonesia became the first country to publish its proprietary vessel monitoring system data through Global Fishing Watch, adding nearly 5,000 vessels to the platform, with Peru announcing it would follow.", "quote": "This week, at the United Nation’s Ocean Conference, the Republic of Indonesia becomes the first nation ever to publish Vessel Monitoring System (VMS) data revealing the location and activity of its commercial fishing fleet.", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/" }, "value": { "amount": "$1 million", "what": "fine secured by Kiribati against the owner of Marshalls 203 for fishing illegally in the Phoenix Islands Protected Area, plus an additional $1 million paid as a grant under a goodwill arrangement", "who": "Government of Kiribati", "estimate": false, "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203. The company also made a special “goodwill arrangement” with the government, agreeing to pay an additional $1 million in the form of a grant.", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/" }, "developer_country": "United States", "deployer_country": "Kiribati", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the value as stated from the cited sources.", "source": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/; https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Oceana_(non-profit_group); https://en.wikipedia.org/wiki/Kiribati", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, two news reports from the makers and the makers' press release. 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Grist, 15 September 2016 (author is an Oceana board member).", "title": "This new digital tool can help fight overfishing around the world", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "date": "2016-09-15", "written_by": "makers", "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203." }, { "kind": "news report", "citation": "Savitz J. How to spot the secretive activities of rogue fishing boats. 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Grist, 15 September 2016 (author is an Oceana board member).", "title": "This new digital tool can help fight overfishing around the world", "url": "https://grist.org/science/this-new-digital-tool-could-help-fight-overfishing-around-the-world/", "date": "2016-09-15", "written_by": "makers", "quote": "This helped the country secure a $1 million fine against the Central Pacific Fishing Company, the owner of Marshalls 203." }, { "kind": "news report", "citation": "Savitz J. How to spot the secretive activities of rogue fishing boats. BBC Future, 7 June 2018 (author is Oceana's chief policy officer for North America).", "title": "How to spot the secretive activities of rogue fishing boats", "url": "https://www.bbc.com/future/article/20180607-how-to-spot-the-secretive-activities-of-rogue-fishing-boats", "date": "2018-06-07", "written_by": "makers", "quote": "Then we run them all through the Global Fishing Watch algorithm, and use machine learning and cloud computing to identify when they are fishing." }, { "kind": "press release", "citation": "Cutlip K. Indonesia Makes its Fishing Fleet Visible to the World. Global Fishing Watch, 7 June 2017.", "title": "Indonesia Makes its Fishing Fleet Visible to the World", "url": "https://globalfishingwatch.org/transparency/indonesia-shares-vms-with-global-fishing-watch/", "date": "2017-06-07", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kiribati used the Google-Oceana-SkyTruth platform to document Marshalls 203 fishing illegally in PIPA; a US$1 million fine plus a US$1 million goodwill grant followed (reported September 2016).", "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Global Fishing Watch (AIS-based fishing detection using machine learning)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Oceana, SkyTruth and Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Global Fishing Watch data lets Kiribati fine a vessel US$1 million for fishing in the Phoenix Islands Protected Area", "headline": "Global Fishing Watch helps Kiribati fine a vessel US$1 million" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d51c291149dabfc21514acaa1e3cb88a3cec30bb0c61bb885ded2889f9236ed8" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Global Fishing Watch, developed by Oceana, SkyTruth and Google, launched publicly at the Our Ocean conference on 15 September 2016. It processes AIS tracks of tens of thousands of vessels. In Oceana's description, it uses machine learning and cloud computing to identify when they are fishing. Before launch, the Washington Post reported, the government of Kiribati used it to document how a tuna-fishing vessel had operated illegally. That was inside the Phoenix Islands Protected Area, declared off-limits to commercial fishing in early 2015, resulting in a US$1 million fine. Oceana board member Ted Danson wrote in Grist that the vessel was the Marshall Islands-flagged Marshalls 203. The fine was against its owner, Central Pacific Fishing Company. A goodwill arrangement to pay a further US$1 million as a grant came with it. Oceana's Jacqueline Savitz wrote in BBC Future in 2018 that the technology helped Kiribati extract a US$2 million fine, about 1% of GDP. The cited sources do not date the fine.", "replication": { "produced_by": [ "A vessel broadcasting AIS inside a no-take area that Kiribati closed to commercial fishing from 1 January 2015", "A government willing to prosecute on third-party satellite-derived evidence" ], "must_hold": [ "AIS transmitters left on (the BBC piece notes vessels can switch them off)", "Admissibility of AIS-derived evidence under the coastal state's law" ], "echoes": [ { "date": "2024-01-03", "setting": "Global coastal waters 2017–2021: Global Fishing Watch deep-learning mapping of untracked industrial vessels", "model_version": "SAR imagery with deep-learning models", "outcome": "unknown", "source": "Paolo FS, Kroodsma D et al. 2024, Nature, doi:10.1038/s41586-023-06825-8" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0166", "case_url": "records/AIBD-0166.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "The model and active-learning rules were built to predict which Flint homes had lead or galvanized service lines and to direct inspections and replacements. The improved hit rate was the intended use." }, { "label": "AI contribution", "value": "Predictions helped officials prioritize inspections and replacements. The field comparison also involved changes in geography and policy, so it does not isolate the model’s contribution. The monetary saving came from simulation." } ], "filters": { "topic": "Energy and water", "benefit": "reported", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0166", "title": "ActiveRemediation helps Flint locate hazardous water pipes", "summary": "ActiveRemediation helped Flint prioritize homes for inspection and replacement of lead or galvanized-steel service lines.", "date_observed": "2016-01-01", "date_basis": "event", "date_precision": "year", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "University researchers worked with Flint’s service-line replacement program from summer 2016. ActiveRemediation used property records and confirmed pipe materials to recommend inspections. Its held-out prediction accuracy was 91.6%. The researchers reported that the program had replaced hazardous lines at 4,419 homes by September 2017. A separate simulation estimated savings of about US$622 per replacement. Independent reporting later documented a decline in inspection success after the contractor changed its approach, alongside changes in geography and policy. A March 2019 court order required prioritization using the model’s address lists. The field evidence does not isolate the model’s effect on residents’ health or realized expenditure.", "category": "benefit created", "unexpected": false, "purpose": "The model and active-learning rules were built to predict which Flint homes had lead or galvanized service lines and to direct inspections and replacements. The improved hit rate was the intended use.", "system": "ActiveRemediation, combining XGBoost predictions, a hierarchical Bayesian spatial model, and active learning", "model_version": null, "developer": "University of Michigan (Jacob Abernethy, Alex Chojnacki, Arya Farahi, Eric Schwartz, Jared Webb)", "deployer": "City of Flint (FAST Start service-line replacement program)", "location": "Flint, United States", "domain": "Water infrastructure", "segment": "Lead service-line replacement", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The public-health good of lead removal is the end; the documented gain is the hit rate per excavation.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents benefited from identification and replacement of hazardous service lines, without a measured health effect attributable to the model." }, { "group": "organizational", "depth": 2, "reason": "The program gained a measured capability to predict pipe materials and prioritize work." }, { "group": "societal", "depth": 3, "reason": "Flint replaced hazardous service lines at thousands of homes, with predictions helping prioritize inspections." } ], "ai_role": { "status": "assessed", "statement": "Predictions helped officials prioritize inspections and replacements. The field comparison also involved changes in geography and policy, so it does not isolate the model’s contribution. The monetary saving came from simulation." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Abernethy, Jacob, Alex Chojnacki, Arya Farahi, Eric Schwartz, and Jared Webb. “ActiveRemediation: The Search for Lead Pipes in Flint, Michigan.” KDD 2018. https://doi.org/10.1145/3219819.3219896.", "title": "ActiveRemediation: The Search for Lead Pipes in Flint, Michigan", "url": "https://arxiv.org/abs/1806.10692", "date": "2018-06-10", "written_by": "makers", "quote": null }, { "kind": "news report", "citation": "Madrigal, Alexis C. “How a Feel-Good AI Story Went Wrong in Flint.” The Atlantic, January 3, 2019.", "title": "How a Feel-Good AI Story Went Wrong in Flint", "url": "https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/", "date": "2019-01-03", "written_by": "independent", "quote": null }, { "kind": "court document", "citation": "Concerned Pastors for Social Action v. Khouri, No. 16-10277, ECF No. 208 (E.D. Mich. 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A separate inflation base year is not stated." } }, "economics[].gross_net": { "state": "value_present", "value": { "kind": "text", "text": "The estimate includes excavation and replacement costs, but not every cost of operating the program." }, "provenance": { "kind": "source_review", "basis": "§4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The estimate includes excavation and replacement costs, but not every cost of operating the program." } }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "text", "text": "Modeled cost savings." }, "provenance": { "kind": "source_review", "basis": "Table 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Modeled cost savings." } }, "economics[].period_unit": { "state": "value_present", "value": { "kind": "text", "text": "Per replacement, with an extrapolation to 18,000 replacements." }, "provenance": { "kind": "source_review", "basis": "Table 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Per replacement, with an extrapolation to 18,000 replacements." } }, "economics[].realization": { "state": "value_present", "value": { "kind": "text", "text": "The savings were simulated. 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They are not an audited reduction in city expenditure." } }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "text", "text": "The modeled savings would reduce the cost of the publicly funded replacement program." }, "provenance": { "kind": "source_review", "basis": "§1; §4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The modeled savings would reduce the cost of the publicly funded replacement program." } }, "economics[].return_payback": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "§4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "No complete return-on-investment or payback calculation is reported.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "No complete return-on-investment or payback calculation is reported." } }, "economics[].valuation_method": { "state": "value_present", "value": { "kind": "text", "text": "Researchers simulated inspection and replacement sequences, applied fixed unit costs, and extrapolated the difference." }, "provenance": { "kind": "source_review", "basis": "§4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers simulated inspection and replacement sequences, applied fixed unit costs, and extrapolated the difference." } }, "evaluations[].ai_uplift": { "state": "value_present", "value": { "kind": "text", "text": "The estimated cost improvement came from a simulation. The field results do not isolate the model from crews, funding, and policy decisions." }, "provenance": { "kind": "source_review", "basis": "§4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The estimated cost improvement came from a simulation. The field results do not isolate the model from crews, funding, and policy decisions." } }, "evaluations[].comparator": { "state": "value_present", "value": { "kind": "text", "text": "The cost simulation compared the proposed strategy with the sequence of work in the city’s records." }, "provenance": { "kind": "source_review", "basis": "§4.2; Table 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The cost simulation compared the proposed strategy with the sequence of work in the city’s records." } }, "evaluations[].design": { "state": "value_present", "value": { "kind": "text", "text": "Researchers tested predictions on held-out homes and compared inspection strategies in simulations." }, "provenance": { "kind": "source_review", "basis": "§§4.1–4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Researchers tested predictions on held-out homes and compared inspection strategies in simulations." } }, "evaluations[].effect": { "state": "value_present", "value": { "kind": "text", "text": "The held-out prediction accuracy was 91.6%. The simulated strategy lowered estimated cost per replacement by 10.7%." }, "provenance": { "kind": "source_review", "basis": "§4.1.1; Table 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The held-out prediction accuracy was 91.6%. The simulated strategy lowered estimated cost per replacement by 10.7%." } }, "evaluations[].exposure": { "state": "value_present", "value": { "kind": "text", "text": "Predictions were updated as crews recorded new inspections and replacements." }, "provenance": { "kind": "source_review", "basis": "§§2.3–3.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Predictions were updated as crews recorded new inspections and replacements." } }, "evaluations[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0166-E01", "AIBD-0166-E02" ], "role": "evaluations" }, "provenance": { "kind": "source_review", "basis": "Identifiers assigned to the evaluations described here.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0166-E01, AIBD-0166-E02" } }, "evaluations[].measurement_method": { "state": "value_present", "value": { "kind": "text", "text": "Excavation results supplied the pipe-material labels. Validation and simulated work sequences used those records." }, "provenance": { "kind": "source_review", "basis": "§4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Excavation results supplied the pipe-material labels. Validation and simulated work sequences used those records." } }, "evaluations[].metric": { "state": "value_present", "value": { "kind": "text", "text": "Pipe-material prediction, inspection success, and modeled cost per replacement." }, "provenance": { "kind": "source_review", "basis": "§4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Pipe-material prediction, inspection success, and modeled cost per replacement." } }, "evaluations[].reproduction_materials": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "§3; §4.1.1, n. 6.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The paper describes the algorithms and simulations. It does not supply all settings needed to reproduce the deployed system.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The paper describes the algorithms and simulations. It does not supply all settings needed to reproduce the deployed system." } }, "evaluations[].setting_sample": { "state": "value_present", "value": { "kind": "text", "text": "The database covered 55,893 parcels. The main accuracy test held out 1,606 homes with known pipe materials." }, "provenance": { "kind": "source_review", "basis": "§2.1; §4.1.1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The database covered 55,893 parcels. The main accuracy test held out 1,606 homes with known pipe materials." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "text", "text": "Known pipe materials came from a selected set of inspected homes. The simulated costs omitted travel and other logistical constraints." }, "provenance": { "kind": "source_review", "basis": "§§4.1–4.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Known pipe materials came from a selected set of inspected homes. The simulated costs omitted travel and other logistical constraints." } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "text", "text": "A March 2019 court order required the city to prioritize addresses identified by the predictive model, subject to defined exceptions." }, "provenance": { "kind": "source_review", "basis": "¶¶1–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A March 2019 court order required the city to prioritize addresses identified by the predictive model, subject to defined exceptions." } }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "City officials set priorities, contractors confirmed pipe materials, and researchers maintained the model." }, "provenance": { "kind": "source_review", "basis": "§§2–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "City officials set priorities, contractors confirmed pipe materials, and researchers maintained the model." } }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0166" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "value_present", "value": { "kind": "text", "text": "The system depended on digitized parcel records, reliable excavation labels, a shared database, and crews who could inspect and replace pipes." }, "provenance": { "kind": "source_review", "basis": "§2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system depended on digitized parcel records, reliable excavation labels, a shared database, and crews who could inspect and replace pipes." } }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Residents whose hazardous service lines were identified and replaced." }, "provenance": { "kind": "source_review", "basis": "Table 1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Residents whose hazardous service lines were identified and replaced." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "By September 2017, the program had replaced hazardous lines at 4,419 homes and identified pipe materials at 6,506." }, "provenance": { "kind": "source_review", "basis": "Table 1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "By September 2017, the program had replaced hazardous lines at 4,419 homes and identified pipe materials at 6,506." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "text", "text": "Improved accuracy in locating hazardous service lines." }, "provenance": { "kind": "source_review", "basis": "§4.1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Improved accuracy in locating hazardous service lines." } }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "text", "text": "The model helped direct inspections toward homes likely to have hazardous pipes." }, "provenance": { "kind": "source_review", "basis": "§§3–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The model helped direct inspections toward homes likely to have hazardous pipes." } }, "outcomes[].distribution": { "state": "value_present", "value": { "kind": "text", "text": "Predictions varied by address and neighborhood. The benefit depended on whether officials used them to set priorities." }, "provenance": { "kind": "source_review", "basis": "§§2–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Predictions varied by address and neighborhood. The benefit depended on whether officials used them to set priorities." } }, "outcomes[].duration": { "state": "value_present", "value": { "kind": "text", "text": "Replacement permanently changed the identified service lines. The paper did not measure residents’ later health." }, "provenance": { "kind": "source_review", "basis": "§§2–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Replacement permanently changed the identified service lines. The paper did not measure residents’ later health." } }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "text", "text": "Residents and the city’s water-infrastructure program." }, "provenance": { "kind": "source_review", "basis": "§§1–2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Residents and the city’s water-infrastructure program." } }, "outcomes[].status": { "state": "value_present", "value": { "kind": "text", "text": "The system was used in the field. Accuracy was tested retrospectively, and monetary savings were modeled." }, "provenance": { "kind": "source_review", "basis": "§4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system was used in the field. Accuracy was tested retrospectively, and monetary savings were modeled." } }, "outcomes[].tradeoffs": { "state": "value_present", "value": { "kind": "text", "text": "Residents and officials also sought broad coverage and certainty about individual homes. These priorities could change the order of excavation." }, "provenance": { "kind": "source_review", "basis": "Interviews about the 2018 change in priorities.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Residents and officials also sought broad coverage and certainty about individual homes. These priorities could change the order of excavation." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "text", "text": "Finding and removing hazardous pipes was the intended benefit." }, "provenance": { "kind": "source_review", "basis": "§1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Finding and removing hazardous pipes was the intended benefit." } }, "places[]": { "state": "value_present", "value": { "kind": "text", "text": "Flint, Michigan, United States." }, "provenance": { "kind": "source_review", "basis": "§1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Flint, Michigan, United States." } }, "purpose": { "state": "value_present", "value": { "kind": "text", "text": "The system estimated which homes were most likely to have hazardous service lines." }, "provenance": { "kind": "source_review", "basis": "§1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system estimated which homes were most likely to have hazardous service lines." } }, "record_dates": { "state": "value_present", "value": { "kind": "structured", "data": { "start": "2016", "end": "2017-09", "precision": "season to month", "display": "Summer 2016–September 2017", "refs": [ "AIBD-0166-source-0001" ], "current_date_observed": "2018-07-19", "instruction": "Use the observed result period for the case date. Keep the full study period separately; the normalized sort date does not imply a more precise observation.", "observed_result_period": "2016", "observed_result_precision": "year", "observed_result_basis": "Model-guided fieldwork began in summer 2016." } }, "provenance": { "kind": "source_review", "basis": "Original study timing.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Field work began in summer 2016. The paper reports replacements and inspections through September 2017." } }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "text", "text": "Useful parcel records, verified inspection results, and a working relationship with the people who choose excavation sites are necessary." }, "provenance": { "kind": "source_review", "basis": "§§2–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Useful parcel records, verified inspection results, and a working relationship with the people who choose excavation sites are necessary." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.michiganpublic.org/news/2019-04-25/in-flint-algorithm-predicts-lead-pipe-locations-as-replacement-work-continues; https://www.mswmag.com/online_exclusives/2019/02/flint-to-revert-back-to-data-driven-predictive-model-for-lead-pipe-replacements", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/University_of_Michigan; https://en.wikipedia.org/wiki/Flint,_Michigan", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to City of Flint and University of Michigan.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the original evidence and supporting material; updated system details, methods, outcomes, limitations, costs, and source lineage.", "source": "https://arxiv.org/abs/1806.10692", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "text", "text": "Water infrastructure and public health." }, "provenance": { "kind": "source_review", "basis": "§1.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Water infrastructure and public health." } }, "sources[].access_version": { "state": "value_present", "value": { "kind": "text", "text": "The revised original paper, the complete news article, and the relevant court-order provisions were read. Earlier versions and the underlying stipulation are preserved in the source chain." }, "provenance": { "kind": "source_review", "basis": "§§2–4, Tables 1 and 5.; Field totals and interviews with Michael McDaniel, Karen Weaver, Alan Wong, and Eric Schwartz.; ¶¶1–6, pp. 1–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001", "AIBD-0166-source-0002", "AIBD-0166-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The revised original paper, the complete news article, and the relevant court-order provisions were read. Earlier versions and the underlying stipulation are preserved in the source chain." } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "text", "text": "The original paper establishes the methods and modeled savings. Independent interviews describe implementation. The court order establishes the required prioritization." }, "provenance": { "kind": "source_review", "basis": "§§2–4, Tables 1 and 5.; Field totals and interviews with Michael McDaniel, Karen Weaver, Alan Wong, and Eric Schwartz.; ¶¶1–6, pp. 1–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001", "AIBD-0166-source-0002", "AIBD-0166-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The original paper establishes the methods and modeled savings. Independent interviews describe implementation. 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The Atlantic adds independent reporting. The court order is evidence of a decision, not an independent estimate of lives or money saved." }, "provenance": { "kind": "source_review", "basis": "§§2–4, Tables 1 and 5.; Field totals and interviews with Michael McDaniel, Karen Weaver, Alan Wong, and Eric Schwartz.; ¶¶1–6, pp. 1–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001", "AIBD-0166-source-0002", "AIBD-0166-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The researchers evaluated their own system. The Atlantic adds independent reporting. The court order is evidence of a decision, not an independent estimate of lives or money saved." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0166-source-0001", "retained_source_order": [ "AIBD-0166-source-0001", "AIBD-0166-source-0002", "AIBD-0166-source-0003", "AIBD-0166-source-0004" ], "origins": [ { "url": "https://arxiv.org/abs/1806.10692", "title": "ActiveRemediation: The Search for Lead Pipes in Flint, Michigan", "locator": "§§2–4, Tables 1 and 5.", "contribution": "Original methods, field implementation, validation, and simulated costs.", "relationship": "Primary report by the researchers who developed and helped deploy the system.", "source_refs": [ "AIBD-0166-source-0001" ], "access": "The original arXiv version history and full v2 PDF dated 17 August 2018 were inspected. The first submission date is 10 June 2018.", "publication_date": "2018-06-10" }, { "url": "https://arxiv.org/pdf/1806.10692v2", "locator": "17 August 2018 revision.", "contribution": "17 August 2018 revision.", "relationship": "Access copy, supplement, or earlier version of the same work.", "source_refs": [ "AIBD-0166-source-0001" ] }, { "url": "https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/", "title": "How a Feel-Good AI Story Went Wrong in Flint", "locator": "Field totals and interviews with Michael McDaniel, Karen Weaver, Alan Wong, and Eric Schwartz.", "contribution": "Independent reporting about model use, excavation results, and the contractor’s later change in priorities.", "relationship": "Original interviews and reporting. The model description and some estimates derive from F1 and court filings.", "source_refs": [ "AIBD-0166-source-0002" ], "access": "The original article’s body was read.", "publication_date": "2019-01-03" }, { "url": "https://www.nrdc.org/sites/default/files/declaration-eric-schwartz-flint-20181001.pdf", "title": "Declaration of Eric Schwartz, October 1, 2018", "locator": "ECF 203-4, model development and communications with AECOM.", "contribution": "Primary declaration underlying part of the implementation account. Targeted passages were read; the whole filing was not reviewed.", "relationship": "Primary declaration underlying part of the implementation account. Targeted passages were read; the whole filing was not reviewed.", "source_refs": [ "AIBD-0166-source-0002" ] }, { "url": "https://www.nrdc.org/sites/default/files/order-amending-settlement-agreement-flint-20190326.pdf", "title": "Order Amending Settlement Agreement", "locator": "¶¶1–6, pp. 1–4.", "contribution": "The court required prioritization of addresses selected using the predictive model, with specified exceptions and reporting.", "relationship": "Original judicial order. It establishes the required decision process, not a causal finding about the system’s benefit.", "source_refs": [ "AIBD-0166-source-0003" ], "access": "Original PDF downloaded; ¶¶1–6 were read.", "publication_date": "2019-03-26", "retention_note": "Decision evidence only. The order does not establish a causal benefit finding." }, { "url": "https://www.nrdc.org/sites/default/files/notice-modified-settlement-agreement-flint-20190212.pdf", "title": "Stipulation and Notice of Proposed Amendments to Settlement Agreement", "locator": "ECF 207, pp. 1–6; ¶1 n. 1 cites ActiveRemediation.", "contribution": "The agreement that the court approved. Relevant original pages were read.", "relationship": "The agreement that the court approved. Relevant original pages were read.", "source_refs": [ "AIBD-0166-source-0003" ] }, { "url": "https://www.michiganpublic.org/news/2019-04-25/in-flint-algorithm-predicts-lead-pipe-locations-as-replacement-work-continues", "title": "In Flint, algorithm predicts lead pipe locations as replacement work continues", "locator": "“A goal of efficiency” and the original interview with Eric Schwartz.", "contribution": "A later firsthand interview reports that the research team was again working with Flint in 2019.", "relationship": "Independent interview with a developer. It establishes his account of renewed implementation, not an independent measurement of the modeled saving.", "source_refs": [ "AIBD-0166-source-0004" ], "access": "The original article’s full substantive text was read on 25 October 2026. The audio was not played.", "publication_date": "2019-04-25" } ], "excluded_or_consolidated_sources": [ { "old_source_ref": "AIBD-0166-source-0006", "url": "https://www.mswmag.com/online_exclusives/2019/02/flint-to-revert-back-to-data-driven-predictive-model-for-lead-pipe-replacements", "reason": "A summary or secondary account was replaced by the original evidence. Any unique earlier quotation remains in the audit artifact." } ], "review_scope": "Original evidence, systems, evaluation methods and results, costs, chronology, human roles, limitations, and follow-up.", "chronology_source": { "source_ref": "AIBD-0166-source-0001", "date": "2018-06-10", "url": "https://arxiv.org/abs/1806.10692v1", "title": "ActiveRemediation: The Search for Lead Pipes in Flint, Michigan", "role": "First public manuscript. Field work began in summer 2016; publication is not the deployment date." }, "source_relationship_note": "Versions, supplements, reanalyses, and repeated developer statements are not independent replications." } }, "provenance": { "kind": "source_review", "basis": "The original evidence and exact locators are preserved in this assessment.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001", "AIBD-0166-source-0002", "AIBD-0166-source-0003", "AIBD-0166-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The original model paper supplies the validation and simulation. The court order follows the parties’ February 2019 stipulation." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "text", "text": "ActiveRemediation helped Flint prioritize homes for inspection and replacement of lead or galvanized-steel service lines." }, "provenance": { "kind": "source_review", "basis": "§§2–4, Tables 1 and 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ActiveRemediation helped Flint prioritize homes for inspection and replacement of lead or galvanized-steel service lines." } }, "systems[].autonomy_permissions": { "state": "value_present", "value": { "kind": "text", "text": "The system recommended priorities. City officials and contractors decided where to excavate and replaced the pipes." }, "provenance": { "kind": "source_review", "basis": "§§2–4.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system recommended priorities. City officials and contractors decided where to excavate and replaced the pipes." } }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The system estimated pipe materials and ranked homes for further investigation." }, "provenance": { "kind": "source_review", "basis": "§3.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The system estimated pipe materials and ranked homes for further investigation." } }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "A shared database and a password-protected Flask application let city staff and contractors review maps and add excavation results." }, "provenance": { "kind": "source_review", "basis": "§2.3.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A shared database and a password-protected Flask application let city staff and contractors review maps and add excavation results." } }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "Parcel records, property characteristics, and confirmed pipe materials produced address-level probabilities and inspection priorities." }, "provenance": { "kind": "source_review", "basis": "§§2.1–3.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Parcel records, property characteristics, and confirmed pipe materials produced address-level probabilities and inspection priorities." } }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "XGBoost classification trees with a hierarchical Bayesian model for spatial patterns. An active-learning policy selected further inspections." }, "provenance": { "kind": "source_review", "basis": "§§3.1–3.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "XGBoost classification trees with a hierarchical Bayesian model for spatial patterns. An active-learning policy selected further inspections." } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "§3; §4.1.1, n. 6.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The paper names the algorithms but does not identify an XGBoost release or a reproducible deployment configuration.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The paper names the algorithms but does not identify an XGBoost release or a reproducible deployment configuration." } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "text", "text": "ActiveRemediation." }, "provenance": { "kind": "source_review", "basis": "Title and §3.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ActiveRemediation." } }, "systems[].provider": { "state": "value_present", "value": { "kind": "text", "text": "A research team from the University of Michigan, Georgia Tech, and Brigham Young University." }, "provenance": { "kind": "source_review", "basis": "Author affiliations.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A research team from the University of Michigan, Georgia Tech, and Brigham Young University." } }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "text", "text": "ActiveRemediation helps Flint locate hazardous water pipes" }, "provenance": { "kind": "source_review", "basis": "§§2–4, Tables 1 and 5.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "ActiveRemediation helps Flint locate hazardous water pipes" } }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "ded7efa1cccb04911fafe17bc25bbadc63f7fe17832804028c9c7cac855e0272" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "text", "text": "Crews inspected pipes and recorded their materials. Researchers used those results to update predictions and recommend further inspections." }, "provenance": { "kind": "source_review", "basis": "§§2.3–3.2.", "record_fields": [], "source_refs": [ "AIBD-0166-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Crews inspected pipes and recorded their materials. Researchers used those results to update predictions and recommend further inspections." } } }, "summary": { "total": 91, "addressed": 91, "known": 75, "reviewed": 76, "record_mapped": 3, "not_assessed": 12, "complete": false, "by_state": { "value_present": 75, "not_reported": 4, "unresolved": 0, "not_assessed": 12, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0375", "case_url": "records/AIBD-0375.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The provincial AI camera system was installed to monitor traffic routes and key areas and detect violations for investigation and enforcement, per Tuổi Trẻ. Identifying people gathering with weapons is that purpose." }, { "label": "AI contribution", "value": "The facial recognition of the provincial AI camera system supplied the identities. Tuổi Trẻ and VietNamNet say all 18 faces were accurately identified within about 30 minutes, after officers monitoring camera data had noticed the gathering. The summons, questioning and confessions were police work. The sources are three national outlets reporting the provincial police's own account, with no vendor, algorithm or accuracy figure given." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Vietnam" ] }, "record": { "id": "AIBD-0375", "title": "Phú Thọ provincial police AI cameras put faces to all 18 teenagers found gathering with weapons to fight in Việt Trì within about 30 minutes; all were summoned and confessed", "summary": "Tuổi Trẻ (14 June 2026): on the night of 13 June Phú Thọ police saw a group preparing weapons to fight in Việt Trì; the AI camera system identified all 18 faces in about 30 minutes; all confessed.", "date_observed": "2026-06-13", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On the night of Saturday 13 June 2026, Phú Thọ provincial police detected a group of teenagers gathering in Việt Trì ward. Officers were monitoring and mining data from the camera system. The teenagers were preparing weapons and vehicles to fight and disturb public order, Tuổi Trẻ reported on 14 June. Within about 30 minutes the AI camera system of the provincial command center had accurately identified the faces of all 18 participants. They were aged 14 to 18. They came from Việt Trì, Vân Phú and Nông Trang wards and Phù Ninh and Bình Phú communes (Tuổi Trẻ, VietNamNet). Tiền Phong says the whole case was clarified in just over two hours. It says 17 of the 18 were pupils of schools including Vũ Thê Lang High School and Dữu Lâu Middle School. One had dropped out. It says the provincial police summoned all 18, and that at the police station they admitted the violations. No report says whether a fight took place.", "category": "protective response", "unexpected": false, "purpose": "The provincial AI camera system was installed to monitor traffic routes and key areas and detect violations for investigation and enforcement, per Tuổi Trẻ. Identifying people gathering with weapons is that purpose.", "system": "Phú Thọ provincial police AI camera system with facial recognition (provincial command and information center)", "model_version": null, "developer": null, "deployer": "Công an tỉnh Phú Thọ (Phú Thọ Provincial Police)", "location": "Việt Trì, Vietnam", "domain": "Policing / public order", "segment": "Public-space facial recognition of minors", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-06-14-ba55" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "Phú Thọ provincial police identified all 18 participants within about 30 minutes. The case was clarified in just over two hours, with all 18 summoned and confessing, per Tuổi Trẻ, VietNamNet and Tiền Phong." }, { "group": "societal", "depth": null, "reason": "Residents of Việt Trì were spared a street fight only if one was about to happen. The reports say the group was preparing weapons and vehicles, but they do not say whether a fight occurred or was interrupted." } ], "ai_role": { "status": "assessed", "statement": "The facial recognition of the provincial AI camera system supplied the identities. Tuổi Trẻ and VietNamNet say all 18 faces were accurately identified within about 30 minutes, after officers monitoring camera data had noticed the gathering. The summons, questioning and confessions were police work. The sources are three national outlets reporting the provincial police's own account, with no vendor, algorithm or accuracy figure given." }, "evidence": [ { "kind": "news report", "citation": "Chí Tuệ (2026). Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố. Tuổi Trẻ Online, 14 June 2026.", "title": "Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố", "url": "https://tuoitre.vn/tu-camera-ai-cong-an-chi-mat-30-phut-nhan-dien-18-thanh-thieu-nien-tu-tap-danh-nhau-tren-pho-20260614143550899.htm", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ 18 thanh, thiếu niên tham gia đã được hệ thống camera AI nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Đức Hoàng (2026). 18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút. VietNamNet, 14 June 2026.", "title": "18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút", "url": "https://vietnamnet.vn/18-thanh-thieu-nien-gay-roi-bi-camera-ai-o-phu-tho-nhan-dien-sau-30-phut-2525799.html", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ các đối tượng tham gia đã được hệ thống nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Viết Hà (2026). Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí. Tiền Phong, 14 June 2026.", "title": "Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí", "url": "https://tienphong.vn/camera-ai-tom-gon-nhom-hoc-sinh-tu-tap-mang-theo-hung-khi-post1851303.tpo", "date": "2026-06-14", "written_by": "independent", "quote": "Hệ thống camera giám sát ứng dụng trí tuệ nhân tạo đã nhanh chóng nhận diện các đối tượng, giúp công an làm rõ toàn bộ vụ việc" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ. OECD.AI, 14 June 2026.", "title": "AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ", "url": "https://oecd.ai/en/incidents/2026-06-14-ba55", "date": "2026-06-14", "written_by": "independent", "quote": "AI-powered surveillance cameras enabled police to quickly identify and apprehend 18 teenagers preparing for a violent public disturbance" } ], "replication": { "produced_by": [ "A provincial camera network with facial recognition feeding the police command center", "Officers watching camera data at night who noticed the gathering", "Reference images that let the system put names to 14-to-18-year-olds", "Police able to summon the 18 and their families within hours" ], "must_hold": [ "Camera coverage of the gathering point with usable faces", "The subjects are enrolled in the reference database", "Police verification of each match before action" ], "echoes": [] }, "paired_incident": null, "headline": "AI cameras name 18 armed teenagers in Việt Trì within half an hour", "metric": "identified all 18 faces in about 30 minutes", "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "Vietnam", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-14-ba55", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://congan.phutho.gov.vn/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0375", "catalog_version": "1.0.0", "source_fingerprint": "cbc3b4ff7f19e78060c6ad0dfbf0d0de7daffde4e0020f6cba8a81fa6f651c51", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0375-source-0001", "AIBD-0375-source-0002", "AIBD-0375-source-0003", "AIBD-0375-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Công an tỉnh Phú Thọ (Phú Thọ Provincial Police)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The facial recognition of the provincial AI camera system supplied the identities. Tuổi Trẻ and VietNamNet say all 18 faces were accurately identified within about 30 minutes, after officers monitoring camera data had noticed the gathering. The summons, questioning and confessions were police work. The sources are three national outlets reporting the provincial police's own account, with no vendor, algorithm or accuracy figure given." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The facial recognition of the provincial AI camera system supplied the identities. Tuổi Trẻ and VietNamNet say all 18 faces were accurately identified within about 30 minutes, after officers monitoring camera data had noticed the gathering. The summons, questioning and confessions were police work. The sources are three national outlets reporting the provincial police's own account, with no vendor, algorithm or accuracy figure given." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Phú Thọ provincial police identified all 18 participants within about 30 minutes. The case was clarified in just over two hours, with all 18 summoned and confessing, per Tuổi Trẻ, VietNamNet and Tiền Phong." }, { "group": "societal", "reason": "Residents of Việt Trì were spared a street fight only if one was about to happen. The reports say the group was preparing weapons and vehicles, but they do not say whether a fight occurred or was interrupted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Phú Thọ provincial police identified all 18 participants within about 30 minutes. The case was clarified in just over two hours, with all 18 summoned and confessing, per Tuổi Trẻ, VietNamNet and Tiền Phong." }, { "group": "societal", "reason": "Residents of Việt Trì were spared a street fight only if one was about to happen. The reports say the group was preparing weapons and vehicles, but they do not say whether a fight occurred or was interrupted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Phú Thọ provincial police identified all 18 participants within about 30 minutes. The case was clarified in just over two hours, with all 18 summoned and confessing, per Tuổi Trẻ, VietNamNet and Tiền Phong." }, { "group": "societal", "reason": "Residents of Việt Trì were spared a street fight only if one was about to happen. The reports say the group was preparing weapons and vehicles, but they do not say whether a fight occurred or was interrupted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "identified all 18 faces in about 30 minutes", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-06-14-ba55" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0375" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On the night of Saturday 13 June 2026, Phú Thọ provincial police detected a group of teenagers gathering in Việt Trì ward. Officers were monitoring and mining data from the camera system. The teenagers were preparing weapons and vehicles to fight and disturb public order, Tuổi Trẻ reported on 14 June. Within about 30 minutes the AI camera system of the provincial command center had accurately identified the faces of all 18 participants. They were aged 14 to 18. They came from Việt Trì, Vân Phú and Nông Trang wards and Phù Ninh and Bình Phú communes (Tuổi Trẻ, VietNamNet). Tiền Phong says the whole case was clarified in just over two hours. It says 17 of the 18 were pupils of schools including Vũ Thê Lang High School and Dữu Lâu Middle School. One had dropped out. It says the provincial police summoned all 18, and that at the police station they admitted the violations. No report says whether a fight took place.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Phú Thọ provincial police identified all 18 participants within about 30 minutes. The case was clarified in just over two hours, with all 18 summoned and confessing, per Tuổi Trẻ, VietNamNet and Tiền Phong." }, { "group": "societal", "reason": "Residents of Việt Trì were spared a street fight only if one was about to happen. The reports say the group was preparing weapons and vehicles, but they do not say whether a fight occurred or was interrupted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The provincial AI camera system was installed to monitor traffic routes and key areas and detect violations for investigation and enforcement, per Tuổi Trẻ. Identifying people gathering with weapons is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Việt Trì, Vietnam", "deployer_country": "Vietnam" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The provincial AI camera system was installed to monitor traffic routes and key areas and detect violations for investigation and enforcement, per Tuổi Trẻ. Identifying people gathering with weapons is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-06-13", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-14-ba55", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://congan.phutho.gov.vn/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A provincial camera network with facial recognition feeding the police command center", "Officers watching camera data at night who noticed the gathering", "Reference images that let the system put names to 14-to-18-year-olds", "Police able to summon the 18 and their families within hours" ], "must_hold": [ "Camera coverage of the gathering point with usable faces", "The subjects are enrolled in the reference database", "Police verification of each match before action" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-14-ba55", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://congan.phutho.gov.vn/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / public order", "segment": "Public-space facial recognition of minors" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Chí Tuệ (2026). Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố. Tuổi Trẻ Online, 14 June 2026.", "title": "Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố", "url": "https://tuoitre.vn/tu-camera-ai-cong-an-chi-mat-30-phut-nhan-dien-18-thanh-thieu-nien-tu-tap-danh-nhau-tren-pho-20260614143550899.htm", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ 18 thanh, thiếu niên tham gia đã được hệ thống camera AI nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Đức Hoàng (2026). 18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút. VietNamNet, 14 June 2026.", "title": "18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút", "url": "https://vietnamnet.vn/18-thanh-thieu-nien-gay-roi-bi-camera-ai-o-phu-tho-nhan-dien-sau-30-phut-2525799.html", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ các đối tượng tham gia đã được hệ thống nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Viết Hà (2026). Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí. Tiền Phong, 14 June 2026.", "title": "Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí", "url": "https://tienphong.vn/camera-ai-tom-gon-nhom-hoc-sinh-tu-tap-mang-theo-hung-khi-post1851303.tpo", "date": "2026-06-14", "written_by": "independent", "quote": "Hệ thống camera giám sát ứng dụng trí tuệ nhân tạo đã nhanh chóng nhận diện các đối tượng, giúp công an làm rõ toàn bộ vụ việc" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ. OECD.AI, 14 June 2026.", "title": "AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ", "url": "https://oecd.ai/en/incidents/2026-06-14-ba55", "date": "2026-06-14", "written_by": "independent", "quote": "AI-powered surveillance cameras enabled police to quickly identify and apprehend 18 teenagers preparing for a violent public disturbance" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0375-source-0001", "AIBD-0375-source-0002", "AIBD-0375-source-0003", "AIBD-0375-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Chí Tuệ (2026). Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố. Tuổi Trẻ Online, 14 June 2026.", "title": "Từ camera AI, công an chỉ mất 30 phút nhận diện 18 thanh thiếu niên tụ tập đánh nhau trên phố", "url": "https://tuoitre.vn/tu-camera-ai-cong-an-chi-mat-30-phut-nhan-dien-18-thanh-thieu-nien-tu-tap-danh-nhau-tren-pho-20260614143550899.htm", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ 18 thanh, thiếu niên tham gia đã được hệ thống camera AI nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Đức Hoàng (2026). 18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút. VietNamNet, 14 June 2026.", "title": "18 thanh thiếu niên gây rối bị camera AI ở Phú Thọ nhận diện sau 30 phút", "url": "https://vietnamnet.vn/18-thanh-thieu-nien-gay-roi-bi-camera-ai-o-phu-tho-nhan-dien-sau-30-phut-2525799.html", "date": "2026-06-14", "written_by": "independent", "quote": "Chỉ sau khoảng 30 phút, toàn bộ các đối tượng tham gia đã được hệ thống nhận diện chính xác khuôn mặt" }, { "kind": "news report", "citation": "Viết Hà (2026). Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí. Tiền Phong, 14 June 2026.", "title": "Camera AI 'tóm gọn' nhóm học sinh tụ tập mang theo hung khí", "url": "https://tienphong.vn/camera-ai-tom-gon-nhom-hoc-sinh-tu-tap-mang-theo-hung-khi-post1851303.tpo", "date": "2026-06-14", "written_by": "independent", "quote": "Hệ thống camera giám sát ứng dụng trí tuệ nhân tạo đã nhanh chóng nhận diện các đối tượng, giúp công an làm rõ toàn bộ vụ việc" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ. OECD.AI, 14 June 2026.", "title": "AI Surveillance Cameras Prevent Youth Gang Violence in Phú Thọ", "url": "https://oecd.ai/en/incidents/2026-06-14-ba55", "date": "2026-06-14", "written_by": "independent", "quote": "AI-powered surveillance cameras enabled police to quickly identify and apprehend 18 teenagers preparing for a violent public disturbance" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Tuổi Trẻ (14 June 2026): on the night of 13 June Phú Thọ police saw a group preparing weapons to fight in Việt Trì; the AI camera system identified all 18 faces in about 30 minutes; all confessed.", "account": "On the night of Saturday 13 June 2026, Phú Thọ provincial police detected a group of teenagers gathering in Việt Trì ward. Officers were monitoring and mining data from the camera system. The teenagers were preparing weapons and vehicles to fight and disturb public order, Tuổi Trẻ reported on 14 June. Within about 30 minutes the AI camera system of the provincial command center had accurately identified the faces of all 18 participants. They were aged 14 to 18. They came from Việt Trì, Vân Phú and Nông Trang wards and Phù Ninh and Bình Phú communes (Tuổi Trẻ, VietNamNet). Tiền Phong says the whole case was clarified in just over two hours. It says 17 of the 18 were pupils of schools including Vũ Thê Lang High School and Dữu Lâu Middle School. One had dropped out. It says the provincial police summoned all 18, and that at the police station they admitted the violations. No report says whether a fight took place." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Phú Thọ provincial police AI camera system with facial recognition (provincial command and information center)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Phú Thọ provincial police AI cameras put faces to all 18 teenagers found gathering with weapons to fight in Việt Trì within about 30 minutes; all were summoned and confessed", "headline": "AI cameras name 18 armed teenagers in Việt Trì within half an hour" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "cbc3b4ff7f19e78060c6ad0dfbf0d0de7daffde4e0020f6cba8a81fa6f651c51" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On the night of Saturday 13 June 2026, Phú Thọ provincial police detected a group of teenagers gathering in Việt Trì ward. Officers were monitoring and mining data from the camera system. The teenagers were preparing weapons and vehicles to fight and disturb public order, Tuổi Trẻ reported on 14 June. Within about 30 minutes the AI camera system of the provincial command center had accurately identified the faces of all 18 participants. They were aged 14 to 18. They came from Việt Trì, Vân Phú and Nông Trang wards and Phù Ninh and Bình Phú communes (Tuổi Trẻ, VietNamNet). Tiền Phong says the whole case was clarified in just over two hours. It says 17 of the 18 were pupils of schools including Vũ Thê Lang High School and Dữu Lâu Middle School. One had dropped out. It says the provincial police summoned all 18, and that at the police station they admitted the violations. No report says whether a fight took place.", "replication": { "produced_by": [ "A provincial camera network with facial recognition feeding the police command center", "Officers watching camera data at night who noticed the gathering", "Reference images that let the system put names to 14-to-18-year-olds", "Police able to summon the 18 and their families within hours" ], "must_hold": [ "Camera coverage of the gathering point with usable faces", "The subjects are enrolled in the reference database", "Police verification of each match before action" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0340", "case_url": "records/AIBD-0340.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Built to pinpoint non-visible underground leaks from satellite data so that crews can find and fix them. The leaks found in the trial are that purpose." }, { "label": "AI contribution", "value": "Only the vendor and the utility describe the AI role. The BBC caption says the AI-generated satellite technology detects moisture and movement in the ground, and Origin Tech describes AI-driven satellite insights. The system outputs suspected-leak points that Thames Water crews then locate and repair with conventional methods. The 800-leak and 8.7 million liters a day figures are Thames Water's estimates relayed by the BBC. They are reproduced in the utility's press release (Water Magazine) and trade press. Origin Tech's site adds vendor claims. No independent verification is among the sources." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0340", "title": "Origin Tech's AI satellite leak detection may have found over 800 leaks in an 18-week Thames Water trial, saving an estimated 8.7 million liters a day", "summary": "18-week Origin Orbit trial on Thames Water's network: over 800 leaks found, est. 8.7 million liters a day saved (utility figures); 13-month contract followed, target 100 million liters a day.", "date_observed": "2026-05-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Thames Water trialed Origin Orbit, the satellite leak-detection product of North East England start-up Origin Tech Ltd (founded 2019). The company and the utility describe it as using AI on satellite data to flag suspected underground leaks within a 30-meter search radius. Origin's co-founder John Marsden told the BBC the satellites pass over Swindon roughly every six days. He said they produce points on a map for Thames Water crews. Thames Water's statements say an 18-week trial found over 800 leaks across the network. They were reported by the BBC (20 May 2026) and reproduced in trade press. It saved an estimated 8.7 million liters of water a day, with several leaks running at more than 10 liters per second. On the strength of the trial, Thames Water signed a 13-month contract, launched in March 2026 per the utility's release. It deploys 20 specialist teams expected to find about 25 leaks a week each, and aims to cut leakage by 100 million liters a day. That makes it the first water company to integrate Origin Orbit into business-as-usual leakage operations. Origin Tech's own site adds a 2026 claim that Origin Orbit identified over 5,000 leaks (73.45 ML/d) across its customers. All figures originate with the utility and the vendor, and no regulator or independent measurement is among the sources. The trial dates are not given. Smart Water Magazine reported the result on 5 May 2026. Cambridgeshire County Council's AI inspection contract (April 2026) and Hertfordshire's ARRES robot (January 2024) have no outcomes on record. They are not among the sources.", "category": "benefit created", "unexpected": false, "purpose": "Built to pinpoint non-visible underground leaks from satellite data so that crews can find and fix them. The leaks found in the trial are that purpose.", "system": "Origin Orbit (Origin Tech satellite leak detection applying AI to satellite data to flag suspected leaks within a 30-meter search radius)", "model_version": "Origin Orbit®", "developer": "Origin Tech Ltd (North East England)", "deployer": "Thames Water (leakage operations; 20 Origin specialist teams under the 2026 contract)", "location": "Thames Water supply area, England (BBC report centered on Swindon, Wiltshire)", "domain": "Water utilities: leakage detection", "segment": "Private regulated water company", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": "Water lost to leaks is read under Trustible's “water consumption”; the saving is the utility's figure.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual customer outcome is documented. The BBC notes most of these leaks are never seen by customers." }, { "group": "organizational", "depth": 2, "reason": "Material to the utility's leakage operations by its own account. Over 800 leaks were found and an estimated 8.7 million liters a day saved in one trial. A 13-month business-as-usual contract followed. The estimate is the utility's own and unverified." }, { "group": "societal", "depth": 1, "reason": "Water was conserved for the public supply. Per the Environment Agency figure cited by the BBC, about 19% of UK water is lost to leakage. The scale claimed is modest and unverified." } ], "ai_role": { "status": "assessed", "statement": "Only the vendor and the utility describe the AI role. The BBC caption says the AI-generated satellite technology detects moisture and movement in the ground, and Origin Tech describes AI-driven satellite insights. The system outputs suspected-leak points that Thames Water crews then locate and repair with conventional methods. The 800-leak and 8.7 million liters a day figures are Thames Water's estimates relayed by the BBC. They are reproduced in the utility's press release (Water Magazine) and trade press. Origin Tech's site adds vendor claims. No independent verification is among the sources." }, "evidence": [ { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026.", "title": "Thames Water using AI technology to find leaks in Swindon", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "During a previous trial, 800 leaks across Thames Water's network were found." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (savings estimate attributed to the company).", "title": "Thames Water using AI technology to find leaks in Swindon (savings estimate)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The company said this helped to save an estimated 8.7 million litres of water per day, enough to fill more than three Olympic-sized swimming pools." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (image caption describing the technology).", "title": "Thames Water using AI technology to find leaks in Swindon (technology description)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The AI-generated satellite technology detects moisture and movement in the ground" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Press release reproduced by Water Magazine, 29 May 2026 (source credited as Thames Water).", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day/", "date": "2026-05-29", "written_by": "deployer", "quote": "During a previous 18-week trial, Origin Orbit® was able to find over 800 leaks across Thames Water's network, helping to save an estimated 8.7 million litres of water per day." }, { "kind": "news report", "citation": "Tempest, O. (2026). Thames Water signs AI satellite deal to plug 100 million litres of daily leaks. Smart Water Magazine, 5 May 2026 (no verbatim quote taken).", "title": "Thames Water signs AI satellite deal to plug 100 million litres of daily leaks", "url": "https://smartwatermagazine.com/news/thames-water/thames-water-signs-ai-satellite-deal-plug-100-million-litres-daily-leaks", "date": "2026-05-05", "written_by": "independent", "quote": null }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech in Partnership with Thames Water. Origin news, 29 May 2026.", "title": "Origin Tech in Partnership with Thames Water", "url": "https://www.origintech.co.uk/origin-news/origin-tech-in-partnership-with-thames-water", "date": "2026-05-29", "written_by": "makers", "quote": "By combining AI-driven satellite insights with operational expertise, Origin Tech is enabling faster, more precise detection of previously invisible leaks." }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech Featured in BBC News and BBC Wiltshire Radio. Origin news, 20 May 2026.", "title": "Origin Tech Featured in BBC News and BBC Wiltshire Radio", "url": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio", "date": "2026-05-20", "written_by": "makers", "quote": "Origin Orbit® has been able to identify over 5K leaks, which is over 73.45 ML/d of leakage" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Newsroom, 1 May 2026.", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "date": "2026-05-01", "written_by": "deployer", "quote": null }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South-East drought pressure intensifies. Newsroom, 24 August 2026.", "title": "Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South‑East drought pressure intensifies", "url": "https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies", "date": "2026-08-24", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Satellite passes about every six days over the network, processed into suspected-leak points within a 30-meter radius", "An 18-week trial in which Thames Water crews investigated the points with conventional ground methods", "A network with a large share of non-visible underground leaks (92% of Origin Orbit finds are non-visible, per the vendor)" ], "must_hold": [ "Satellite revisit frequency and ground conditions that allow moisture and movement to be detected", "Crew capacity to investigate points (20 teams at about 25 leaks a week each under the contract)", "Repair follow-through so that found leaks become saved water", "The utility's method for estimating liters saved per leak, which no independent source has checked" ], "echoes": [ { "date": "2026-05-29", "setting": "13-month business-as-usual contract launched March 2026 with 20 specialist teams, targeting 100 million liters a day; no result reported yet", "model_version": null, "outcome": "unknown", "source": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-liters-of-water-per-day/" }, { "date": "2026-05-20", "setting": "Origin Tech's 2026 claim across its customers: over 5,000 leaks identified, 73.45 ML/d (vendor statement, unverified)", "model_version": null, "outcome": "unknown", "source": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio" } ] }, "paired_incident": null, "headline": "Origin Orbit trial may have found over 800 Thames Water leaks", "metric": "over 800 leaks across the network, saving an estimated 8.7 million liters of water a day", "comparator": { "against": "a person detecting leaks unassisted on the ground (the vendor co-founder's claim)", "before": "a person working unassisted on the ground", "after": "at least five times quicker", "basis": "assumed", "quote": "Marsden said the technology is \"at least five times quicker\" at detecting \"invisible leaks that are really difficult to find\" than a person working unassisted on the ground.", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo" }, "later": { "status": "scaled", "date": "2026-08-24", "note": "After the trial Thames Water took Origin Orbit into a 13-month business-as-usual contract (launched March 2026, 20 teams) and by August 2026 described the satellite detection as part of its drought-response leak hunting, without reporting post-trial leak counts.", "quote": "Thames Water also uses an AI-driven leak detection solution that uses satellite data to identify leaks. The utility company partners with Origin Orbit to detect anomalies in the ground that indicate underground water leaks.", "url": "https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo; https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies; https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://find-and-update.company-information.service.gov.uk/company/12359793; https://en.wikipedia.org/wiki/Thames_Water", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, two press releases from the makers and three press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0340", "catalog_version": "1.0.0", "source_fingerprint": "7d1b2c1ddb627a3cb73bdc8b7e842b73fac11086faf02670ed739acb1100f910", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0340-source-0001", "AIBD-0340-source-0002", "AIBD-0340-source-0003", "AIBD-0340-source-0004", "AIBD-0340-source-0005", "AIBD-0340-source-0006", "AIBD-0340-source-0007", "AIBD-0340-source-0008", "AIBD-0340-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Origin Tech Ltd (North East England)", "deployer": "Thames Water (leakage operations; 20 Origin specialist teams under the 2026 contract)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Only the vendor and the utility describe the AI role. The BBC caption says the AI-generated satellite technology detects moisture and movement in the ground, and Origin Tech describes AI-driven satellite insights. The system outputs suspected-leak points that Thames Water crews then locate and repair with conventional methods. The 800-leak and 8.7 million liters a day figures are Thames Water's estimates relayed by the BBC. They are reproduced in the utility's press release (Water Magazine) and trade press. Origin Tech's site adds vendor claims. No independent verification is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Only the vendor and the utility describe the AI role. The BBC caption says the AI-generated satellite technology detects moisture and movement in the ground, and Origin Tech describes AI-driven satellite insights. The system outputs suspected-leak points that Thames Water crews then locate and repair with conventional methods. The 800-leak and 8.7 million liters a day figures are Thames Water's estimates relayed by the BBC. They are reproduced in the utility's press release (Water Magazine) and trade press. Origin Tech's site adds vendor claims. No independent verification is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual customer outcome is documented. The BBC notes most of these leaks are never seen by customers." }, { "group": "organizational", "depth": 2, "reason": "Material to the utility's leakage operations by its own account. Over 800 leaks were found and an estimated 8.7 million liters a day saved in one trial. A 13-month business-as-usual contract followed. The estimate is the utility's own and unverified." }, { "group": "societal", "depth": 1, "reason": "Water was conserved for the public supply. Per the Environment Agency figure cited by the BBC, about 19% of UK water is lost to leakage. The scale claimed is modest and unverified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports, two press releases from the makers and three press releases from the deployer. 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The estimate is the utility's own and unverified." }, { "group": "societal", "depth": 1, "reason": "Water was conserved for the public supply. Per the Environment Agency figure cited by the BBC, about 19% of UK water is lost to leakage. The scale claimed is modest and unverified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual customer outcome is documented. 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"review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "over 800 leaks across the network, saving an estimated 8.7 million liters of water a day", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-08-24", "note": "After the trial Thames Water took Origin Orbit into a 13-month business-as-usual contract (launched March 2026, 20 teams) and by August 2026 described the satellite detection as part of its drought-response leak hunting, without reporting post-trial leak counts.", "quote": "Thames Water also uses an AI-driven leak detection solution that uses satellite data to identify leaks. The utility company partners with Origin Orbit to detect anomalies in the ground that indicate underground water leaks.", "url": "https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0340" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": "Water lost to leaks is read under Trustible's “water consumption”; the saving is the utility's figure." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Thames Water trialed Origin Orbit, the satellite leak-detection product of North East England start-up Origin Tech Ltd (founded 2019). The company and the utility describe it as using AI on satellite data to flag suspected underground leaks within a 30-meter search radius. Origin's co-founder John Marsden told the BBC the satellites pass over Swindon roughly every six days. He said they produce points on a map for Thames Water crews. Thames Water's statements say an 18-week trial found over 800 leaks across the network. They were reported by the BBC (20 May 2026) and reproduced in trade press. It saved an estimated 8.7 million liters of water a day, with several leaks running at more than 10 liters per second. On the strength of the trial, Thames Water signed a 13-month contract, launched in March 2026 per the utility's release. It deploys 20 specialist teams expected to find about 25 leaks a week each, and aims to cut leakage by 100 million liters a day. That makes it the first water company to integrate Origin Orbit into business-as-usual leakage operations. Origin Tech's own site adds a 2026 claim that Origin Orbit identified over 5,000 leaks (73.45 ML/d) across its customers. All figures originate with the utility and the vendor, and no regulator or independent measurement is among the sources. The trial dates are not given. Smart Water Magazine reported the result on 5 May 2026. Cambridgeshire County Council's AI inspection contract (April 2026) and Hertfordshire's ARRES robot (January 2024) have no outcomes on record. They are not among the sources.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual customer outcome is documented. The BBC notes most of these leaks are never seen by customers." }, { "group": "organizational", "depth": 2, "reason": "Material to the utility's leakage operations by its own account. Over 800 leaks were found and an estimated 8.7 million liters a day saved in one trial. A 13-month business-as-usual contract followed. The estimate is the utility's own and unverified." }, { "group": "societal", "depth": 1, "reason": "Water was conserved for the public supply. Per the Environment Agency figure cited by the BBC, about 19% of UK water is lost to leakage. The scale claimed is modest and unverified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to pinpoint non-visible underground leaks from satellite data so that crews can find and fix them. The leaks found in the trial are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Thames Water supply area, England (BBC report centered on Swindon, Wiltshire)", "developer_country": "United Kingdom", "deployer_country": "United Kingdom" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to pinpoint non-visible underground leaks from satellite data so that crews can find and fix them. The leaks found in the trial are that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-05-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo; https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies; https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://find-and-update.company-information.service.gov.uk/company/12359793; https://en.wikipedia.org/wiki/Thames_Water", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite passes about every six days over the network, processed into suspected-leak points within a 30-meter radius", "An 18-week trial in which Thames Water crews investigated the points with conventional ground methods", "A network with a large share of non-visible underground leaks (92% of Origin Orbit finds are non-visible, per the vendor)" ], "must_hold": [ "Satellite revisit frequency and ground conditions that allow moisture and movement to be detected", "Crew capacity to investigate points (20 teams at about 25 leaks a week each under the contract)", "Repair follow-through so that found leaks become saved water", "The utility's method for estimating liters saved per leak, which no independent source has checked" ], "echoes": [ { "date": "2026-05-29", "setting": "13-month business-as-usual contract launched March 2026 with 20 specialist teams, targeting 100 million liters a day; no result reported yet", "outcome": "unknown", "source": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-liters-of-water-per-day/" }, { "date": "2026-05-20", "setting": "Origin Tech's 2026 claim across its customers: over 5,000 leaks identified, 73.45 ML/d (vendor statement, unverified)", "outcome": "unknown", "source": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite passes about every six days over the network, processed into suspected-leak points within a 30-meter radius", "An 18-week trial in which Thames Water crews investigated the points with conventional ground methods", "A network with a large share of non-visible underground leaks (92% of Origin Orbit finds are non-visible, per the vendor)" ], "must_hold": [ "Satellite revisit frequency and ground conditions that allow moisture and movement to be detected", "Crew capacity to investigate points (20 teams at about 25 leaks a week each under the contract)", "Repair follow-through so that found leaks become saved water", "The utility's method for estimating liters saved per leak, which no independent source has checked" ], "echoes": [ { "date": "2026-05-29", "setting": "13-month business-as-usual contract launched March 2026 with 20 specialist teams, targeting 100 million liters a day; no result reported yet", "outcome": "unknown", "source": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-liters-of-water-per-day/" }, { "date": "2026-05-20", "setting": "Origin Tech's 2026 claim across its customers: over 5,000 leaks identified, 73.45 ML/d (vendor statement, unverified)", "outcome": "unknown", "source": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo; https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies; https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://find-and-update.company-information.service.gov.uk/company/12359793; https://en.wikipedia.org/wiki/Thames_Water", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Water utilities: leakage detection", "segment": "Private regulated water company" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026.", "title": "Thames Water using AI technology to find leaks in Swindon", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "During a previous trial, 800 leaks across Thames Water's network were found." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (savings estimate attributed to the company).", "title": "Thames Water using AI technology to find leaks in Swindon (savings estimate)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The company said this helped to save an estimated 8.7 million litres of water per day, enough to fill more than three Olympic-sized swimming pools." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (image caption describing the technology).", "title": "Thames Water using AI technology to find leaks in Swindon (technology description)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The AI-generated satellite technology detects moisture and movement in the ground" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Press release reproduced by Water Magazine, 29 May 2026 (source credited as Thames Water).", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day/", "date": "2026-05-29", "written_by": "deployer", "quote": "During a previous 18-week trial, Origin Orbit® was able to find over 800 leaks across Thames Water's network, helping to save an estimated 8.7 million litres of water per day." }, { "kind": "news report", "citation": "Tempest, O. (2026). Thames Water signs AI satellite deal to plug 100 million litres of daily leaks. Smart Water Magazine, 5 May 2026 (no verbatim quote taken).", "title": "Thames Water signs AI satellite deal to plug 100 million litres of daily leaks", "url": "https://smartwatermagazine.com/news/thames-water/thames-water-signs-ai-satellite-deal-plug-100-million-litres-daily-leaks", "date": "2026-05-05", "written_by": "independent" }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech in Partnership with Thames Water. Origin news, 29 May 2026.", "title": "Origin Tech in Partnership with Thames Water", "url": "https://www.origintech.co.uk/origin-news/origin-tech-in-partnership-with-thames-water", "date": "2026-05-29", "written_by": "makers", "quote": "By combining AI-driven satellite insights with operational expertise, Origin Tech is enabling faster, more precise detection of previously invisible leaks." }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech Featured in BBC News and BBC Wiltshire Radio. Origin news, 20 May 2026.", "title": "Origin Tech Featured in BBC News and BBC Wiltshire Radio", "url": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio", "date": "2026-05-20", "written_by": "makers", "quote": "Origin Orbit® has been able to identify over 5K leaks, which is over 73.45 ML/d of leakage" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Newsroom, 1 May 2026.", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "date": "2026-05-01", "written_by": "deployer" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South-East drought pressure intensifies. Newsroom, 24 August 2026.", "title": "Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South‑East drought pressure intensifies", "url": "https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies", "date": "2026-08-24", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0340-source-0001", "AIBD-0340-source-0002", "AIBD-0340-source-0003", "AIBD-0340-source-0004", "AIBD-0340-source-0005", "AIBD-0340-source-0006", "AIBD-0340-source-0007", "AIBD-0340-source-0008", "AIBD-0340-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026.", "title": "Thames Water using AI technology to find leaks in Swindon", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "During a previous trial, 800 leaks across Thames Water's network were found." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (savings estimate attributed to the company).", "title": "Thames Water using AI technology to find leaks in Swindon (savings estimate)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The company said this helped to save an estimated 8.7 million litres of water per day, enough to fill more than three Olympic-sized swimming pools." }, { "kind": "news report", "citation": "Rattley, H. and Jewers, N. (2026). Thames Water using AI technology to find leaks in Swindon. BBC News, 20 May 2026 (image caption describing the technology).", "title": "Thames Water using AI technology to find leaks in Swindon (technology description)", "url": "https://www.bbc.co.uk/news/articles/cr5pzmyr5jeo", "date": "2026-05-20", "written_by": "independent", "quote": "The AI-generated satellite technology detects moisture and movement in the ground" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Press release reproduced by Water Magazine, 29 May 2026 (source credited as Thames Water).", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day/", "date": "2026-05-29", "written_by": "deployer", "quote": "During a previous 18-week trial, Origin Orbit® was able to find over 800 leaks across Thames Water's network, helping to save an estimated 8.7 million litres of water per day." }, { "kind": "news report", "citation": "Tempest, O. (2026). Thames Water signs AI satellite deal to plug 100 million litres of daily leaks. Smart Water Magazine, 5 May 2026 (no verbatim quote taken).", "title": "Thames Water signs AI satellite deal to plug 100 million litres of daily leaks", "url": "https://smartwatermagazine.com/news/thames-water/thames-water-signs-ai-satellite-deal-plug-100-million-litres-daily-leaks", "date": "2026-05-05", "written_by": "independent" }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech in Partnership with Thames Water. Origin news, 29 May 2026.", "title": "Origin Tech in Partnership with Thames Water", "url": "https://www.origintech.co.uk/origin-news/origin-tech-in-partnership-with-thames-water", "date": "2026-05-29", "written_by": "makers", "quote": "By combining AI-driven satellite insights with operational expertise, Origin Tech is enabling faster, more precise detection of previously invisible leaks." }, { "kind": "press release", "citation": "Origin Tech (2026). Origin Tech Featured in BBC News and BBC Wiltshire Radio. Origin news, 20 May 2026.", "title": "Origin Tech Featured in BBC News and BBC Wiltshire Radio", "url": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio", "date": "2026-05-20", "written_by": "makers", "quote": "Origin Orbit® has been able to identify over 5K leaks, which is over 73.45 ML/d of leakage" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day. Newsroom, 1 May 2026.", "title": "Thames Water signs landmark leak detection contract with Origin Tech to save over 100 million litres of water per day", "url": "https://www.thameswater.co.uk/news/2026/apr/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-litres-of-water-per-day", "date": "2026-05-01", "written_by": "deployer" }, { "kind": "press release", "citation": "Thames Water (2026). Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South-East drought pressure intensifies. Newsroom, 24 August 2026.", "title": "Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South‑East drought pressure intensifies", "url": "https://www.thameswater.co.uk/news/2026/aug/thames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies", "date": "2026-08-24", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "18-week Origin Orbit trial on Thames Water's network: over 800 leaks found, est. 8.7 million liters a day saved (utility figures); 13-month contract followed, target 100 million liters a day.", "account": "Thames Water trialed Origin Orbit, the satellite leak-detection product of North East England start-up Origin Tech Ltd (founded 2019). The company and the utility describe it as using AI on satellite data to flag suspected underground leaks within a 30-meter search radius. Origin's co-founder John Marsden told the BBC the satellites pass over Swindon roughly every six days. He said they produce points on a map for Thames Water crews. Thames Water's statements say an 18-week trial found over 800 leaks across the network. They were reported by the BBC (20 May 2026) and reproduced in trade press. It saved an estimated 8.7 million liters of water a day, with several leaks running at more than 10 liters per second. On the strength of the trial, Thames Water signed a 13-month contract, launched in March 2026 per the utility's release. It deploys 20 specialist teams expected to find about 25 leaks a week each, and aims to cut leakage by 100 million liters a day. That makes it the first water company to integrate Origin Orbit into business-as-usual leakage operations. Origin Tech's own site adds a 2026 claim that Origin Orbit identified over 5,000 leaks (73.45 ML/d) across its customers. All figures originate with the utility and the vendor, and no regulator or independent measurement is among the sources. The trial dates are not given. Smart Water Magazine reported the result on 5 May 2026. Cambridgeshire County Council's AI inspection contract (April 2026) and Hertfordshire's ARRES robot (January 2024) have no outcomes on record. They are not among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The software analyzed satellite data to identify possible water leaks." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The deployment used Origin Orbit." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Origin Orbit®" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Origin Orbit (Origin Tech satellite leak detection applying AI to satellite data to flag suspected leaks within a 30-meter search radius)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Origin Tech Ltd (North East England)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Origin Tech's AI satellite leak detection may have found over 800 leaks in an 18-week Thames Water trial, saving an estimated 8.7 million liters a day", "headline": "Origin Orbit trial may have found over 800 Thames Water leaks" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7d1b2c1ddb627a3cb73bdc8b7e842b73fac11086faf02670ed739acb1100f910" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Thames Water trialed Origin Orbit, the satellite leak-detection product of North East England start-up Origin Tech Ltd (founded 2019). The company and the utility describe it as using AI on satellite data to flag suspected underground leaks within a 30-meter search radius. Origin's co-founder John Marsden told the BBC the satellites pass over Swindon roughly every six days. He said they produce points on a map for Thames Water crews. Thames Water's statements say an 18-week trial found over 800 leaks across the network. They were reported by the BBC (20 May 2026) and reproduced in trade press. It saved an estimated 8.7 million liters of water a day, with several leaks running at more than 10 liters per second. On the strength of the trial, Thames Water signed a 13-month contract, launched in March 2026 per the utility's release. It deploys 20 specialist teams expected to find about 25 leaks a week each, and aims to cut leakage by 100 million liters a day. That makes it the first water company to integrate Origin Orbit into business-as-usual leakage operations. Origin Tech's own site adds a 2026 claim that Origin Orbit identified over 5,000 leaks (73.45 ML/d) across its customers. All figures originate with the utility and the vendor, and no regulator or independent measurement is among the sources. The trial dates are not given. Smart Water Magazine reported the result on 5 May 2026. Cambridgeshire County Council's AI inspection contract (April 2026) and Hertfordshire's ARRES robot (January 2024) have no outcomes on record. They are not among the sources.", "replication": { "produced_by": [ "Satellite passes about every six days over the network, processed into suspected-leak points within a 30-meter radius", "An 18-week trial in which Thames Water crews investigated the points with conventional ground methods", "A network with a large share of non-visible underground leaks (92% of Origin Orbit finds are non-visible, per the vendor)" ], "must_hold": [ "Satellite revisit frequency and ground conditions that allow moisture and movement to be detected", "Crew capacity to investigate points (20 teams at about 25 leaks a week each under the contract)", "Repair follow-through so that found leaks become saved water", "The utility's method for estimating liters saved per leak, which no independent source has checked" ], "echoes": [ { "date": "2026-05-29", "setting": "13-month business-as-usual contract launched March 2026 with 20 specialist teams, targeting 100 million liters a day; no result reported yet", "outcome": "unknown", "source": "https://watermagazine.co.uk/2026/05/29/thames-water-signs-landmark-leak-detection-contract-with-origin-tech-to-save-over-100-million-liters-of-water-per-day/" }, { "date": "2026-05-20", "setting": "Origin Tech's 2026 claim across its customers: over 5,000 leaks identified, 73.45 ML/d (vendor statement, unverified)", "outcome": "unknown", "source": "https://www.origintech.co.uk/origin-news/origin-tech-featured-in-bbc-news-and-bbc-wiltshire-radio" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0344", "case_url": "records/AIBD-0344.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." }, { "label": "AI contribution", "value": "The trade press credits the mitigation to MARS alerts. Only UNEP's general page describes MARS as satellite-based with advanced AI models, so the AI contribution to this detection is not isolated. A single news item relaying the system operator documents the case. The named field operator declined to confirm closure of the specific source." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0344", "title": "After UNEP MARS alerts, operators divert gas from an Iraqi flare that had released about 1.7m tCO2e since 2023; the source may be in the Zubair field near Basra", "summary": "The Chemical Engineer (5 May 2026), relaying UNEP: the largest source mitigated in 2026 was an Iraqi flare, 1.7m tCO2e since 2023, apparently in the Zubair field; Eni did not confirm closure.", "date_observed": "2026-05-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Chemical Engineer reported on 5 May 2026, relaying UNEP's Methane Alert and Response System (MARS) figures. MARS had curbed 41 methane super-emitters since launching in 2023. Six significant sources had stopped emitting in 2026 following MARS alerts, including three in Turkmenistan and one each in Libya, Iraq and Kazakhstan. The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility. The flare had released an estimated 1.7m tCO2e since 2023. The source appears to have been at a facility in the Zubair oil and gas field near Basra. The article reports that the operator Eni acknowledged running a leak detection and repair campaign at Zubair. Eni did not confirm closure of the specific source. The article says MARS uses data from 35 satellite instruments to identify large plumes and alert operators. UNEP's MARS page adds that IMEO couples satellite data with scientific expertise and advanced AI models. No date for the mitigation is given beyond 2026. UNEP's press releases were not available, and no UNEP case document for this source is on record.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); facility operators in Iraq (gas diversion); the article names Eni as operator of the Zubair field", "location": "Zubair oil and gas field near Basra, Iraq (location reported as apparent, not confirmed)", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas production; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 1, "reason": "Operators are reported to have diverted gas to a newer facility. The operator did not confirm the specific closure, and no operational detail or cost is documented." }, { "group": "societal", "depth": 2, "reason": "Reported as the largest source mitigated in 2026, after an estimated 1.7m tCO2e released since 2023. It is still one case among about 41 confirmed, and unconfirmed by the operator." } ], "ai_role": { "status": "assessed", "statement": "The trade press credits the mitigation to MARS alerts. Only UNEP's general page describes MARS as satellite-based with advanced AI models, so the AI contribution to this detection is not isolated. A single news item relaying the system operator documents the case. The named field operator declined to confirm closure of the specific source." }, "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emissions estimate.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Iraq flare emissions)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The flare had released an estimated 1.7m tCO2e since 2023." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, location and operator response (Eni acknowledged a campaign at Zubair but did not confirm closure of the specific source).", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Zubair)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The source appears to have been at a facility in the Zubair oil and gas field near Basra." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ], "replication": { "produced_by": [ "Satellite detection of a persistent flare plume emitting since 2023", "MARS alerts to operators with a newer facility able to take the diverted gas", "UNEP confirmation through later satellite observation (as described for MARS cases generally)" ], "must_hold": [ "Operator capacity and willingness to re-route gas rather than flare it", "Satellite coverage to confirm cessation", "Operator or government confirmation, absent here (Eni did not confirm the specific closure)" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection (trade press relaying UNEP)", "model_version": null, "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091)", "model_version": null, "outcome": "recurred", "source": "AIBD-0091" } ] }, "paired_incident": null, "headline": "UNEP MARS alerts lead operators to divert gas from Iraqi flare", "metric": "the flare had released an estimated 1.7m tCO2e since 2023", "comparator": { "against": "the flare before gas was diverted", "before": "an estimated 1.7m tCO2e since 2023", "after": "operators diverted gas production to a newer facility", "basis": "measured", "quote": "The largest source mitigated this year was a flare in Iraq, where operators diverted gas production to a newer facility. The flare had released an estimated 1.7m tCO2e since 2023.", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/" }, "later": { "status": "unknown", "date": null, "note": "Eni did not confirm closing the specific source and nothing dated after the 5 May 2026 article was found in the cited sources; the September 2026 UNEP snapshot lists no Iraqi source among its top 50.", "quote": null, "url": null }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0344", "catalog_version": "1.0.0", "source_fingerprint": "5f1b2e0372d74c8235c8410853ea5a5f5eca7ae7023e04fed53a297c8d67ad9b", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0344-source-0001", "AIBD-0344-source-0002", "AIBD-0344-source-0003", "AIBD-0344-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); facility operators in Iraq (gas diversion); the article names Eni as operator of the Zubair field" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The trade press credits the mitigation to MARS alerts. 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were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0344" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 5 May 2026, relaying UNEP's Methane Alert and Response System (MARS) figures. MARS had curbed 41 methane super-emitters since launching in 2023. Six significant sources had stopped emitting in 2026 following MARS alerts, including three in Turkmenistan and one each in Libya, Iraq and Kazakhstan. The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility. The flare had released an estimated 1.7m tCO2e since 2023. The source appears to have been at a facility in the Zubair oil and gas field near Basra. The article reports that the operator Eni acknowledged running a leak detection and repair campaign at Zubair. Eni did not confirm closure of the specific source. The article says MARS uses data from 35 satellite instruments to identify large plumes and alert operators. UNEP's MARS page adds that IMEO couples satellite data with scientific expertise and advanced AI models. No date for the mitigation is given beyond 2026. UNEP's press releases were not available, and no UNEP case document for this source is on record.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 1, "reason": "Operators are reported to have diverted gas to a newer facility. The operator did not confirm the specific closure, and no operational detail or cost is documented." }, { "group": "societal", "depth": 2, "reason": "Reported as the largest source mitigated in 2026, after an estimated 1.7m tCO2e released since 2023. It is still one case among about 41 confirmed, and unconfirmed by the operator." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Zubair oil and gas field near Basra, Iraq (location reported as apparent, not confirmed)", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-05-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite detection of a persistent flare plume emitting since 2023", "MARS alerts to operators with a newer facility able to take the diverted gas", "UNEP confirmation through later satellite observation (as described for MARS cases generally)" ], "must_hold": [ "Operator capacity and willingness to re-route gas rather than flare it", "Satellite coverage to confirm cessation", "Operator or government confirmation, absent here (Eni did not confirm the specific closure)" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection (trade press relaying UNEP)", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite detection of a persistent flare plume emitting since 2023", "MARS alerts to operators with a newer facility able to take the diverted gas", "UNEP confirmation through later satellite observation (as described for MARS cases generally)" ], "must_hold": [ "Operator capacity and willingness to re-route gas rather than flare it", "Satellite coverage to confirm cessation", "Operator or government confirmation, absent here (Eni did not confirm the specific closure)" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection (trade press relaying UNEP)", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas production; intergovernmental observatory" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emissions estimate.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Iraq flare emissions)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The flare had released an estimated 1.7m tCO2e since 2023." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, location and operator response (Eni acknowledged a campaign at Zubair but did not confirm closure of the specific source).", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Zubair)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The source appears to have been at a facility in the Zubair oil and gas field near Basra." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0344-source-0001", "AIBD-0344-source-0002", "AIBD-0344-source-0003", "AIBD-0344-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emissions estimate.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Iraq flare emissions)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The flare had released an estimated 1.7m tCO2e since 2023." }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, location and operator response (Eni acknowledged a campaign at Zubair but did not confirm closure of the specific source).", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' (Zubair)", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "The source appears to have been at a facility in the Zubair oil and gas field near Basra." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The Chemical Engineer (5 May 2026), relaying UNEP: the largest source mitigated in 2026 was an Iraqi flare, 1.7m tCO2e since 2023, apparently in the Zubair field; Eni did not confirm closure.", "account": "The Chemical Engineer reported on 5 May 2026, relaying UNEP's Methane Alert and Response System (MARS) figures. MARS had curbed 41 methane super-emitters since launching in 2023. Six significant sources had stopped emitting in 2026 following MARS alerts, including three in Turkmenistan and one each in Libya, Iraq and Kazakhstan. The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility. The flare had released an estimated 1.7m tCO2e since 2023. The source appears to have been at a facility in the Zubair oil and gas field near Basra. The article reports that the operator Eni acknowledged running a leak detection and repair campaign at Zubair. Eni did not confirm closure of the specific source. The article says MARS uses data from 35 satellite instruments to identify large plumes and alert operators. UNEP's MARS page adds that IMEO couples satellite data with scientific expertise and advanced AI models. No date for the mitigation is given beyond 2026. UNEP's press releases were not available, and no UNEP case document for this source is on record." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "After UNEP MARS alerts, operators divert gas from an Iraqi flare that had released about 1.7m tCO2e since 2023; the source may be in the Zubair field near Basra", "headline": "UNEP MARS alerts lead operators to divert gas from Iraqi flare" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5f1b2e0372d74c8235c8410853ea5a5f5eca7ae7023e04fed53a297c8d67ad9b" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 5 May 2026, relaying UNEP's Methane Alert and Response System (MARS) figures. MARS had curbed 41 methane super-emitters since launching in 2023. Six significant sources had stopped emitting in 2026 following MARS alerts, including three in Turkmenistan and one each in Libya, Iraq and Kazakhstan. The largest source mitigated this year was a flare in Iraq, where operators diverted gas to a newer facility. The flare had released an estimated 1.7m tCO2e since 2023. The source appears to have been at a facility in the Zubair oil and gas field near Basra. The article reports that the operator Eni acknowledged running a leak detection and repair campaign at Zubair. Eni did not confirm closure of the specific source. The article says MARS uses data from 35 satellite instruments to identify large plumes and alert operators. UNEP's MARS page adds that IMEO couples satellite data with scientific expertise and advanced AI models. No date for the mitigation is given beyond 2026. UNEP's press releases were not available, and no UNEP case document for this source is on record.", "replication": { "produced_by": [ "Satellite detection of a persistent flare plume emitting since 2023", "MARS alerts to operators with a newer facility able to take the diverted gas", "UNEP confirmation through later satellite observation (as described for MARS cases generally)" ], "must_hold": [ "Operator capacity and willingness to re-route gas rather than flare it", "Satellite coverage to confirm cessation", "Operator or government confirmation, absent here (Eni did not confirm the specific closure)" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection (trade press relaying UNEP)", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0226", "case_url": "records/AIBD-0226.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Detect visible firearms in existing security-camera feeds, verify with human analysts and alert police within seconds to reduce response times to potential shootings." }, { "label": "AI contribution", "value": "Court documents (as reported) attribute the police response to a ZeroEyes camera notification. Human analysts verify detections before alerts, so the AI's contribution is the initial detection. Both sources are local news citing arrest papers. Neither quantifies response time or states whether anyone else had called police." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0226", "title": "ZeroEyes camera alert sends FSU police to a man pointing a stolen Glock at bystanders; arrested on four charges", "summary": "At about 2:30 a.m. on 2 May 2026 ZeroEyes flagged a man pointing a gun in FSU's St. Augustine garage; officers found a stolen Glock 19 on Tyler Farmer and charged him, per court documents.", "date_observed": "2026-05-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "According to arrest papers cited by WCTV, Florida State University police responded to the St. Augustine parking garage at about 2:30 a.m. on Saturday 2 May 2026. ZeroEyes gun detection technology had detected an individual in black pointing a gun on the bottom level. Camera footage showed Tyler Farmer, 19, brandishing a loaded firearm at four other people. Officers found a Glock 19 handgun, stolen from Miller County, Georgia, in his waistband. He was charged with possessing a firearm on school property, unlawful carry, improper display of a firearm and grand theft of a firearm. He bonded out on Sunday. The Tallahassee Democrat, citing court records, says police responded to a “ZeroEyes camera notification” and that Farmer is not an FSU student. ZeroEyes layers over existing cameras and routes human-verified detections to police.", "category": "protective response", "unexpected": false, "purpose": "Detect visible firearms in existing security-camera feeds, verify with human analysts and alert police within seconds to reduce response times to potential shootings.", "system": "ZeroEyes AI gun detection", "model_version": null, "developer": "ZeroEyes", "deployer": "Florida State University Police Department", "location": "Tallahassee, United States", "domain": "Public safety / weapons detection", "segment": "Education", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "An armed man waving a loaded, stolen handgun at bystanders was arrested and the weapon recovered, per court documents reported by WCTV." }, { "group": "personal", "depth": null, "reason": "The four bystanders are not identified and no source documents what the arrest prevented for them." } ], "ai_role": { "status": "assessed", "statement": "Court documents (as reported) attribute the police response to a ZeroEyes camera notification. Human analysts verify detections before alerts, so the AI's contribution is the initial detection. Both sources are local news citing arrest papers. 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https://police.fsu.edu/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public safety / weapons detection", "segment": "Education" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Caroline Christensen, WCTV, 4 May 2026 (citing court documents)", "title": "Court documents: 19-year-old arrested after brandishing gun in FSU parking garage early Saturday morning", "url": "https://www.wctv.tv/2026/05/04/court-documents-19-year-old-arrested-after-brandishing-weapon-fsu-parking-garage-early-saturday-morning/", "date": "2026-05-04", "written_by": "independent", "quote": "Tyler Farmer is charged with possessing a firearm on school property, unlawful carry, improper display of a firearm and grand theft of a firearm, court documents say." }, { "kind": "news report", "citation": "Arianna Otero, Tallahassee Democrat, 5 May 2026 (citing court records)", "title": "New AI gun detection tool leads to arrest at FSU parking garage", "url": "https://www.tallahassee.com/story/news/local/2026/05/05/ai-tool-identifying-firearm-leads-to-arrest-of-fsu-student/89946929007/", "date": "2026-05-05", "written_by": "independent", "quote": "led to the arrest of a teen after he was caught brandishing a gun at an Florida State University campus parking garage, police said." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0226-source-0001", "AIBD-0226-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Caroline Christensen, WCTV, 4 May 2026 (citing court documents)", "title": "Court documents: 19-year-old arrested after brandishing gun in FSU parking garage early Saturday morning", "url": "https://www.wctv.tv/2026/05/04/court-documents-19-year-old-arrested-after-brandishing-weapon-fsu-parking-garage-early-saturday-morning/", "date": "2026-05-04", "written_by": "independent", "quote": "Tyler Farmer is charged with possessing a firearm on school property, unlawful carry, improper display of a firearm and grand theft of a firearm, court documents say." }, { "kind": "news report", "citation": "Arianna Otero, Tallahassee Democrat, 5 May 2026 (citing court records)", "title": "New AI gun detection tool leads to arrest at FSU parking garage", "url": "https://www.tallahassee.com/story/news/local/2026/05/05/ai-tool-identifying-firearm-leads-to-arrest-of-fsu-student/89946929007/", "date": "2026-05-05", "written_by": "independent", "quote": "led to the arrest of a teen after he was caught brandishing a gun at an Florida State University campus parking garage, police said." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "At about 2:30 a.m. on 2 May 2026 ZeroEyes flagged a man pointing a gun in FSU's St. Augustine garage; officers found a stolen Glock 19 on Tyler Farmer and charged him, per court documents.", "account": "According to arrest papers cited by WCTV, Florida State University police responded to the St. Augustine parking garage at about 2:30 a.m. on Saturday 2 May 2026. ZeroEyes gun detection technology had detected an individual in black pointing a gun on the bottom level. Camera footage showed Tyler Farmer, 19, brandishing a loaded firearm at four other people. Officers found a Glock 19 handgun, stolen from Miller County, Georgia, in his waistband. He was charged with possessing a firearm on school property, unlawful carry, improper display of a firearm and grand theft of a firearm. He bonded out on Sunday. The Tallahassee Democrat, citing court records, says police responded to a “ZeroEyes camera notification” and that Farmer is not an FSU student. ZeroEyes layers over existing cameras and routes human-verified detections to police." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ZeroEyes AI gun detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "ZeroEyes" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ZeroEyes camera alert sends FSU police to a man pointing a stolen Glock at bystanders; arrested on four charges", "headline": "ZeroEyes alert sends FSU police to a man pointing a stolen Glock" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7fad5474a387556d45b9263837e3e6d278b86523681e910cc04197cbf55cbb7d" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "According to arrest papers cited by WCTV, Florida State University police responded to the St. Augustine parking garage at about 2:30 a.m. on Saturday 2 May 2026. ZeroEyes gun detection technology had detected an individual in black pointing a gun on the bottom level. Camera footage showed Tyler Farmer, 19, brandishing a loaded firearm at four other people. Officers found a Glock 19 handgun, stolen from Miller County, Georgia, in his waistband. He was charged with possessing a firearm on school property, unlawful carry, improper display of a firearm and grand theft of a firearm. He bonded out on Sunday. The Tallahassee Democrat, citing court records, says police responded to a “ZeroEyes camera notification” and that Farmer is not an FSU student. ZeroEyes layers over existing cameras and routes human-verified detections to police.", "replication": { "produced_by": [ "A firearm visibly brandished within view of a connected security camera", "Human verification at the vendor's operations center before dispatch", "Campus police able to reach the location while the person was still present" ], "must_hold": [ "Camera coverage and image quality sufficient for the detector", "The weapon must be visible (concealed guns are not detected)", "A staffed verification center and a responsive police unit" ], "echoes": [ { "date": "2023-11-30", "setting": "Hobbs, New Mexico: parking lot of Del Norte Pool, suspects detained and one charged", "outcome": "recurred", "source": "https://www.govtech.com/public-safety/ai-gun-detection-system-leads-to-charges-in-new-mexico" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0205", "case_url": "records/AIBD-0205.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "The monitors and the anomaly model were installed to spot blockages and leaks forming in sewers. Crews can then clear them before they cause pollution or flooding. This case was that intended use." }, { "label": "AI contribution", "value": "The level monitors detected an abnormal flow at Rowner. The machine-learning model raised the alert to the control room; by the company's account it distinguishes a forming blockage from daily rushes and rainfall. The jetting crew found and removed the fatberg. The articles are built on a Southern Water statement and a single quoted manager. They do not name the developer of the model and give no sensor data. No independent party confirmed that an overflow was imminent." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0205", "title": "Southern Water's sewer-monitor anomaly model flagged a forming fatberg in Rowner, Gosport, and a crew cleared it before any overflow into the River Alver", "summary": "Southern Water's AI sewer monitors flagged a forming fatberg in Rowner, Gosport; a crew cleared it before any overflow into the River Alver.", "date_observed": "2026-04-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Southern Water runs around 34,000 sewer level monitors with machine learning. Its proactive operations control manager Daniel McElhinney says the system learns the normal behavior of sewers. He says it can tell a forming blockage from daily rushes or rain. Shortly before 22 April 2026 the sensors detected an abnormal flow in Rowner, Gosport, and alerted the control room. A crew jetted the sewer and found a fatberg of wet wipes and other unflushable items. The company said that, left unchecked, the sewer could have overflowed from a manhole into a surface water drain leading to the River Alver. The case was reported on 22 April 2026. A second flagged blockage at Warlands Lane, Shalfleet, Isle of Wight, was reported on 23 April.", "category": "protective response", "unexpected": false, "purpose": "The monitors and the anomaly model were installed to spot blockages and leaks forming in sewers. Crews can then clear them before they cause pollution or flooding. This case was that intended use.", "system": "Southern Water's AI-enabled digital sewer system: a network of around 34,000 sewer level monitors in blockage hotspots with machine learning that learns normal sewer behavior and flags a forming blockage", "model_version": null, "developer": null, "deployer": "Southern Water", "location": "Gosport, United Kingdom", "domain": "Wastewater networks / pollution prevention", "segment": "Regulated water and wastewater utility", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "A sewage overflow averted is pollution prevented, which Trustible's description does not name.", "registry_ids": { "oecd_aim": "2026-04-22-7047" }, "ratings": [ { "group": "societal", "depth": 2, "reason": "The company says a sewage overflow into the River Alver was averted. The Andover Advertiser says the river is home to herons, water voles and kingfishers. The counterfactual is asserted rather than shown, and no pollution occurred. This is a specific avoided harm of unmeasured size." }, { "group": "organizational", "depth": 1, "reason": "Southern Water cleared one blockage before it became a pollution incident or a customer flooding. The company says blocked sewers are the leading cause of its pollution incidents. This is one of the more than 4,000 interventions it reports in a year." } ], "ai_role": { "status": "assessed", "statement": "The level monitors detected an abnormal flow at Rowner. The machine-learning model raised the alert to the control room; by the company's account it distinguishes a forming blockage from daily rushes and rainfall. The jetting crew found and removed the fatberg. The articles are built on a Southern Water statement and a single quoted manager. They do not name the developer of the model and give no sensor data. No independent party confirmed that an overflow was imminent." }, "evidence": [ { "kind": "news report", "citation": "Southern Daily Echo (2026). Fatberg found in Gosport removed before causing damage. dailyecho.co.uk, 23 April 2026; reproduces a Southern Water statement.", "title": "Fatberg found in Gosport removed before causing damage", "url": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/", "date": "2026-04-23", "written_by": "independent", "quote": "Southern Water prevented sewage pollution of the River Alver in Gosport with the help of its AI-enabled digital sewer system and rapid action from its teams." }, { "kind": "news report", "citation": "Andover Advertiser (2026). Southern Water using AI to prevent pollution incidents. andoveradvertiser.co.uk, 25 April 2026; reproduces a Southern Water statement.", "title": "Southern Water using AI to prevent pollution incidents", "url": "https://www.andoveradvertiser.co.uk/news/26046397.southern-water-using-ai-prevent-pollution-incidents/", "date": "2026-04-25", "written_by": "independent", "quote": "Southern Water's AI technology detected a fatberg forming in Rowner, Gosport, before it could pollute the River Alver last week." }, { "kind": "news report", "citation": "Island Echo (2026). AI sewer tech prevents fatberg from polluting chalk stream in Calbourne. islandecho.co.uk, 23 April 2026; the second flagged blockage, with the same Southern Water quote.", "title": "AI sewer tech prevents fatberg from polluting chalk stream in Calbourne", "url": "https://www.islandecho.co.uk/ai-sewer-tech-prevents-fatberg-from-polluting-chalk-stream-in-calbourne/", "date": "2026-04-23", "written_by": "independent", "quote": "The issue at Warlands Lane, Shalfleet was identified using Southern Water's network of around 34,000 sewer level monitors, which continuously track flows and detect unusual activity" } ], "replication": { "produced_by": [ "Level monitors in sewer blockage hotspots feeding a model that has learned the sewer's normal flow pattern and flags deviations from it", "A staffed control room that receives the alert and can dispatch a jetting crew quickly" ], "must_hold": [ "The flagged anomaly is a forming blockage rather than rain or a daily peak, which the company says the model distinguishes but for which no accuracy figures are given", "A crew reaches the site before the sewer surcharges; the articles do not say how long that took here", "The overflow route the company describes (manhole to surface water drain to river) exists as stated; it is the company's own account" ], "echoes": [] }, "paired_incident": null, "headline": "Southern Water's sewer monitors flag a fatberg before overflow", "metric": null, "comparator": { "against": "the previous reactive practice of attending blockages after a flooding or pollution event", "before": "turning up after the event to clean up and commiserate with devastated customers", "after": "spotting hundreds of potential blockages before it is too late", "basis": "assumed", "quote": "\"Instead of turning up after the event to clean up and commiserate with devastated customers we're spotting hundreds of potential blockages before it's too late.\"", "url": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/" }, "later": { "status": "persisted", "date": "2026-04-23", "note": "The next day the same monitor network flagged a second forming fatberg at Warlands Lane, Shalfleet, on the Isle of Wight, which crews cleared before it reached the Caul Bourne chalk stream.", "quote": "The issue at Warlands Lane, Shalfleet was identified using Southern Water’s network of around 34,000 sewer level monitors, which continuously track flows and detect unusual activity that may indicate a blockage or leak. As the fatberg began to form, sensors alerted the control room, prompting a swift response.", "url": "https://www.islandecho.co.uk/ai-sewer-tech-prevents-fatberg-from-polluting-chalk-stream-in-calbourne/" }, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-22-7047", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/; https://www.islandecho.co.uk/ai-sewer-tech-prevents-fatberg-from-polluting-chalk-stream-in-calbourne/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Southern_Water", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0205", "catalog_version": "1.0.0", "source_fingerprint": "d37052488798e332c79e45b266c0729c2f981e8be35ae9795e110e247cccd2d6", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0205-source-0001", "AIBD-0205-source-0002", "AIBD-0205-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Southern Water" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The level monitors detected an abnormal flow at Rowner. The machine-learning model raised the alert to the control room; by the company's account it distinguishes a forming blockage from daily rushes and rainfall. The jetting crew found and removed the fatberg. The articles are built on a Southern Water statement and a single quoted manager. They do not name the developer of the model and give no sensor data. No independent party confirmed that an overflow was imminent." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The level monitors detected an abnormal flow at Rowner. The machine-learning model raised the alert to the control room; by the company's account it distinguishes a forming blockage from daily rushes and rainfall. The jetting crew found and removed the fatberg. The articles are built on a Southern Water statement and a single quoted manager. They do not name the developer of the model and give no sensor data. No independent party confirmed that an overflow was imminent." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "The company says a sewage overflow into the River Alver was averted. The Andover Advertiser says the river is home to herons, water voles and kingfishers. The counterfactual is asserted rather than shown, and no pollution occurred. This is a specific avoided harm of unmeasured size." }, { "group": "organizational", "depth": 1, "reason": "Southern Water cleared one blockage before it became a pollution incident or a customer flooding. The company says blocked sewers are the leading cause of its pollution incidents. This is one of the more than 4,000 interventions it reports in a year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports. 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Its proactive operations control manager Daniel McElhinney says the system learns the normal behavior of sewers. He says it can tell a forming blockage from daily rushes or rain. Shortly before 22 April 2026 the sensors detected an abnormal flow in Rowner, Gosport, and alerted the control room. A crew jetted the sewer and found a fatberg of wet wipes and other unflushable items. The company said that, left unchecked, the sewer could have overflowed from a manhole into a surface water drain leading to the River Alver. The case was reported on 22 April 2026. 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Fatberg found in Gosport removed before causing damage. dailyecho.co.uk, 23 April 2026; reproduces a Southern Water statement.", "title": "Fatberg found in Gosport removed before causing damage", "url": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/", "date": "2026-04-23", "written_by": "independent", "quote": "Southern Water prevented sewage pollution of the River Alver in Gosport with the help of its AI-enabled digital sewer system and rapid action from its teams." }, { "kind": "news report", "citation": "Andover Advertiser (2026). Southern Water using AI to prevent pollution incidents. andoveradvertiser.co.uk, 25 April 2026; reproduces a Southern Water statement.", "title": "Southern Water using AI to prevent pollution incidents", "url": "https://www.andoveradvertiser.co.uk/news/26046397.southern-water-using-ai-prevent-pollution-incidents/", "date": "2026-04-25", "written_by": "independent", "quote": "Southern Water's AI technology detected a fatberg forming in Rowner, Gosport, before it could pollute the River Alver last week." }, { "kind": "news report", "citation": "Island Echo (2026). AI sewer tech prevents fatberg from polluting chalk stream in Calbourne. islandecho.co.uk, 23 April 2026; the second flagged blockage, with the same Southern Water quote.", "title": "AI sewer tech prevents fatberg from polluting chalk stream in Calbourne", "url": "https://www.islandecho.co.uk/ai-sewer-tech-prevents-fatberg-from-polluting-chalk-stream-in-calbourne/", "date": "2026-04-23", "written_by": "independent", "quote": "The issue at Warlands Lane, Shalfleet was identified using Southern Water's network of around 34,000 sewer level monitors, which continuously track flows and detect unusual activity" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0205-source-0001", "AIBD-0205-source-0002", "AIBD-0205-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Southern Daily Echo (2026). Fatberg found in Gosport removed before causing damage. dailyecho.co.uk, 23 April 2026; reproduces a Southern Water statement.", "title": "Fatberg found in Gosport removed before causing damage", "url": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/", "date": "2026-04-23", "written_by": "independent", "quote": "Southern Water prevented sewage pollution of the River Alver in Gosport with the help of its AI-enabled digital sewer system and rapid action from its teams." }, { "kind": "news report", "citation": "Andover Advertiser (2026). Southern Water using AI to prevent pollution incidents. andoveradvertiser.co.uk, 25 April 2026; reproduces a Southern Water statement.", "title": "Southern Water using AI to prevent pollution incidents", "url": "https://www.andoveradvertiser.co.uk/news/26046397.southern-water-using-ai-prevent-pollution-incidents/", "date": "2026-04-25", "written_by": "independent", "quote": "Southern Water's AI technology detected a fatberg forming in Rowner, Gosport, before it could pollute the River Alver last week." }, { "kind": "news report", "citation": "Island Echo (2026). AI sewer tech prevents fatberg from polluting chalk stream in Calbourne. islandecho.co.uk, 23 April 2026; the second flagged blockage, with the same Southern Water quote.", "title": "AI sewer tech prevents fatberg from polluting chalk stream in Calbourne", "url": "https://www.islandecho.co.uk/ai-sewer-tech-prevents-fatberg-from-polluting-chalk-stream-in-calbourne/", "date": "2026-04-23", "written_by": "independent", "quote": "The issue at Warlands Lane, Shalfleet was identified using Southern Water's network of around 34,000 sewer level monitors, which continuously track flows and detect unusual activity" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Southern Water's AI sewer monitors flagged a forming fatberg in Rowner, Gosport; a crew cleared it before any overflow into the River Alver.", "account": "Southern Water runs around 34,000 sewer level monitors with machine learning. Its proactive operations control manager Daniel McElhinney says the system learns the normal behavior of sewers. He says it can tell a forming blockage from daily rushes or rain. Shortly before 22 April 2026 the sensors detected an abnormal flow in Rowner, Gosport, and alerted the control room. A crew jetted the sewer and found a fatberg of wet wipes and other unflushable items. The company said that, left unchecked, the sewer could have overflowed from a manhole into a surface water drain leading to the River Alver. The case was reported on 22 April 2026. A second flagged blockage at Warlands Lane, Shalfleet, Isle of Wight, was reported on 23 April." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Southern Water's AI-enabled digital sewer system: a network of around 34,000 sewer level monitors in blockage hotspots with machine learning that learns normal sewer behavior and flags a forming blockage" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Southern Water's sewer-monitor anomaly model flagged a forming fatberg in Rowner, Gosport, and a crew cleared it before any overflow into the River Alver", "headline": "Southern Water's sewer monitors flag a fatberg before overflow" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d37052488798e332c79e45b266c0729c2f981e8be35ae9795e110e247cccd2d6" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Southern Water runs around 34,000 sewer level monitors with machine learning. Its proactive operations control manager Daniel McElhinney says the system learns the normal behavior of sewers. He says it can tell a forming blockage from daily rushes or rain. Shortly before 22 April 2026 the sensors detected an abnormal flow in Rowner, Gosport, and alerted the control room. A crew jetted the sewer and found a fatberg of wet wipes and other unflushable items. The company said that, left unchecked, the sewer could have overflowed from a manhole into a surface water drain leading to the River Alver. The case was reported on 22 April 2026. A second flagged blockage at Warlands Lane, Shalfleet, Isle of Wight, was reported on 23 April.", "replication": { "produced_by": [ "Level monitors in sewer blockage hotspots feeding a model that has learned the sewer's normal flow pattern and flags deviations from it", "A staffed control room that receives the alert and can dispatch a jetting crew quickly" ], "must_hold": [ "The flagged anomaly is a forming blockage rather than rain or a daily peak, which the company says the model distinguishes but for which no accuracy figures are given", "A crew reaches the site before the sewer surcharges; the articles do not say how long that took here", "The overflow route the company describes (manhole to surface water drain to river) exists as stated; it is the company's own account" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0255", "case_url": "records/AIBD-0255.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "AIVR is built to capture and analyze front-of-train video with machine learning. The aim is to find and fix track and lineside defects before they cause disruption." }, { "label": "AI contribution", "value": "AIVR's analysis raised the fault report that Network Rail engineers acted on. No human had reported it. Whether the minor fault would have escalated is LNER's claim. Detection rates and false-positive rates are not published." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0255", "title": "LNER's on-train AIVR camera AI flags a minor track fault near Retford a week after a driver-reported defect cost 10,000 delay minutes; overnight repair, zero delay minutes", "summary": "New Civil Engineer (6 Jul 2026) quoting LNER's 9 Apr 2026 announcement: after a January 2026 Cambridgeshire defect caused over 10,000 delay minutes, One Big Circle's AIVR identified a developing fa...", "date_observed": "2026-04-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "New Civil Engineer's Tom Pashby reported on 6 Jul 2026. His story was about the system flagging a snake as a track defect on 3 July. LNER runs the Pantograph Damage Assessment System (PANDAS) and One Big Circle's Automated Intelligent Video Review (AIVR) on a number of its trains. They assess overhead line equipment and track and report potential damage to Network Rail. The article reproduces LNER's 9 April 2026 announcement. In January 2026 a train driver in Cambridgeshire reported a track defect. It caused over 10,000 delay minutes, multiple cancellations and a full day of disruption. A week later the AIVR system identified a minor fault near Retford with the potential to become a larger issue. Engineers carried out an overnight repair with no delay to passengers and zero delay minutes. An LNER spokesperson said the information is fed back to Network Rail to target areas for attention. A Network Rail spokesperson said the snake would be raised automatically as standard practice. Network Rail's own 2022 story describes AIVR as a phone-sized front-of-train device applying machine learning to video of more than 20,000 miles of track. The exact day of the Retford detection is not given. LNER's announcement itself and later Southeastern and South Western Railway AIVR releases are not among the sources.", "category": "benefit created", "unexpected": false, "purpose": "AIVR is built to capture and analyze front-of-train video with machine learning. The aim is to find and fix track and lineside defects before they cause disruption.", "system": "Automated Intelligent Video Review (AIVR) front-of-train camera system with machine-learning analysis", "model_version": null, "developer": "One Big Circle", "deployer": "LNER (on-train cameras), with findings passed to Network Rail", "location": "Retford, United Kingdom", "domain": "Railway safety", "segment": "Track inspection / defect detection", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "An overnight planned repair with zero delay minutes. The comparison is a defect the same month that cost over 10,000 delay minutes and a day of disruption." }, { "group": "societal", "depth": 1, "reason": "Passengers were spared delay from this fault. The scale of avoided disruption is not quantified." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "AIVR's analysis raised the fault report that Network Rail engineers acted on. No human had reported it. Whether the minor fault would have escalated is LNER's claim. Detection rates and false-positive rates are not published." }, "evidence": [ { "kind": "news report", "citation": "Pashby T (6 Jul 2026). AI system on LNER train misidentifies snake as track defect. New Civil Engineer.", "title": "AI system on LNER train misidentifies snake as track defect", "url": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/", "date": "2026-07-06", "written_by": "independent", "quote": "a week later, the AIVR system identified a minor fault which had the potential to turn into a larger issue near Retford." }, { "kind": "press release", "citation": "Network Rail (31 Oct 2022). Future Rail – how machine learning is improving your journeys.", "title": "Future Rail – how machine learning is improving your journeys", "url": "https://www.networkrail.co.uk/stories/future-rail-how-machine-learning-is-improving-your-journeys/", "date": "2022-10-31", "written_by": "deployer", "quote": "Automated Intelligent Video Review (AIVR) – developed by Bristol-based tech company One Big Circle – gathers a wide range of data from high-definition video around the railway." } ], "replication": { "produced_by": [ "Front-of-train cameras on scheduled passenger services covering the same track daily", "Machine-learning review that raises possible defects to the infrastructure manager", "A maintenance organization able to schedule overnight possessions" ], "must_hold": [ "Engineers triage AI reports promptly", "False positives (such as the snake) do not swamp the reporting channel", "The fault is visible from a train-mounted camera" ], "echoes": [ { "date": "2026-07-03", "setting": "The same AIVR system on an LNER train raised a snake crossing the line as a potential rail defect; Network Rail said raising it automatically was standard practice", "model_version": null, "outcome": "did not recur", "source": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/" } ] }, "paired_incident": null, "headline": "LNER AIVR flags a Retford track fault; overnight fix, zero delay", "metric": "an overnight repair with no delay to passengers and zero delay minutes", "comparator": { "against": "a track defect reported by a train driver a week earlier (January 2026, Cambridgeshire)", "before": "over 10,000 delay minutes, multiple cancellations, and led to a full day of disruption for customers", "after": "an overnight repair, with no delay to passengers, and zero delay minutes caused", "basis": "measured", "quote": "In January 2026, a track defect was reported by a train driver in Cambridgeshire which caused over 10,000 delay minutes, multiple cancellations, and led to a full day of disruption for customers. However, a week later, the AIVR system identified a minor fault which had the potential to turn into a larger issue near Retford. The report allowed engineers to carry out an overnight repair, with no delay to passengers, and zero delay minutes caused.", "url": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/" }, "later": { "status": "persisted", "date": "2026-07-06", "note": "The system was still running on LNER trains in July 2026, when it raised a snake crossing the line as a potential track defect; Network Rail said raising it automatically was standard practice.", "quote": "An artificial intelligence-powered system on an LNER (London North Eastern Railway) train designed to inform LNER and Network Rail about issues on the railway, misidentified a snake as a track defect.", "url": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/; https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.onebigcircle.co.uk/contact; https://en.wikipedia.org/wiki/London_North_Eastern_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0255", "catalog_version": "1.0.0", "source_fingerprint": "c075807381a97cbf07f267ea4cf5da92a2d06c318e0f1c74a67f9a0915908db7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0255-source-0001", "AIBD-0255-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "One Big Circle", "deployer": "LNER (on-train cameras), with findings passed to Network Rail" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "AIVR's analysis raised the fault report that Network Rail engineers acted on. No human had reported it. Whether the minor fault would have escalated is LNER's claim. Detection rates and false-positive rates are not published." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "AIVR's analysis raised the fault report that Network Rail engineers acted on. No human had reported it. Whether the minor fault would have escalated is LNER's claim. 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Future Rail – how machine learning is improving your journeys.", "title": "Future Rail – how machine learning is improving your journeys", "url": "https://www.networkrail.co.uk/stories/future-rail-how-machine-learning-is-improving-your-journeys/", "date": "2022-10-31", "written_by": "deployer", "quote": "Automated Intelligent Video Review (AIVR) – developed by Bristol-based tech company One Big Circle – gathers a wide range of data from high-definition video around the railway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0255-source-0001", "AIBD-0255-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Pashby T (6 Jul 2026). 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Future Rail – how machine learning is improving your journeys.", "title": "Future Rail – how machine learning is improving your journeys", "url": "https://www.networkrail.co.uk/stories/future-rail-how-machine-learning-is-improving-your-journeys/", "date": "2022-10-31", "written_by": "deployer", "quote": "Automated Intelligent Video Review (AIVR) – developed by Bristol-based tech company One Big Circle – gathers a wide range of data from high-definition video around the railway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "New Civil Engineer (6 Jul 2026) quoting LNER's 9 Apr 2026 announcement: after a January 2026 Cambridgeshire defect caused over 10,000 delay minutes, One Big Circle's AIVR identified a developing fa...", "account": "New Civil Engineer's Tom Pashby reported on 6 Jul 2026. His story was about the system flagging a snake as a track defect on 3 July. LNER runs the Pantograph Damage Assessment System (PANDAS) and One Big Circle's Automated Intelligent Video Review (AIVR) on a number of its trains. They assess overhead line equipment and track and report potential damage to Network Rail. The article reproduces LNER's 9 April 2026 announcement. In January 2026 a train driver in Cambridgeshire reported a track defect. It caused over 10,000 delay minutes, multiple cancellations and a full day of disruption. A week later the AIVR system identified a minor fault near Retford with the potential to become a larger issue. Engineers carried out an overnight repair with no delay to passengers and zero delay minutes. An LNER spokesperson said the information is fed back to Network Rail to target areas for attention. A Network Rail spokesperson said the snake would be raised automatically as standard practice. Network Rail's own 2022 story describes AIVR as a phone-sized front-of-train device applying machine learning to video of more than 20,000 miles of track. The exact day of the Retford detection is not given. LNER's announcement itself and later Southeastern and South Western Railway AIVR releases are not among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Automated Intelligent Video Review (AIVR) front-of-train camera system with machine-learning analysis" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "One Big Circle" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "LNER's on-train AIVR camera AI flags a minor track fault near Retford a week after a driver-reported defect cost 10,000 delay minutes; overnight repair, zero delay minutes", "headline": "LNER AIVR flags a Retford track fault; overnight fix, zero delay" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c075807381a97cbf07f267ea4cf5da92a2d06c318e0f1c74a67f9a0915908db7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "New Civil Engineer's Tom Pashby reported on 6 Jul 2026. His story was about the system flagging a snake as a track defect on 3 July. LNER runs the Pantograph Damage Assessment System (PANDAS) and One Big Circle's Automated Intelligent Video Review (AIVR) on a number of its trains. They assess overhead line equipment and track and report potential damage to Network Rail. The article reproduces LNER's 9 April 2026 announcement. In January 2026 a train driver in Cambridgeshire reported a track defect. It caused over 10,000 delay minutes, multiple cancellations and a full day of disruption. A week later the AIVR system identified a minor fault near Retford with the potential to become a larger issue. Engineers carried out an overnight repair with no delay to passengers and zero delay minutes. An LNER spokesperson said the information is fed back to Network Rail to target areas for attention. A Network Rail spokesperson said the snake would be raised automatically as standard practice. Network Rail's own 2022 story describes AIVR as a phone-sized front-of-train device applying machine learning to video of more than 20,000 miles of track. The exact day of the Retford detection is not given. LNER's announcement itself and later Southeastern and South Western Railway AIVR releases are not among the sources.", "replication": { "produced_by": [ "Front-of-train cameras on scheduled passenger services covering the same track daily", "Machine-learning review that raises possible defects to the infrastructure manager", "A maintenance organization able to schedule overnight possessions" ], "must_hold": [ "Engineers triage AI reports promptly", "False positives (such as the snake) do not swamp the reporting channel", "The fault is visible from a train-mounted camera" ], "echoes": [ { "date": "2026-07-03", "setting": "The same AIVR system on an LNER train raised a snake crossing the line as a potential rail defect; Network Rail said raising it automatically was standard practice", "outcome": "did not recur", "source": "https://www.newcivilengineer.com/latest/ai-system-on-lner-train-misidentifies-snake-as-track-defect-06-07-2026/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0343", "case_url": "records/AIBD-0343.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." }, { "label": "AI contribution", "value": "The report credits the repair to a MARS alert and UNEP's confirmation. Only UNEP's general page describes MARS detection and analysis as using satellites with advanced AI models. The AI contribution to this specific alert is not isolated. One news item relaying the system operator's snapshot documents the event, and the snapshot itself is not among the sources." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Canada" ] }, "record": { "id": "AIBD-0343", "title": "After a UNEP MARS alert, operators repair a failed ball valve that released 175 t of methane in one week at the Aerial oil and gas field, Alberta, a week after detection (April 2026)", "summary": "Trade press relaying UNEP's August 2026 snapshot: a 175 t/week methane plume at Alberta's Aerial field in April 2026 was traced to a failed ball valve on pigging equipment and repaired within a week.", "date_observed": "2026-04-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operators at the Aerial oil and gas field (repair)", "location": "Aerial oil and gas field, southeastern Alberta, Canada", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas production; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ], "ai_role": { "status": "assessed", "statement": "The report credits the repair to a MARS alert and UNEP's confirmation. Only UNEP's general page describes MARS detection and analysis as using satellites with advanced AI models. The AI contribution to this specific alert is not isolated. One news item relaying the system operator's snapshot documents the event, and the snapshot itself is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ], "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "model_version": null, "outcome": "recurred", "source": "AIBD-0091" } ] }, "paired_incident": null, "headline": "UNEP MARS alert leads to Alberta methane leak repair in a week", "metric": "released 175 t of methane (4,900 tCO2e) in a single week", "comparator": { "against": "the leak before repair", "before": "175 t of methane (4,900 tCO2e) in a single week in April", "after": "Operators completed the repair work one week after the methane plume was first detected.", "basis": "measured", "quote": "In its monthly snapshot of the biggest methane plumes recorded by satellites in August, the UNEP confirmed operators had plugged a source of methane that emitted 175 t of methane (4,900 tCO2e) in a single week in April.", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" }, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the 4 September 2026 article was found; UNEP's September 2026 snapshot does not mention Canada.", "quote": null, "url": null }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0343", "catalog_version": "1.0.0", "source_fingerprint": "0ef391e4175d499d06ad640f10c507dee33241c2b451a8983764232aa2f2583e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0343-source-0001", "AIBD-0343-source-0002", "AIBD-0343-source-0003", "AIBD-0343-source-0004", "AIBD-0343-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operators at the Aerial oil and gas field (repair)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The report credits the repair to a MARS alert and UNEP's confirmation. 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}, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "released 175 t of methane (4,900 tCO2e) in a single week", "editor_note": "" } }, 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"provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The operator found and fixed a failed valve releasing 175 t of methane a week, within one week of detection. The operator is unnamed, and no cost or further detail is reported." }, { "group": "societal", "depth": 1, "reason": "A single confirmed case among about 43 since 2023, against more than 3,500 alerts. A one-week 4,900 tCO2e release was stopped." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Aerial oil and gas field, southeastern Alberta, Canada", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Satellite-based detection and notification of large methane plumes so that operators mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-04-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas production; intergovernmental observatory" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0343-source-0001", "AIBD-0343-source-0002", "AIBD-0343-source-0003", "AIBD-0343-source-0004", "AIBD-0343-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Baker, S. (2026). Canada plugs 'major source' of methane emissions from oil and gas field leak. The Chemical Engineer, 4 Sep 2026.", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Operators completed the repair work one week after the methane plume was first detected" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, emission estimate.", "title": "Canada plugs 'major source' of methane emissions (emission estimate)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "175 t of methane (4,900 tCO2e) in a single week in April" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, cause of the leak.", "title": "Canada plugs 'major source' of methane emissions (cause)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "a failed ball valve connected to some pigging equipment" }, { "kind": "news report", "citation": "Baker, S. (2026). Same article, programme totals.", "title": "Canada plugs 'major source' of methane emissions (MARS totals)", "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "date": "2026-09-04", "written_by": "independent", "quote": "Since 2023, MARS has sent over 3,500 alerts to operators of sites releasing methane plumes, but has confirmed just 43 mitigation cases." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Trade press relaying UNEP's August 2026 snapshot: a 175 t/week methane plume at Alberta's Aerial field in April 2026 was traced to a failed ball valve on pigging equipment and repaired within a week.", "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "After a UNEP MARS alert, operators repair a failed ball valve that released 175 t of methane in one week at the Aerial oil and gas field, Alberta, a week after detection (April 2026)", "headline": "UNEP MARS alert leads to Alberta methane leak repair in a week" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0ef391e4175d499d06ad640f10c507dee33241c2b451a8983764232aa2f2583e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Chemical Engineer reported on 4 September 2026 on UNEP's monthly snapshot of the biggest satellite-detected methane plumes recorded in August. UNEP confirmed that operators had plugged a source at the Aerial oil and gas field in southeastern Alberta, Canada. The source had released 175 t of methane (4,900 tCO2e) in a single week in April 2026. The cause was a failed ball valve connected to pigging equipment. Operators completed the repair one week after the plume was first detected. The repair followed an alert issued under UNEP's Methane Alert and Response System (MARS). The article states that UNEP does not name suspected or known operators. It states that since 2023 MARS has sent over 3,500 alerts but confirmed just 43 mitigation cases. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments. It couples that with scientific expertise and advanced AI models to notify governments and companies. The August 2026 UNEP snapshot itself is not among the sources; UNEP's snapshot page now carries the September 2026 update, which does not mention Canada. UNEP's press pages were not available and are not among the sources. The case rests on one trade-press report relaying UNEP.", "replication": { "produced_by": [ "Satellite detection of a large plume at a producing field", "A MARS alert reaching operators able to inspect equipment within days", "A discrete equipment failure (ball valve on pigging equipment) that a repair could end" ], "must_hold": [ "Responsive operators or a national focal point. UNEP's snapshot says most mitigation occurs where focal points direct information", "Clear-sky satellite coverage to detect and later confirm cessation", "Independent confirmation beyond UNEP's own snapshot, which is not among the sources" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: pipeline leak stopped by rerouting gas after a MARS notification (UNEP case document); emissions observed again by 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (existing record AIBD-0091): MARS notification followed by repair", "outcome": "recurred", "source": "AIBD-0091" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0312", "case_url": "records/AIBD-0312.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Built to detect smoke on camera feeds and notify the utility and fire agencies earlier than 911 calls. This detection was that purpose." }, { "label": "AI contribution", "value": "AP reports that the camera's AI detected something resembling smoke and spotted early signs of the fire. It does not say whether the alert was the first report or what the fire agencies did with it. The Daily Sun's timeline does not mention the camera: first report, 7 acres by 3:16 p.m., progress stopped 3:37 p.m., contained 6:09 p.m. The attribution to AI rests on one news report, and the camera's maker is implied rather than stated. No official record naming the camera was found." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0312", "title": "AI smoke-detection camera installed for Arizona Public Service spots early signs of the March 2026 Diamond Fire in Coconino National Forest; crews hold it under 7 acres", "summary": "AP: an APS AI camera detected smoke on a March 2026 afternoon and crews contained the Diamond Fire before 7 acres; the Arizona Daily Sun's account of 26 March 2026 gives the timeline but no camera.", "date_observed": "2026-03-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Associated Press reported on 4 May 2026 that artificial intelligence detected something resembling smoke on a camera feed from Arizona's Coconino National Forest. The report is by Dorany Pineda and Brittany Peterson, on record in the KPBS and Courthouse News republications. The detection came on a March afternoon. One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs. The fire came to be known as the Diamond Fire. Firefighters raced to the scene and contained it before it grew past 7 acres (2.8 hectares). AP says APS has nearly 40 active AI smoke-detection cameras and plans 71 by summer's end. APS meteorologist Cindy Kobold says the technology notifies the utility about 45 minutes faster on average than the first 911 call. Pano AI, launched in 2020 and charging around $50,000 a year per camera, counts APS among its customers. It says it detected 725 wildfires in the US last year. AP does not state outright which company made the camera that spotted the Diamond Fire. The Arizona Daily Sun (staff report, 26 March 2026) reports that a small fire later named the Diamond Fire began shortly after 2 p.m. on Thursday. It started just north of Lake Mary Road, about three miles south-east of Lake Mary Road and Beulah Boulevard. That is south of Pine Canyon Golf Club and east of Flagstaff Pulliam Airport. It reached seven acres by 3:16 p.m. according to WatchDuty. Its forward progress was stopped at 3:37 p.m., about 80 minutes after the first report, and it was 100% contained at 6:09 p.m. The Daily Sun does not mention a camera, the Coconino National Forest or who made the first report. The two accounts are matched by the fire's name, month and size. No official incident record naming the camera was found; InciWeb's “Diamond Fire” is a June 2023 Scottsdale fire. The Daily Sun reported the fire that Thursday.", "category": "protective response", "unexpected": false, "purpose": "Built to detect smoke on camera feeds and notify the utility and fire agencies earlier than 911 calls. This detection was that purpose.", "system": "AI smoke-detection camera installed for Arizona Public Service (AP lists APS among Pano AI's customers; the Diamond Fire camera's maker is not stated outright)", "model_version": null, "developer": "Pano AI, by implication of AP's customer list; not stated for this camera", "deployer": "Arizona Public Service", "location": "Coconino National Forest near Flagstaff, Arizona, United States", "domain": "Wildfire detection and response", "segment": "Electric utility wildfire mitigation", "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "A fire near Flagstaff was held to 7 acres, with forward progress stopped about 80 minutes after the first report, per the Daily Sun. AP, in a single report, attributes early detection to the APS camera." }, { "group": "organizational", "depth": null, "reason": "APS's own gain for this fire is not documented, whether protection of its lines or earlier dispatch. Its meteorologist gives only an average 45-minute lead over 911 calls across fires." } ], "ai_role": { "status": "assessed", "statement": "AP reports that the camera's AI detected something resembling smoke and spotted early signs of the fire. It does not say whether the alert was the first report or what the fire agencies did with it. The Daily Sun's timeline does not mention the camera: first report, 7 acres by 3:16 p.m., progress stopped 3:37 p.m., contained 6:09 p.m. The attribution to AI rests on one news report, and the camera's maker is implied rather than stated. No official record naming the camera was found." }, "evidence": [ { "kind": "news report", "citation": "Pineda D, Peterson B (2026). States across the wildfire-prone Western US are using AI for early detection. Associated Press, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://apnews.com/article/ai-wildfire-detection-cameras-194656fe63ea89dbc4661eaf8b79f6bb", "date": "2026-05-04", "written_by": "independent", "quote": "One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs of what came to be known as the Diamond Fire." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by KPBS, 5 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.kpbs.org/news/science-technology/2026/05/05/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection", "date": "2026-05-05", "written_by": "independent", "quote": "Firefighters raced to the scene and contained the blaze before it grew past 7 acres (2.8 hectares)." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by Courthouse News Service, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "date": "2026-05-04", "written_by": "independent", "quote": "Cindy Kobold, an Arizona Public Service meteorologist, said the technology notifies them about 45 minutes faster on average than the first 911 call." }, { "kind": "news report", "citation": "Daily Sun staff (2026). Seven-acre fire fully contained south of Flagstaff on Thursday afternoon. Arizona Daily Sun, 26 March 2026 (the article does not mention a camera).", "title": "Seven-acre fire fully contained south of Flagstaff on Thursday afternoon", "url": "https://azdailysun.com/news/local/seven-acre-fire-fully-contained-south-of-flagstaff-on-thursday-afternoon/article_585bebfe-c17c-4872-a37d-7e08195a39d7.html", "date": "2026-03-26", "written_by": "independent", "quote": "Forward progress on the fire was stopped at 3:37 p.m., about 80 minutes after the first report of the fire, and reached 100% containment at 6:09 p.m." } ], "replication": { "produced_by": [ "An AI smoke-detection camera network installed for a utility covering national-forest land near Flagstaff", "Human review of the AI detection, then notification to the utility and fire agencies. AP says human intervention keeps false positives low", "Crews able to reach a small fire on a March afternoon within about 80 minutes of the first report" ], "must_hold": [ "The camera has line of sight to the ignition and the detection reaches a responding agency before the fire grows", "Fire weather and fuels that allow containment at a few acres. The sources give no conditions for this fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire in Douglas County, Colorado; held to three acres (AIBD-0161)", "model_version": null, "outcome": "recurred", "source": "AIBD-0161" }, { "date": "2024-07-09", "setting": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge, Colorado; crews hold it to one-eighth of an acre (AIBD-0162)", "model_version": null, "outcome": "recurred", "source": "AIBD-0162" }, { "date": null, "setting": "Pano AI's own claim, relayed by AP, that its technology detected 725 wildfires in the US in the previous year; no outcomes given", "model_version": null, "outcome": "unknown", "source": "Associated Press, 4 May 2026" } ] }, "paired_incident": null, "headline": "APS AI camera spots Diamond Fire; crews hold it under 7 acres", "metric": "contained it before it grew past 7 acres (2.8 hectares)", "comparator": { "against": "the first 911 call reporting a fire", "before": "the first 911 call", "after": "about 45 minutes faster on average than the first 911 call", "basis": "assumed", "quote": "Cindy Kobold, an Arizona Public Service meteorologist, said the technology notifies them about 45 minutes faster on average than the first 911 call.", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" }, "later": { "status": "scaled", "date": "2026-05-04", "note": "Seven weeks after the fire, AP reported that APS had nearly 40 active AI smoke-detection cameras and planned to have 71 by the end of summer 2026.", "quote": "Arizona Public Service has nearly 40 active AI smoke-detection cameras and plans to have 71 by summer’s end, and the state’s fire agency has deployed seven of its own.", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/; https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.pbs.org/newshour/show/how-satellites-and-ai-cameras-are-detecting-wildfires-before-they-get-out-of-control; https://en.wikipedia.org/wiki/Arizona_Public_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports. 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has deployed seven of its own.", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0312" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": 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The report is by Dorany Pineda and Brittany Peterson, on record in the KPBS and Courthouse News republications. The detection came on a March afternoon. One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs. The fire came to be known as the Diamond Fire. Firefighters raced to the scene and contained it before it grew past 7 acres (2.8 hectares). AP says APS has nearly 40 active AI smoke-detection cameras and plans 71 by summer's end. APS meteorologist Cindy Kobold says the technology notifies the utility about 45 minutes faster on average than the first 911 call. Pano AI, launched in 2020 and charging around $50,000 a year per camera, counts APS among its customers. It says it detected 725 wildfires in the US last year. AP does not state outright which company made the camera that spotted the Diamond Fire. The Arizona Daily Sun (staff report, 26 March 2026) reports that a small fire later named the Diamond Fire began shortly after 2 p.m. on Thursday. It started just north of Lake Mary Road, about three miles south-east of Lake Mary Road and Beulah Boulevard. That is south of Pine Canyon Golf Club and east of Flagstaff Pulliam Airport. It reached seven acres by 3:16 p.m. according to WatchDuty. Its forward progress was stopped at 3:37 p.m., about 80 minutes after the first report, and it was 100% contained at 6:09 p.m. The Daily Sun does not mention a camera, the Coconino National Forest or who made the first report. The two accounts are matched by the fire's name, month and size. No official incident record naming the camera was found; InciWeb's “Diamond Fire” is a June 2023 Scottsdale fire. The Daily Sun reported the fire that Thursday.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "A fire near Flagstaff was held to 7 acres, with forward progress stopped about 80 minutes after the first report, per the Daily Sun. AP, in a single report, attributes early detection to the APS camera." }, { "group": "organizational", "reason": "APS's own gain for this fire is not documented, whether protection of its lines or earlier dispatch. Its meteorologist gives only an average 45-minute lead over 911 calls across fires." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect smoke on camera feeds and notify the utility and fire agencies earlier than 911 calls. This detection was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Coconino National Forest near Flagstaff, Arizona, United States", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect smoke on camera feeds and notify the utility and fire agencies earlier than 911 calls. This detection was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-03-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/; https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.pbs.org/newshour/show/how-satellites-and-ai-cameras-are-detecting-wildfires-before-they-get-out-of-control; https://en.wikipedia.org/wiki/Arizona_Public_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "An AI smoke-detection camera network installed for a utility covering national-forest land near Flagstaff", "Human review of the AI detection, then notification to the utility and fire agencies. AP says human intervention keeps false positives low", "Crews able to reach a small fire on a March afternoon within about 80 minutes of the first report" ], "must_hold": [ "The camera has line of sight to the ignition and the detection reaches a responding agency before the fire grows", "Fire weather and fuels that allow containment at a few acres. The sources give no conditions for this fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire in Douglas County, Colorado; held to three acres (AIBD-0161)", "outcome": "recurred", "source": "AIBD-0161" }, { "date": "2024-07-09", "setting": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge, Colorado; crews hold it to one-eighth of an acre (AIBD-0162)", "outcome": "recurred", "source": "AIBD-0162" }, { "setting": "Pano AI's own claim, relayed by AP, that its technology detected 725 wildfires in the US in the previous year; no outcomes given", "outcome": "unknown", "source": "Associated Press, 4 May 2026" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "An AI smoke-detection camera network installed for a utility covering national-forest land near Flagstaff", "Human review of the AI detection, then notification to the utility and fire agencies. 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The sources give no conditions for this fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire in Douglas County, Colorado; held to three acres (AIBD-0161)", "outcome": "recurred", "source": "AIBD-0161" }, { "date": "2024-07-09", "setting": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge, Colorado; crews hold it to one-eighth of an acre (AIBD-0162)", "outcome": "recurred", "source": "AIBD-0162" }, { "setting": "Pano AI's own claim, relayed by AP, that its technology detected 725 wildfires in the US in the previous year; no outcomes given", "outcome": "unknown", "source": "Associated Press, 4 May 2026" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/; https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.pbs.org/newshour/show/how-satellites-and-ai-cameras-are-detecting-wildfires-before-they-get-out-of-control; https://en.wikipedia.org/wiki/Arizona_Public_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Wildfire detection and response", "segment": "Electric utility wildfire mitigation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Pineda D, Peterson B (2026). States across the wildfire-prone Western US are using AI for early detection. Associated Press, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://apnews.com/article/ai-wildfire-detection-cameras-194656fe63ea89dbc4661eaf8b79f6bb", "date": "2026-05-04", "written_by": "independent", "quote": "One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs of what came to be known as the Diamond Fire." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by KPBS, 5 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.kpbs.org/news/science-technology/2026/05/05/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection", "date": "2026-05-05", "written_by": "independent", "quote": "Firefighters raced to the scene and contained the blaze before it grew past 7 acres (2.8 hectares)." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by Courthouse News Service, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "date": "2026-05-04", "written_by": "independent", "quote": "Cindy Kobold, an Arizona Public Service meteorologist, said the technology notifies them about 45 minutes faster on average than the first 911 call." }, { "kind": "news report", "citation": "Daily Sun staff (2026). Seven-acre fire fully contained south of Flagstaff on Thursday afternoon. Arizona Daily Sun, 26 March 2026 (the article does not mention a camera).", "title": "Seven-acre fire fully contained south of Flagstaff on Thursday afternoon", "url": "https://azdailysun.com/news/local/seven-acre-fire-fully-contained-south-of-flagstaff-on-thursday-afternoon/article_585bebfe-c17c-4872-a37d-7e08195a39d7.html", "date": "2026-03-26", "written_by": "independent", "quote": "Forward progress on the fire was stopped at 3:37 p.m., about 80 minutes after the first report of the fire, and reached 100% containment at 6:09 p.m." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0312-source-0001", "AIBD-0312-source-0002", "AIBD-0312-source-0003", "AIBD-0312-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Pineda D, Peterson B (2026). States across the wildfire-prone Western US are using AI for early detection. Associated Press, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://apnews.com/article/ai-wildfire-detection-cameras-194656fe63ea89dbc4661eaf8b79f6bb", "date": "2026-05-04", "written_by": "independent", "quote": "One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs of what came to be known as the Diamond Fire." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by KPBS, 5 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.kpbs.org/news/science-technology/2026/05/05/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection", "date": "2026-05-05", "written_by": "independent", "quote": "Firefighters raced to the scene and contained the blaze before it grew past 7 acres (2.8 hectares)." }, { "kind": "news report", "citation": "Pineda D, Peterson B (2026). The same AP report as republished by Courthouse News Service, 4 May 2026.", "title": "States across the wildfire-prone Western US are using AI for early detection", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/", "date": "2026-05-04", "written_by": "independent", "quote": "Cindy Kobold, an Arizona Public Service meteorologist, said the technology notifies them about 45 minutes faster on average than the first 911 call." }, { "kind": "news report", "citation": "Daily Sun staff (2026). Seven-acre fire fully contained south of Flagstaff on Thursday afternoon. Arizona Daily Sun, 26 March 2026 (the article does not mention a camera).", "title": "Seven-acre fire fully contained south of Flagstaff on Thursday afternoon", "url": "https://azdailysun.com/news/local/seven-acre-fire-fully-contained-south-of-flagstaff-on-thursday-afternoon/article_585bebfe-c17c-4872-a37d-7e08195a39d7.html", "date": "2026-03-26", "written_by": "independent", "quote": "Forward progress on the fire was stopped at 3:37 p.m., about 80 minutes after the first report of the fire, and reached 100% containment at 6:09 p.m." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AP: an APS AI camera detected smoke on a March 2026 afternoon and crews contained the Diamond Fire before 7 acres; the Arizona Daily Sun's account of 26 March 2026 gives the timeline but no camera.", "account": "The Associated Press reported on 4 May 2026 that artificial intelligence detected something resembling smoke on a camera feed from Arizona's Coconino National Forest. The report is by Dorany Pineda and Brittany Peterson, on record in the KPBS and Courthouse News republications. The detection came on a March afternoon. One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs. The fire came to be known as the Diamond Fire. Firefighters raced to the scene and contained it before it grew past 7 acres (2.8 hectares). AP says APS has nearly 40 active AI smoke-detection cameras and plans 71 by summer's end. APS meteorologist Cindy Kobold says the technology notifies the utility about 45 minutes faster on average than the first 911 call. Pano AI, launched in 2020 and charging around $50,000 a year per camera, counts APS among its customers. It says it detected 725 wildfires in the US last year. AP does not state outright which company made the camera that spotted the Diamond Fire. The Arizona Daily Sun (staff report, 26 March 2026) reports that a small fire later named the Diamond Fire began shortly after 2 p.m. on Thursday. It started just north of Lake Mary Road, about three miles south-east of Lake Mary Road and Beulah Boulevard. That is south of Pine Canyon Golf Club and east of Flagstaff Pulliam Airport. It reached seven acres by 3:16 p.m. according to WatchDuty. Its forward progress was stopped at 3:37 p.m., about 80 minutes after the first report, and it was 100% contained at 6:09 p.m. The Daily Sun does not mention a camera, the Coconino National Forest or who made the first report. The two accounts are matched by the fire's name, month and size. No official incident record naming the camera was found; InciWeb's “Diamond Fire” is a June 2023 Scottsdale fire. The Daily Sun reported the fire that Thursday." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI smoke-detection camera installed for Arizona Public Service (AP lists APS among Pano AI's customers; the Diamond Fire camera's maker is not stated outright)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Pano AI, by implication of AP's customer list; not stated for this camera" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "AI smoke-detection camera installed for Arizona Public Service spots early signs of the March 2026 Diamond Fire in Coconino National Forest; crews hold it under 7 acres", "headline": "APS AI camera spots Diamond Fire; crews hold it under 7 acres" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c81aed4312d5a518f38cb56ae8aac7ef5e300608c8f387fa0b37d080e2be4538" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Associated Press reported on 4 May 2026 that artificial intelligence detected something resembling smoke on a camera feed from Arizona's Coconino National Forest. The report is by Dorany Pineda and Brittany Peterson, on record in the KPBS and Courthouse News republications. The detection came on a March afternoon. One of dozens of AI cameras installed for the utility Arizona Public Service had spotted early signs. The fire came to be known as the Diamond Fire. Firefighters raced to the scene and contained it before it grew past 7 acres (2.8 hectares). AP says APS has nearly 40 active AI smoke-detection cameras and plans 71 by summer's end. APS meteorologist Cindy Kobold says the technology notifies the utility about 45 minutes faster on average than the first 911 call. Pano AI, launched in 2020 and charging around $50,000 a year per camera, counts APS among its customers. It says it detected 725 wildfires in the US last year. AP does not state outright which company made the camera that spotted the Diamond Fire. The Arizona Daily Sun (staff report, 26 March 2026) reports that a small fire later named the Diamond Fire began shortly after 2 p.m. on Thursday. It started just north of Lake Mary Road, about three miles south-east of Lake Mary Road and Beulah Boulevard. That is south of Pine Canyon Golf Club and east of Flagstaff Pulliam Airport. It reached seven acres by 3:16 p.m. according to WatchDuty. Its forward progress was stopped at 3:37 p.m., about 80 minutes after the first report, and it was 100% contained at 6:09 p.m. The Daily Sun does not mention a camera, the Coconino National Forest or who made the first report. The two accounts are matched by the fire's name, month and size. No official incident record naming the camera was found; InciWeb's “Diamond Fire” is a June 2023 Scottsdale fire. The Daily Sun reported the fire that Thursday.", "replication": { "produced_by": [ "An AI smoke-detection camera network installed for a utility covering national-forest land near Flagstaff", "Human review of the AI detection, then notification to the utility and fire agencies. AP says human intervention keeps false positives low", "Crews able to reach a small fire on a March afternoon within about 80 minutes of the first report" ], "must_hold": [ "The camera has line of sight to the ignition and the detection reaches a responding agency before the fire grows", "Fire weather and fuels that allow containment at a few acres. The sources give no conditions for this fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire in Douglas County, Colorado; held to three acres (AIBD-0161)", "outcome": "recurred", "source": "AIBD-0161" }, { "date": "2024-07-09", "setting": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge, Colorado; crews hold it to one-eighth of an acre (AIBD-0162)", "outcome": "recurred", "source": "AIBD-0162" }, { "setting": "Pano AI's own claim, relayed by AP, that its technology detected 725 wildfires in the US in the previous year; no outcomes given", "outcome": "unknown", "source": "Associated Press, 4 May 2026" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0206", "case_url": "records/AIBD-0206.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "Claude Code is Anthropic's agentic coding tool, built to read a codebase, edit files, run commands and connect to external tools over MCP. Wiz used it, with IDA MCP, to automate reverse engineering of GitHub's compiled binaries. Analyzing code for flaws is within that general purpose. The benefit was an application of what the tool was built to do rather than a departure from it." }, { "label": "AI contribution", "value": "The AI's documented contribution is speed of analysis, on the researchers' own account. Wiz writes that AI-augmented reverse-engineering tooling, particularly IDA MCP, let it rapidly analyze GitHub's compiled binaries and reconstruct internal protocols. Wiz says that speed would not have been feasible manually. Sagi Tzadik told Dark Reading that with AI tools it took under 48 hours from idea to working exploit. The Register adds that the team used Claude Code “to take a lot of the legwork out of the process”. The hypothesis, the exploit, the report and the fix were human work. Six named Wiz researchers confirmed RCE on GHES 3.19.1 and wrote what GitHub called a thorough report. GitHub's security and engineering teams reproduced the bug within 40 minutes and deployed the fix. GitHub's own post, the CVE record and the GHES release notes describe a Bug Bounty report without mentioning AI. The AI-assistance claim therefore rests on Wiz and on outlets quoting Wiz. Wiz's write-up, in the text as read, names IDA MCP and not Claude." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0206", "title": "Wiz Research's AI-augmented reverse engineering of GitHub's closed-source git binaries, with Claude Code and IDA MCP, finds a critical push-option injection giving remote code execution on GitHub.com and GitHub Enterprise Server; GitHub reproduces it in 40 minutes, patches github.com by 7:00 pm UTC on 4 March 2026 and finds no exploitation", "summary": "Wiz's AI-aided reverse engineering found a critical GitHub RCE (CVE-2026–3854); github.com was patched the same day, no exploitation found.", "date_observed": "2026-03-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 4 March 2026 Wiz Research reported a critical remote code execution (RCE) flaw to GitHub, later CVE-2026–3854. It sat in GitHub's internal git infrastructure. Git push options were not sanitized before being written into internal headers. Any authenticated user could therefore run arbitrary commands on GitHub's servers with a single git push. Wiz says AI-augmented tooling made the research feasible, particularly automated reverse engineering of GitHub's compiled binaries with IDA MCP. The Register reports that the team used Claude Code and went from idea to working exploit in under 48 hours. GitHub reproduced the bug within 40 minutes and deployed a fix to github.com at 7:00 pm UTC that day. It released GitHub Enterprise Server (GHES) patches from 10 March, and found no exploitation.", "category": "benefit created", "unexpected": false, "purpose": "Claude Code is Anthropic's agentic coding tool, built to read a codebase, edit files, run commands and connect to external tools over MCP. Wiz used it, with IDA MCP, to automate reverse engineering of GitHub's compiled binaries. Analyzing code for flaws is within that general purpose. The benefit was an application of what the tool was built to do rather than a departure from it.", "system": "AI-augmented reverse-engineering tooling used by Wiz Research: Claude Code (named by The Register and WebProNews) with IDA MCP for automated reverse engineering of GitHub's compiled binaries (named by Wiz), applied to GitHub.com's backend and GitHub Enterprise Server 3.19.1", "model_version": "Not named in the cited sources. Wiz's write-up credits 'AI-augmented tooling, particularly automated reverse engineering using IDA MCP' and does not name Claude. The Register and WebProNews name Claude Code.", "developer": "Anthropic (Claude Code); the maker of IDA MCP is not named in the sources", "deployer": "Wiz Research (Sagi Tzadik, Nir Ohfeld, Ronen Shustin, Hillai Ben-Sasson, Yuval Avrahami, Noam Malron)", "location": null, "domain": "Cybersecurity: vulnerability research and software supply chain", "segment": "Bug bounty research: AI-assisted reverse engineering of closed-source server binaries", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-04-28-f5d8" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "GitHub closed a critical remote code execution hole in its git push pipeline within hours of the report. The fix reached github.com at 7:00 pm UTC on 4 March 2026. GitHub also shipped patches for six GitHub Enterprise Server release lines. Wiz earned what GitHub calls one of the highest rewards in the history of its Bug Bounty program. The loss avoided is hypothetical. GitHub's forensic investigation found that no other account had triggered the code path. No customer data was accessed, modified or exfiltrated. The benefit is an exposure closed rather than a demonstrated loss prevented." } ], "ai_role": { "status": "assessed", "statement": "The AI's documented contribution is speed of analysis, on the researchers' own account. Wiz writes that AI-augmented reverse-engineering tooling, particularly IDA MCP, let it rapidly analyze GitHub's compiled binaries and reconstruct internal protocols. Wiz says that speed would not have been feasible manually. Sagi Tzadik told Dark Reading that with AI tools it took under 48 hours from idea to working exploit. The Register adds that the team used Claude Code “to take a lot of the legwork out of the process”. The hypothesis, the exploit, the report and the fix were human work. Six named Wiz researchers confirmed RCE on GHES 3.19.1 and wrote what GitHub called a thorough report. GitHub's security and engineering teams reproduced the bug within 40 minutes and deployed the fix. GitHub's own post, the CVE record and the GHES release notes describe a Bug Bounty report without mentioning AI. The AI-assistance claim therefore rests on Wiz and on outlets quoting Wiz. Wiz's write-up, in the text as read, names IDA MCP and not Claude." }, "evidence": [ { "kind": "report", "citation": "Wales A / GitHub (2026). Securing the git push pipeline: Responding to a critical remote code execution vulnerability. The GitHub Blog, 28 April 2026 (updated 29 April 2026).", "title": "Securing the git push pipeline: Responding to a critical remote code execution vulnerability", "url": "https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "date": "2026-04-28", "written_by": "beneficiary", "quote": "our engineering team developed and deployed a fix to github.com at 7:00 p.m. UTC that same day" }, { "kind": "other", "citation": "GitHub (CNA) (2026). CVE-2026-3854. CVE Program record, published 10 March 2026.", "title": "CVE-2026-3854", "url": "https://cveawg.mitre.org/api/cve/CVE-2026-3854", "date": "2026-03-10", "written_by": "beneficiary", "quote": "allowed an attacker with push access to a repository to achieve remote code execution on the instance" }, { "kind": "other", "citation": "GitHub (2026). GitHub Enterprise Server 3.14 release notes, 3.14.25, 12 March 2026. GitHub Docs.", "title": "GitHub Enterprise Server 3.14 release notes (3.14.25)", "url": "https://docs.github.com/en/enterprise-server@3.14/admin/release-notes", "date": "2026-03-12", "written_by": "beneficiary", "quote": "GitHub has requested CVE ID CVE-2026-3854 for this vulnerability, which was reported via the GitHub Bug Bounty program." }, { "kind": "press release", "citation": "Tzadik S, Ohfeld N, Shustin R, Ben-Sasson H, Avrahami Y, Malron N / Wiz Research (2026). Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854). Wiz Blog, 28 April 2026.", "title": "Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854)", "url": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854", "date": "2026-04-28", "written_by": "deployer", "quote": "This research was made possible by AI-augmented reverse engineering tooling, particularly IDA MCP, which allowed us to rapidly analyze GitHub's compiled binaries" }, { "kind": "news report", "citation": "Jones C / The Register (2026). GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash. The Register, 29 April 2026.", "title": "GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash", "url": "https://www.theregister.com/2026/04/29/github_woah_a_genuinely_helpful/", "date": "2026-04-29", "written_by": "independent", "quote": "They used Claude Code to take a lot of the legwork out of the process" }, { "kind": "news report", "citation": "Warren T / The Verge (2026). GitHub rushed to fix a critical vulnerability in less than six hours. The Verge, 29 April 2026.", "title": "GitHub rushed to fix a critical vulnerability in less than six hours", "url": "https://www.theverge.com/news/920295/github-remote-code-execution-vulnerability-fix", "date": "2026-04-29", "written_by": "independent", "quote": "A critical remote code execution vulnerability was discovered using an AI model and patched within hours." }, { "kind": "news report", "citation": "Culafi A / Dark Reading (2026). Reverse Engineering With AI Unearths High-Severity GitHub Bug. Dark Reading, 29 April 2026.", "title": "Reverse Engineering With AI Unearths High-Severity GitHub Bug", "url": "https://www.darkreading.com/application-security/reverse-engineering-ai-unearths-high-severity-github-bug", "date": "2026-04-29", "written_by": "independent", "quote": "With the help of AI tools, it took less than 48 hours to go from idea to a working exploit" }, { "kind": "news report", "citation": "Ritchie D / WebProNews (2026). Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty. WebProNews, 30 April 2026.", "title": "Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty", "url": "https://www.webpronews.com/wizs-ai-powered-strike-how-claude-cracked-githubs-git-fortress-for-a-fat-bounty/", "date": "2026-04-30", "written_by": "independent", "quote": "They leaned on Claude Code, Anthropic's agentic coding tool, for the heavy lifting." }, { "kind": "press release", "citation": "Anthropic (2026). Claude Code overview. Claude Code documentation.", "title": "Overview", "url": "https://code.claude.com/docs/en/overview", "date": null, "written_by": "makers", "quote": "Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools." } ], "replication": { "produced_by": [ "A closed-source target that researchers could obtain and disassemble. GitHub's backend git services also ship as GitHub Enterprise Server binaries. Those binaries gave automated reverse engineering something to work on, and GHES 3.19.1 gave a place to confirm the exploit.", "AI-augmented tooling for reverse engineering: IDA MCP (per Wiz) driven by Claude Code (per The Register). It reconstructed internal protocols and traced where user input could influence server behavior across the pipeline.", "Experienced vulnerability researchers: six named Wiz authors formed the hypothesis and built and confirmed a working exploit. They wrote what GitHub called a thorough report that clearly demonstrated the impact.", "A beneficiary with a Bug Bounty intake and an on-call security team. GitHub reproduced the bug within 40 minutes of the report. It deployed a fix to github.com at 7:00 pm UTC the same day." ], "must_hold": [ "AI output has to be confirmed by exploit, not trusted. Wiz confirmed RCE on a GHES 3.19.1 instance before reporting. That is why GitHub could validate the report in 40 minutes.", "The receiving organization must be able to patch its own service and ship fixes to self-hosted customers. GHES customers stayed exposed until they upgraded to the patched versions released from 10 March 2026.", "Disclosure must wait for the patches, here from 4 March to 28 April 2026. No exploitation had occurred, so the benefit is an exposure closed, not a loss recovered." ], "echoes": [] }, "paired_incident": null, "headline": "Wiz finds a critical GitHub RCE with Claude Code and IDA MCP", "metric": "went from idea to working exploit in under 48 hours", "comparator": { "against": "manual reverse engineering of GitHub's compiled binaries without AI tooling", "before": "an impractical amount of time and manual effort (Wiz); months' worth of manual analysis, per The Register's account of Wiz", "after": "from idea to working exploit in less than 48 hours", "basis": "assumed", "quote": "However, extracting and auditing the sheer volume of compiled blackbox binaries that run this pipeline historically required an impractical amount of time and manual effort. But this is Round 2, and the landscape has shifted. By leveraging AI-augmented tooling-particularly automated reverse engineering using IDA MCP-we were able to do what was previously too costly.", "url": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854" }, "later": { "status": "persisted", "date": "2026-04-28", "note": "GitHub's 28 April 2026 disclosure confirmed the same-day fix, patches for all supported GitHub Enterprise Server releases, and a forensic investigation that found no exploitation beyond the researchers' own testing.", "quote": "In less than two hours we had validated the finding, deployed a fix to github.com, and begun a forensic investigation that concluded there was no exploitation.", "url": "https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 4 March 2026, the day Wiz reported the flaw and GitHub fixed github.com (GitHub's post: root cause identified at 5:45 pm UTC, fix deployed at 7:00 pm UTC; Wiz and The Verge say within six hours of the report; GitHub's standfirst says under two hours); the Monitor's 28 April 2026 is the public-disclosure date. Filed as benefit created or restored: the AI-assisted discovery produced a concrete fix for an identifiable organization, and the harm avoided is hypothetical because GitHub's forensic investigation found no exploitation. The AI tool is named differently by the sources: The Register (byline Connor Jones on the page as first read) and WebProNews say Claude Code, and WebProNews names Anthropic; Wiz's own write-up credits “AI-augmented tooling, particularly automated reverse engineering using IDA MCP”, and a search of its text as read found no occurrence of “Claude” or “Anthropic”, so the record names Claude Code on The Register's authority and IDA MCP on Wiz's. No Claude model is named anywhere. Severity: the CVE record (CNA GitHub) gives CVSS 4.0 8.7; The Register reports an 8.8 rating from NIST. GHES patch dates: Wiz's timeline says 10 March 2026, the 3.14.25 release notes are dated 12 March 2026. The Wiz post is marked as written by the makers because it is the discoverer's own account of its tooling, although Wiz is not the AI vendor; GitHub's post is the beneficiary's statement and is not flagged. Anthropic's Claude Code overview is cited only for what the product is. The Monitor lists The Register's headline as “Wiz hands GitHub AI-aided bug report that isn't total slop”; the page as read carries the longer headline used in the citation. The Verge and Dark Reading refused direct fetches and were read through the r.jina.ai text proxy; NVD's page did not render; WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged, so the CVE record's reference list was used to locate the Wiz and GitHub posts.", "source": "https://oecd.ai/en/incidents/2026-04-28-f5d8", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854; https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Wiz_(company)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports, the makers' press release, the deployer's press release, the beneficiary's report and two other sources from the beneficiary. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0206", "catalog_version": "1.0.0", "source_fingerprint": "7f34bca5fb89430f7594439f89d31ee3a5bdf590dac0a26ae25031315a985960", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0206-source-0001", "AIBD-0206-source-0002", "AIBD-0206-source-0003", "AIBD-0206-source-0004", "AIBD-0206-source-0005", "AIBD-0206-source-0006", "AIBD-0206-source-0007", "AIBD-0206-source-0008", "AIBD-0206-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Anthropic (Claude Code); the maker of IDA MCP is not named in the sources", "deployer": "Wiz Research (Sagi Tzadik, Nir Ohfeld, Ronen Shustin, Hillai Ben-Sasson, Yuval Avrahami, Noam Malron)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI's documented contribution is speed of analysis, on the researchers' own account. Wiz writes that AI-augmented reverse-engineering tooling, particularly IDA MCP, let it rapidly analyze GitHub's compiled binaries and reconstruct internal protocols. Wiz says that speed would not have been feasible manually. Sagi Tzadik told Dark Reading that with AI tools it took under 48 hours from idea to working exploit. The Register adds that the team used Claude Code “to take a lot of the legwork out of the process”. The hypothesis, the exploit, the report and the fix were human work. Six named Wiz researchers confirmed RCE on GHES 3.19.1 and wrote what GitHub called a thorough report. GitHub's security and engineering teams reproduced the bug within 40 minutes and deployed the fix. GitHub's own post, the CVE record and the GHES release notes describe a Bug Bounty report without mentioning AI. The AI-assistance claim therefore rests on Wiz and on outlets quoting Wiz. Wiz's write-up, in the text as read, names IDA MCP and not Claude." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Wiz researchers used AI tools to analyze GitHub’s compiled software and reconstruct its internal protocols. They reported that the tools accelerated the work and that they produced a working exploit in under 48 hours.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The AI's documented contribution is speed of analysis, on the researchers' own account. Wiz writes that AI-augmented reverse-engineering tooling, particularly IDA MCP, let it rapidly analyze GitHub's compiled binaries and reconstruct internal protocols. Wiz says that speed would not have been feasible manually. Sagi Tzadik told Dark Reading that with AI tools it took under 48 hours from idea to working exploit. The Register adds that the team used Claude Code “to take a lot of the legwork out of the process”. The hypothesis, the exploit, the report and the fix were human work. Six named Wiz researchers confirmed RCE on GHES 3.19.1 and wrote what GitHub called a thorough report. GitHub's security and engineering teams reproduced the bug within 40 minutes and deployed the fix. GitHub's own post, the CVE record and the GHES release notes describe a Bug Bounty report without mentioning AI. The AI-assistance claim therefore rests on Wiz and on outlets quoting Wiz. Wiz's write-up, in the text as read, names IDA MCP and not Claude." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "GitHub closed a critical remote code execution hole in its git push pipeline within hours of the report. The fix reached github.com at 7:00 pm UTC on 4 March 2026. GitHub also shipped patches for six GitHub Enterprise Server release lines. Wiz earned what GitHub calls one of the highest rewards in the history of its Bug Bounty program. The loss avoided is hypothetical. GitHub's forensic investigation found that no other account had triggered the code path. No customer data was accessed, modified or exfiltrated. The benefit is an exposure closed rather than a demonstrated loss prevented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports, the makers' press release, the deployer's press release, the beneficiary's report and two other sources from the beneficiary. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "GitHub closed a critical remote code execution hole in its git push pipeline within hours of the report. The fix reached github.com at 7:00 pm UTC on 4 March 2026. GitHub also shipped patches for six GitHub Enterprise Server release lines. Wiz earned what GitHub calls one of the highest rewards in the history of its Bug Bounty program. The loss avoided is hypothetical. GitHub's forensic investigation found that no other account had triggered the code path. No customer data was accessed, modified or exfiltrated. The benefit is an exposure closed rather than a demonstrated loss prevented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GitHub closed the vulnerability within hours and patched six Enterprise Server release lines. Its investigation found that no other account had triggered the affected code path and that no customer data had been accessed, changed, or removed. The benefit is a corrected exposure, with no measured loss avoided." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "GitHub closed a critical remote code execution hole in its git push pipeline within hours of the report. The fix reached github.com at 7:00 pm UTC on 4 March 2026. GitHub also shipped patches for six GitHub Enterprise Server release lines. Wiz earned what GitHub calls one of the highest rewards in the history of its Bug Bounty program. The loss avoided is hypothetical. GitHub's forensic investigation found that no other account had triggered the code path. No customer data was accessed, modified or exfiltrated. The benefit is an exposure closed rather than a demonstrated loss prevented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Wiz reports less than 48 hours from the initial idea to a working exploit. Its comparison with months of manual analysis is an estimate, without a measured control." } }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Time from the initial idea to a working exploit, time to verify the report, and time to deploy a fix." } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The account of AI assistance comes from Wiz and interviews with its researchers. Wiz names IDA MCP; The Register and WebProNews name Claude Code. GitHub independently confirmed and fixed the vulnerability but did not describe AI’s contribution.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-04-28-f5d8" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-04-28", "note": "GitHub's 28 April 2026 disclosure confirmed the same-day fix, patches for all supported GitHub Enterprise Server releases, and a forensic investigation that found no exploitation beyond the researchers' own testing.", "quote": "In less than two hours we had validated the finding, deployed a fix to github.com, and begun a forensic investigation that concluded there was no exploitation.", "url": "https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GitHub fixed github.com on 4 March 2026 and released Enterprise Server patches from 10 March. Public disclosure followed on 28 April, with an investigation that found no exploitation beyond the researchers’ testing." } }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Wiz researchers developed and tested the exploit. GitHub’s security and engineering teams reproduced the vulnerability and deployed the fix." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0206" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "GitHub and organizations using GitHub Enterprise Server." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GitHub and organizations using GitHub Enterprise Server." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 4 March 2026 Wiz Research reported a critical remote code execution (RCE) flaw to GitHub, later CVE-2026–3854. It sat in GitHub's internal git infrastructure. Git push options were not sanitized before being written into internal headers. Any authenticated user could therefore run arbitrary commands on GitHub's servers with a single git push. Wiz says AI-augmented tooling made the research feasible, particularly automated reverse engineering of GitHub's compiled binaries with IDA MCP. The Register reports that the team used Claude Code and went from idea to working exploit in under 48 hours. GitHub reproduced the bug within 40 minutes and deployed a fix to github.com at 7:00 pm UTC that day. It released GitHub Enterprise Server (GHES) patches from 10 March, and found no exploitation.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GitHub fixed a vulnerability that allowed remote code execution after Wiz researchers reported it." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "GitHub closed a critical remote code execution hole in its git push pipeline within hours of the report. The fix reached github.com at 7:00 pm UTC on 4 March 2026. GitHub also shipped patches for six GitHub Enterprise Server release lines. Wiz earned what GitHub calls one of the highest rewards in the history of its Bug Bounty program. The loss avoided is hypothetical. GitHub's forensic investigation found that no other account had triggered the code path. No customer data was accessed, modified or exfiltrated. The benefit is an exposure closed rather than a demonstrated loss prevented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Claude Code is Anthropic's agentic coding tool, built to read a codebase, edit files, run commands and connect to external tools over MCP. Wiz used it, with IDA MCP, to automate reverse engineering of GitHub's compiled binaries. Analyzing code for flaws is within that general purpose. The benefit was an application of what the tool was built to do rather than a departure from it." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Claude Code is Anthropic's agentic coding tool, built to read a codebase, edit files, run commands and connect to external tools over MCP. Wiz used it, with IDA MCP, to automate reverse engineering of GitHub's compiled binaries. Analyzing code for flaws is within that general purpose. The benefit was an application of what the tool was built to do rather than a departure from it." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-03-04", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 4 March 2026, the day Wiz reported the flaw and GitHub fixed github.com (GitHub's post: root cause identified at 5:45 pm UTC, fix deployed at 7:00 pm UTC; Wiz and The Verge say within six hours of the report; GitHub's standfirst says under two hours); the Monitor's 28 April 2026 is the public-disclosure date. Filed as benefit created or restored: the AI-assisted discovery produced a concrete fix for an identifiable organization, and the harm avoided is hypothetical because GitHub's forensic investigation found no exploitation. The AI tool is named differently by the sources: The Register (byline Connor Jones on the page as first read) and WebProNews say Claude Code, and WebProNews names Anthropic; Wiz's own write-up credits “AI-augmented tooling, particularly automated reverse engineering using IDA MCP”, and a search of its text as read found no occurrence of “Claude” or “Anthropic”, so the record names Claude Code on The Register's authority and IDA MCP on Wiz's. No Claude model is named anywhere. Severity: the CVE record (CNA GitHub) gives CVSS 4.0 8.7; The Register reports an 8.8 rating from NIST. GHES patch dates: Wiz's timeline says 10 March 2026, the 3.14.25 release notes are dated 12 March 2026. The Wiz post is marked as written by the makers because it is the discoverer's own account of its tooling, although Wiz is not the AI vendor; GitHub's post is the beneficiary's statement and is not flagged. Anthropic's Claude Code overview is cited only for what the product is. The Monitor lists The Register's headline as “Wiz hands GitHub AI-aided bug report that isn't total slop”; the page as read carries the longer headline used in the citation. The Verge and Dark Reading refused direct fetches and were read through the r.jina.ai text proxy; NVD's page did not render; WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged, so the CVE record's reference list was used to locate the Wiz and GitHub posts.", "source": "https://oecd.ai/en/incidents/2026-04-28-f5d8", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854; https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Wiz_(company)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A closed-source target that researchers could obtain and disassemble. GitHub's backend git services also ship as GitHub Enterprise Server binaries. Those binaries gave automated reverse engineering something to work on, and GHES 3.19.1 gave a place to confirm the exploit.", "AI-augmented tooling for reverse engineering: IDA MCP (per Wiz) driven by Claude Code (per The Register). It reconstructed internal protocols and traced where user input could influence server behavior across the pipeline.", "Experienced vulnerability researchers: six named Wiz authors formed the hypothesis and built and confirmed a working exploit. They wrote what GitHub called a thorough report that clearly demonstrated the impact.", "A beneficiary with a Bug Bounty intake and an on-call security team. GitHub reproduced the bug within 40 minutes of the report. It deployed a fix to github.com at 7:00 pm UTC the same day." ], "must_hold": [ "AI output has to be confirmed by exploit, not trusted. Wiz confirmed RCE on a GHES 3.19.1 instance before reporting. That is why GitHub could validate the report in 40 minutes.", "The receiving organization must be able to patch its own service and ship fixes to self-hosted customers. GHES customers stayed exposed until they upgraded to the patched versions released from 10 March 2026.", "Disclosure must wait for the patches, here from 4 March to 28 April 2026. No exploitation had occurred, so the benefit is an exposure closed, not a loss recovered." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Researchers need access to the compiled software and an environment for testing the suspected flaw. They must establish that the exploit works, report it privately, and allow time for patches. Self-hosted customers need to install the patched release." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 4 March 2026, the day Wiz reported the flaw and GitHub fixed github.com (GitHub's post: root cause identified at 5:45 pm UTC, fix deployed at 7:00 pm UTC; Wiz and The Verge say within six hours of the report; GitHub's standfirst says under two hours); the Monitor's 28 April 2026 is the public-disclosure date. Filed as benefit created or restored: the AI-assisted discovery produced a concrete fix for an identifiable organization, and the harm avoided is hypothetical because GitHub's forensic investigation found no exploitation. The AI tool is named differently by the sources: The Register (byline Connor Jones on the page as first read) and WebProNews say Claude Code, and WebProNews names Anthropic; Wiz's own write-up credits “AI-augmented tooling, particularly automated reverse engineering using IDA MCP”, and a search of its text as read found no occurrence of “Claude” or “Anthropic”, so the record names Claude Code on The Register's authority and IDA MCP on Wiz's. No Claude model is named anywhere. Severity: the CVE record (CNA GitHub) gives CVSS 4.0 8.7; The Register reports an 8.8 rating from NIST. GHES patch dates: Wiz's timeline says 10 March 2026, the 3.14.25 release notes are dated 12 March 2026. The Wiz post is marked as written by the makers because it is the discoverer's own account of its tooling, although Wiz is not the AI vendor; GitHub's post is the beneficiary's statement and is not flagged. Anthropic's Claude Code overview is cited only for what the product is. The Monitor lists The Register's headline as “Wiz hands GitHub AI-aided bug report that isn't total slop”; the page as read carries the longer headline used in the citation. The Verge and Dark Reading refused direct fetches and were read through the r.jina.ai text proxy; NVD's page did not render; WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged, so the CVE record's reference list was used to locate the Wiz and GitHub posts.", "source": "https://oecd.ai/en/incidents/2026-04-28-f5d8", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854; https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Wiz_(company)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Cybersecurity: vulnerability research and software supply chain", "segment": "Bug bounty research: AI-assisted reverse engineering of closed-source server binaries" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Wales A / GitHub (2026). Securing the git push pipeline: Responding to a critical remote code execution vulnerability. The GitHub Blog, 28 April 2026 (updated 29 April 2026).", "title": "Securing the git push pipeline: Responding to a critical remote code execution vulnerability", "url": "https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "date": "2026-04-28", "written_by": "beneficiary", "quote": "our engineering team developed and deployed a fix to github.com at 7:00 p.m. UTC that same day" }, { "kind": "other", "citation": "GitHub (CNA) (2026). CVE-2026-3854. CVE Program record, published 10 March 2026.", "title": "CVE-2026-3854", "url": "https://cveawg.mitre.org/api/cve/CVE-2026-3854", "date": "2026-03-10", "written_by": "beneficiary", "quote": "allowed an attacker with push access to a repository to achieve remote code execution on the instance" }, { "kind": "other", "citation": "GitHub (2026). GitHub Enterprise Server 3.14 release notes, 3.14.25, 12 March 2026. GitHub Docs.", "title": "GitHub Enterprise Server 3.14 release notes (3.14.25)", "url": "https://docs.github.com/en/enterprise-server@3.14/admin/release-notes", "date": "2026-03-12", "written_by": "beneficiary", "quote": "GitHub has requested CVE ID CVE-2026-3854 for this vulnerability, which was reported via the GitHub Bug Bounty program." }, { "kind": "press release", "citation": "Tzadik S, Ohfeld N, Shustin R, Ben-Sasson H, Avrahami Y, Malron N / Wiz Research (2026). Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854). Wiz Blog, 28 April 2026.", "title": "Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854)", "url": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854", "date": "2026-04-28", "written_by": "deployer", "quote": "This research was made possible by AI-augmented reverse engineering tooling, particularly IDA MCP, which allowed us to rapidly analyze GitHub's compiled binaries" }, { "kind": "news report", "citation": "Jones C / The Register (2026). GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash. The Register, 29 April 2026.", "title": "GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash", "url": "https://www.theregister.com/2026/04/29/github_woah_a_genuinely_helpful/", "date": "2026-04-29", "written_by": "independent", "quote": "They used Claude Code to take a lot of the legwork out of the process" }, { "kind": "news report", "citation": "Warren T / The Verge (2026). GitHub rushed to fix a critical vulnerability in less than six hours. The Verge, 29 April 2026.", "title": "GitHub rushed to fix a critical vulnerability in less than six hours", "url": "https://www.theverge.com/news/920295/github-remote-code-execution-vulnerability-fix", "date": "2026-04-29", "written_by": "independent", "quote": "A critical remote code execution vulnerability was discovered using an AI model and patched within hours." }, { "kind": "news report", "citation": "Culafi A / Dark Reading (2026). Reverse Engineering With AI Unearths High-Severity GitHub Bug. Dark Reading, 29 April 2026.", "title": "Reverse Engineering With AI Unearths High-Severity GitHub Bug", "url": "https://www.darkreading.com/application-security/reverse-engineering-ai-unearths-high-severity-github-bug", "date": "2026-04-29", "written_by": "independent", "quote": "With the help of AI tools, it took less than 48 hours to go from idea to a working exploit" }, { "kind": "news report", "citation": "Ritchie D / WebProNews (2026). Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty. WebProNews, 30 April 2026.", "title": "Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty", "url": "https://www.webpronews.com/wizs-ai-powered-strike-how-claude-cracked-githubs-git-fortress-for-a-fat-bounty/", "date": "2026-04-30", "written_by": "independent", "quote": "They leaned on Claude Code, Anthropic's agentic coding tool, for the heavy lifting." }, { "kind": "press release", "citation": "Anthropic (2026). Claude Code overview. Claude Code documentation.", "title": "Overview", "url": "https://code.claude.com/docs/en/overview", "written_by": "makers", "quote": "Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0206-source-0001", "AIBD-0206-source-0002", "AIBD-0206-source-0003", "AIBD-0206-source-0004", "AIBD-0206-source-0005", "AIBD-0206-source-0006", "AIBD-0206-source-0007", "AIBD-0206-source-0008", "AIBD-0206-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Wales A / GitHub (2026). Securing the git push pipeline: Responding to a critical remote code execution vulnerability. The GitHub Blog, 28 April 2026 (updated 29 April 2026).", "title": "Securing the git push pipeline: Responding to a critical remote code execution vulnerability", "url": "https://github.blog/security/securing-the-git-push-pipeline-responding-to-a-critical-remote-code-execution-vulnerability/", "date": "2026-04-28", "written_by": "beneficiary", "quote": "our engineering team developed and deployed a fix to github.com at 7:00 p.m. UTC that same day" }, { "kind": "other", "citation": "GitHub (CNA) (2026). CVE-2026-3854. CVE Program record, published 10 March 2026.", "title": "CVE-2026-3854", "url": "https://cveawg.mitre.org/api/cve/CVE-2026-3854", "date": "2026-03-10", "written_by": "beneficiary", "quote": "allowed an attacker with push access to a repository to achieve remote code execution on the instance" }, { "kind": "other", "citation": "GitHub (2026). GitHub Enterprise Server 3.14 release notes, 3.14.25, 12 March 2026. GitHub Docs.", "title": "GitHub Enterprise Server 3.14 release notes (3.14.25)", "url": "https://docs.github.com/en/enterprise-server@3.14/admin/release-notes", "date": "2026-03-12", "written_by": "beneficiary", "quote": "GitHub has requested CVE ID CVE-2026-3854 for this vulnerability, which was reported via the GitHub Bug Bounty program." }, { "kind": "press release", "citation": "Tzadik S, Ohfeld N, Shustin R, Ben-Sasson H, Avrahami Y, Malron N / Wiz Research (2026). Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854). Wiz Blog, 28 April 2026.", "title": "Securing GitHub: Wiz Research uncovers Remote Code Execution in GitHub.com and GitHub Enterprise Server (CVE-2026-3854)", "url": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854", "date": "2026-04-28", "written_by": "deployer", "quote": "This research was made possible by AI-augmented reverse engineering tooling, particularly IDA MCP, which allowed us to rapidly analyze GitHub's compiled binaries" }, { "kind": "news report", "citation": "Jones C / The Register (2026). GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash. The Register, 29 April 2026.", "title": "GitHub: Zounds, a genuinely helpful AI-assisted bug report that isn't total slop! Here, Wiz, take this wad of cash", "url": "https://www.theregister.com/2026/04/29/github_woah_a_genuinely_helpful/", "date": "2026-04-29", "written_by": "independent", "quote": "They used Claude Code to take a lot of the legwork out of the process" }, { "kind": "news report", "citation": "Warren T / The Verge (2026). GitHub rushed to fix a critical vulnerability in less than six hours. The Verge, 29 April 2026.", "title": "GitHub rushed to fix a critical vulnerability in less than six hours", "url": "https://www.theverge.com/news/920295/github-remote-code-execution-vulnerability-fix", "date": "2026-04-29", "written_by": "independent", "quote": "A critical remote code execution vulnerability was discovered using an AI model and patched within hours." }, { "kind": "news report", "citation": "Culafi A / Dark Reading (2026). Reverse Engineering With AI Unearths High-Severity GitHub Bug. Dark Reading, 29 April 2026.", "title": "Reverse Engineering With AI Unearths High-Severity GitHub Bug", "url": "https://www.darkreading.com/application-security/reverse-engineering-ai-unearths-high-severity-github-bug", "date": "2026-04-29", "written_by": "independent", "quote": "With the help of AI tools, it took less than 48 hours to go from idea to a working exploit" }, { "kind": "news report", "citation": "Ritchie D / WebProNews (2026). Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty. WebProNews, 30 April 2026.", "title": "Wiz's AI-Powered Strike: How Claude Cracked GitHub's Git Fortress for a Fat Bounty", "url": "https://www.webpronews.com/wizs-ai-powered-strike-how-claude-cracked-githubs-git-fortress-for-a-fat-bounty/", "date": "2026-04-30", "written_by": "independent", "quote": "They leaned on Claude Code, Anthropic's agentic coding tool, for the heavy lifting." }, { "kind": "press release", "citation": "Anthropic (2026). Claude Code overview. Claude Code documentation.", "title": "Overview", "url": "https://code.claude.com/docs/en/overview", "written_by": "makers", "quote": "Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Wiz's AI-aided reverse engineering found a critical GitHub RCE (CVE-2026–3854); github.com was patched the same day, no exploitation found.", "account": "On 4 March 2026 Wiz Research reported a critical remote code execution (RCE) flaw to GitHub, later CVE-2026–3854. It sat in GitHub's internal git infrastructure. Git push options were not sanitized before being written into internal headers. Any authenticated user could therefore run arbitrary commands on GitHub's servers with a single git push. Wiz says AI-augmented tooling made the research feasible, particularly automated reverse engineering of GitHub's compiled binaries with IDA MCP. The Register reports that the team used Claude Code and went from idea to working exploit in under 48 hours. GitHub reproduced the bug within 40 minutes and deployed a fix to github.com at 7:00 pm UTC that day. It released GitHub Enterprise Server (GHES) patches from 10 March, and found no exploitation." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The tools analyzed compiled binaries and helped researchers reconstruct internal protocols." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "Wiz used IDA MCP to analyze GitHub’s compiled binaries. The Register identified Claude Code as the tool through which researchers performed some of that work." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Wiz used IDA MCP to analyze GitHub’s compiled binaries. The Register identified Claude Code as the tool through which researchers performed some of that work.", "editor_note": "Wiz’s original report names IDA MCP but not Claude Code. The latter identification comes from The Register’s reporting (references 4 and 5)." } }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "The tools analyzed compiled binaries and helped researchers reconstruct internal protocols." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "system", "ai_role", "replication", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Not named in the cited sources. Wiz's write-up credits 'AI-augmented tooling, particularly automated reverse engineering using IDA MCP' and does not name Claude. The Register and WebProNews name Claude Code." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI-augmented reverse-engineering tooling used by Wiz Research: Claude Code (named by The Register and WebProNews) with IDA MCP for automated reverse engineering of GitHub's compiled binaries (named by Wiz), applied to GitHub.com's backend and GitHub Enterprise Server 3.19.1" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Anthropic (Claude Code); the maker of IDA MCP is not named in the sources" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Wiz Research's AI-augmented reverse engineering of GitHub's closed-source git binaries, with Claude Code and IDA MCP, finds a critical push-option injection giving remote code execution on GitHub.com and GitHub Enterprise Server; GitHub reproduces it in 40 minutes, patches github.com by 7:00 pm UTC on 4 March 2026 and finds no exploitation", "headline": "Wiz finds a critical GitHub RCE with Claude Code and IDA MCP" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7f34bca5fb89430f7594439f89d31ee3a5bdf590dac0a26ae25031315a985960" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 4 March 2026 Wiz Research reported a critical remote code execution (RCE) flaw to GitHub, later CVE-2026–3854. It sat in GitHub's internal git infrastructure. Git push options were not sanitized before being written into internal headers. Any authenticated user could therefore run arbitrary commands on GitHub's servers with a single git push. Wiz says AI-augmented tooling made the research feasible, particularly automated reverse engineering of GitHub's compiled binaries with IDA MCP. The Register reports that the team used Claude Code and went from idea to working exploit in under 48 hours. GitHub reproduced the bug within 40 minutes and deployed a fix to github.com at 7:00 pm UTC that day. It released GitHub Enterprise Server (GHES) patches from 10 March, and found no exploitation.", "replication": { "produced_by": [ "A closed-source target that researchers could obtain and disassemble. GitHub's backend git services also ship as GitHub Enterprise Server binaries. Those binaries gave automated reverse engineering something to work on, and GHES 3.19.1 gave a place to confirm the exploit.", "AI-augmented tooling for reverse engineering: IDA MCP (per Wiz) driven by Claude Code (per The Register). It reconstructed internal protocols and traced where user input could influence server behavior across the pipeline.", "Experienced vulnerability researchers: six named Wiz authors formed the hypothesis and built and confirmed a working exploit. They wrote what GitHub called a thorough report that clearly demonstrated the impact.", "A beneficiary with a Bug Bounty intake and an on-call security team. GitHub reproduced the bug within 40 minutes of the report. It deployed a fix to github.com at 7:00 pm UTC the same day." ], "must_hold": [ "AI output has to be confirmed by exploit, not trusted. Wiz confirmed RCE on a GHES 3.19.1 instance before reporting. That is why GitHub could validate the report in 40 minutes.", "The receiving organization must be able to patch its own service and ship fixes to self-hosted customers. GHES customers stayed exposed until they upgraded to the patched versions released from 10 March 2026.", "Disclosure must wait for the patches, here from 4 March to 28 April 2026. No exploitation had occurred, so the benefit is an exposure closed, not a loss recovered." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Wiz researchers obtained GitHub’s compiled software that also shipped with Enterprise Server. They used AI tools to examine it and developed an exploit that they confirmed on version 3.19.1. Their report showed that remote code execution was possible. GitHub reproduced the issue within 40 minutes and fixed github.com that evening." } } }, "summary": { "total": 91, "addressed": 91, "known": 35, "reviewed": 0, "record_mapped": 35, "not_assessed": 56, "complete": false, "by_state": { "value_present": 35, "not_reported": 0, "unresolved": 0, "not_assessed": 56, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0160", "case_url": "records/AIBD-0160.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Istanbul police built AVCI to infiltrate and analyze encrypted messaging groups used for drug dealing. It also identifies suspects, fake profiles and network hierarchies. The 10 February operation was that intended use." }, { "label": "AI contribution", "value": "By the police account relayed by Anadolu Agency, AVCI infiltrated closed messaging groups and decoded slang and coded language. It recovered deleted messages, linked fake profiles through IP analysis and mapped group hierarchies. That work produced the suspects and groups targeted on 10 February. Police and courts carried out the detentions, referrals and remands. The sources include no independent evaluation of the system. No official is quoted on how many of the 325 were identified by AVCI rather than by conventional work. The sources name no developer beyond the police's own software team." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0160", "title": "Istanbul police's AVCI messaging-analysis system used for the first time in a 10 February 2026 drug operation across 14 provinces: 325 suspects detained and remanded, 69 dealer groups closed", "summary": "Istanbul police's AI-based AVCI system, first used 10 February 2026 in a 14-province drug operation: 325 detained and remanded, 69 groups closed.", "date_observed": "2026-02-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi, Open Source Intelligence Analysis System) is an AI-supported program. The Istanbul Police Department's narcotics and cybercrime divisions built it in-house with a special software team. Police chief Selami Yıldız ordered the work. It infiltrates closed groups and channels on end-to-end encrypted messaging apps. It analyzes messages, voice notes, photos and location data. It decodes dealer slang, recovers deleted messages, links fake profiles and maps group hierarchies. Anadolu Agency reported on 6 April 2026 that AVCI was first used on 10 February 2026. The drug operation was centered on Istanbul and ran across 14 provinces. 325 suspects were detained. All 325 referred to court were remanded in custody. 69 messaging-app groups were closed.", "category": "benefit created", "unexpected": false, "purpose": "Istanbul police built AVCI to infiltrate and analyze encrypted messaging groups used for drug dealing. It also identifies suspects, fake profiles and network hierarchies. The 10 February operation was that intended use.", "system": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi): AI-supported open-source intelligence analysis software that infiltrates closed groups on encrypted messaging apps and analyzes messages, media and location data with natural language processing", "model_version": null, "developer": "Istanbul Police Department (İstanbul Emniyet Müdürlüğü), narcotics and cybercrime divisions with an in-house software team", "deployer": "Istanbul Police Department, Anti-Narcotics and Cybercrime divisions", "location": "Istanbul, Turkey", "domain": "Law enforcement / narcotics investigation", "segment": "Provincial police force", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-04-06-4878" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "In one operation the force detained 325 suspects across 14 provinces and closed 69 dealer groups. All 325 were remanded. The sources give no comparison with operations before AVCI, so the gain to the force is not measured." }, { "group": "societal", "depth": 2, "reason": "The police say the operation disrupted drug sales networks run through encrypted groups and social media across 14 provinces. No source measures any effect on drug supply or on the public. A population-level benefit is not established." } ], "ai_role": { "status": "assessed", "statement": "By the police account relayed by Anadolu Agency, AVCI infiltrated closed messaging groups and decoded slang and coded language. It recovered deleted messages, linked fake profiles through IP analysis and mapped group hierarchies. That work produced the suspects and groups targeted on 10 February. Police and courts carried out the detentions, referrals and remands. The sources include no independent evaluation of the system. No official is quoted on how many of the 325 were identified by AVCI rather than by conventional work. The sources name no developer beyond the police's own software team." }, "evidence": [ { "kind": "news report", "citation": "Anadolu Ajansı (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. aa.com.tr, 6 April 2026.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439", "date": "2026-04-06", "written_by": "independent", "quote": "AVCI ilk kez, İstanbul Narkotik Suçlarla Mücadele ve Siber Suçlarla Mücadele Şube müdürlükleri ekiplerince 10 Şubat'ta kent merkezli 14 ilde düzenlenen uyuşturucu operasyonunda kullanıldı." }, { "kind": "news report", "citation": "Haberler.com (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. haberler.com, 6 April 2026, crediting Anadolu Ajansı.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.haberler.com/guncel/istanbul-emniyeti-yapay-zeka-destekli-avci-yla-19720800-haberi/", "date": "2026-04-06", "written_by": "independent", "quote": "adliyeye sevk edilen 325 zanlının tamamı sevk edildikleri adli makamlarca tutuklanmıştı." }, { "kind": "news report", "citation": "Akşam (2026). Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor. aksam.com.tr, 6 April 2026, crediting Anadolu Ajansı.", "title": "Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor", "url": "https://www.aksam.com.tr/guncel/zehir-tacirleriyle-mucadelede-yeni-donem-avci-goz-actirmiyor/haber-1656417", "date": "2026-04-06", "written_by": "independent", "quote": "Yapay zeka destekli projenin ilk kez kullanıldığı operasyonda 325 şüpheli gözaltına alınmış" }, { "kind": "news report", "citation": "Hürriyet (2026). AVCI uyuşturucunun 'siber' inlerinde. hurriyet.com.tr, 7 April 2026, Anadolu Ajansı byline.", "title": "AVCI uyuşturucunun 'siber' inlerinde", "url": "https://www.hurriyet.com.tr/gundem/avci-uyusturucunun-siber-inlerinde-43145237", "date": "2026-04-07", "written_by": "independent", "quote": "İstanbul Emniyet Müdürlüğü tarafından hazırlanan yapay zekâ destekli Açık Kaynak İstihbaratı Analiz Sistemi (AVCI), uçtan uca şifreli iletişim uygulamalarının kapalı grup ve kanallarına sızarak, uyuşturucu tacirlerine göz açtırmıyor." }, { "kind": "news report", "citation": "Gökmen, E. (2026). İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı. Anadolu Agency, 17 July 2026.", "title": "İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı", "url": "https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "date": "2026-07-17", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "In-house police software that infiltrates closed groups on encrypted messaging apps and analyzes messages, media and location data with natural language processing", "A coordinated same-day operation across 14 provinces in which the identified suspects were detained and referred to court" ], "must_hold": [ "Police have access to the closed groups and to the server-side data the sources say the system uses to recover deleted messages and link profiles", "Courts remand the suspects. The sources report all 325 remanded, but no later charging or conviction outcomes", "The attribution of the suspects to AVCI, rather than to conventional investigation, is the police's own. No independent evaluation exists" ], "echoes": [] }, "paired_incident": null, "headline": "Istanbul police's AVCI detains 325 and closes 69 dealer groups", "metric": "325 detained and remanded, 69 groups closed", "comparator": { "against": "manual analysis of messaging data by police staff", "before": "insan gücüyle aylar sürecek bir analiz", "after": "saniyeler içine sığdırılarak", "basis": "assumed", "quote": "Programla suç gerçekleşmeden önce istihbarat odaklı yapılan çalışmayla insan gücüyle aylar sürecek bir analiz saniyeler içine sığdırılarak, uyuşturucu baronlarının dijital maskeleri düşürülüyor.", "url": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439" }, "later": { "status": "persisted", "date": "2026-07-17", "note": "AVCI was used again in an Istanbul-based operation across 35 provinces in which 110 suspects were detained and 65 Telegram groups and channels were closed.", "quote": "İstanbul merkezli 35 ilde uyuşturucu satıcılarına yönelik düzenlenen ve 110 zanlının yakalandığı operasyonda, yapay zeka destekli Açık Kaynak İstihbaratı Analiz Sistemi (AVCI) kullanıldı.", "url": "https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945" }, "value": null, "developer_country": "Turkey", "deployer_country": "Turkey", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-06-4878", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439; https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü; https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0160", "catalog_version": "1.0.0", "source_fingerprint": "10a892784ce22659e8b0daf253441c77114008e569616d29537281bd52d9d8c1", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0160-source-0001", "AIBD-0160-source-0002", "AIBD-0160-source-0003", "AIBD-0160-source-0004", "AIBD-0160-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Istanbul Police Department (İstanbul Emniyet Müdürlüğü), narcotics and cybercrime divisions with an in-house software team", "deployer": "Istanbul Police Department, Anti-Narcotics and Cybercrime divisions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "By the police account relayed by Anadolu Agency, AVCI infiltrated closed messaging groups and decoded slang and coded language. It recovered deleted messages, linked fake profiles through IP analysis and mapped group hierarchies. That work produced the suspects and groups targeted on 10 February. Police and courts carried out the detentions, referrals and remands. The sources include no independent evaluation of the system. No official is quoted on how many of the 325 were identified by AVCI rather than by conventional work. The sources name no developer beyond the police's own software team." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "By the police account relayed by Anadolu Agency, AVCI infiltrated closed messaging groups and decoded slang and coded language. It recovered deleted messages, linked fake profiles through IP analysis and mapped group hierarchies. That work produced the suspects and groups targeted on 10 February. Police and courts carried out the detentions, referrals and remands. The sources include no independent evaluation of the system. No official is quoted on how many of the 325 were identified by AVCI rather than by conventional work. The sources name no developer beyond the police's own software team." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "In one operation the force detained 325 suspects across 14 provinces and closed 69 dealer groups. All 325 were remanded. The sources give no comparison with operations before AVCI, so the gain to the force is not measured." }, { "group": "societal", "depth": 2, "reason": "The police say the operation disrupted drug sales networks run through encrypted groups and social media across 14 provinces. No source measures any effect on drug supply or on the public. A population-level benefit is not established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "In one operation the force detained 325 suspects across 14 provinces and closed 69 dealer groups. All 325 were remanded. The sources give no comparison with operations before AVCI, so the gain to the force is not measured." }, { "group": "societal", "depth": 2, "reason": "The police say the operation disrupted drug sales networks run through encrypted groups and social media across 14 provinces. No source measures any effect on drug supply or on the public. A population-level benefit is not established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "In one operation the force detained 325 suspects across 14 provinces and closed 69 dealer groups. All 325 were remanded. The sources give no comparison with operations before AVCI, so the gain to the force is not measured." }, { "group": "societal", "depth": 2, "reason": "The police say the operation disrupted drug sales networks run through encrypted groups and social media across 14 provinces. No source measures any effect on drug supply or on the public. A population-level benefit is not established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "325 detained and remanded, 69 groups closed", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-04-06-4878" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-07-17", "note": "AVCI was used again in an Istanbul-based operation across 35 provinces in which 110 suspects were detained and 65 Telegram groups and channels were closed.", "quote": "İstanbul merkezli 35 ilde uyuşturucu satıcılarına yönelik düzenlenen ve 110 zanlının yakalandığı operasyonda, yapay zeka destekli Açık Kaynak İstihbaratı Analiz Sistemi (AVCI) kullanıldı.", "url": "https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0160" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi, Open Source Intelligence Analysis System) is an AI-supported program. The Istanbul Police Department's narcotics and cybercrime divisions built it in-house with a special software team. Police chief Selami Yıldız ordered the work. It infiltrates closed groups and channels on end-to-end encrypted messaging apps. It analyzes messages, voice notes, photos and location data. It decodes dealer slang, recovers deleted messages, links fake profiles and maps group hierarchies. Anadolu Agency reported on 6 April 2026 that AVCI was first used on 10 February 2026. The drug operation was centered on Istanbul and ran across 14 provinces. 325 suspects were detained. All 325 referred to court were remanded in custody. 69 messaging-app groups were closed.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "In one operation the force detained 325 suspects across 14 provinces and closed 69 dealer groups. All 325 were remanded. The sources give no comparison with operations before AVCI, so the gain to the force is not measured." }, { "group": "societal", "depth": 2, "reason": "The police say the operation disrupted drug sales networks run through encrypted groups and social media across 14 provinces. No source measures any effect on drug supply or on the public. A population-level benefit is not established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Istanbul police built AVCI to infiltrate and analyze encrypted messaging groups used for drug dealing. It also identifies suspects, fake profiles and network hierarchies. The 10 February operation was that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Istanbul, Turkey", "developer_country": "Turkey", "deployer_country": "Turkey" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Istanbul police built AVCI to infiltrate and analyze encrypted messaging groups used for drug dealing. It also identifies suspects, fake profiles and network hierarchies. The 10 February operation was that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-02-10", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-06-4878", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439; https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü; https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "In-house police software that infiltrates closed groups on encrypted messaging apps and analyzes messages, media and location data with natural language processing", "A coordinated same-day operation across 14 provinces in which the identified suspects were detained and referred to court" ], "must_hold": [ "Police have access to the closed groups and to the server-side data the sources say the system uses to recover deleted messages and link profiles", "Courts remand the suspects. The sources report all 325 remanded, but no later charging or conviction outcomes", "The attribution of the suspects to AVCI, rather than to conventional investigation, is the police's own. No independent evaluation exists" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-06-4878", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439; https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü; https://tr.wikipedia.org/wiki/İstanbul_Emniyet_Müdürlüğü", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Law enforcement / narcotics investigation", "segment": "Provincial police force" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Anadolu Ajansı (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. aa.com.tr, 6 April 2026.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439", "date": "2026-04-06", "written_by": "independent", "quote": "AVCI ilk kez, İstanbul Narkotik Suçlarla Mücadele ve Siber Suçlarla Mücadele Şube müdürlükleri ekiplerince 10 Şubat'ta kent merkezli 14 ilde düzenlenen uyuşturucu operasyonunda kullanıldı." }, { "kind": "news report", "citation": "Haberler.com (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. haberler.com, 6 April 2026, crediting Anadolu Ajansı.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.haberler.com/guncel/istanbul-emniyeti-yapay-zeka-destekli-avci-yla-19720800-haberi/", "date": "2026-04-06", "written_by": "independent", "quote": "adliyeye sevk edilen 325 zanlının tamamı sevk edildikleri adli makamlarca tutuklanmıştı." }, { "kind": "news report", "citation": "Akşam (2026). Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor. aksam.com.tr, 6 April 2026, crediting Anadolu Ajansı.", "title": "Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor", "url": "https://www.aksam.com.tr/guncel/zehir-tacirleriyle-mucadelede-yeni-donem-avci-goz-actirmiyor/haber-1656417", "date": "2026-04-06", "written_by": "independent", "quote": "Yapay zeka destekli projenin ilk kez kullanıldığı operasyonda 325 şüpheli gözaltına alınmış" }, { "kind": "news report", "citation": "Hürriyet (2026). AVCI uyuşturucunun 'siber' inlerinde. hurriyet.com.tr, 7 April 2026, Anadolu Ajansı byline.", "title": "AVCI uyuşturucunun 'siber' inlerinde", "url": "https://www.hurriyet.com.tr/gundem/avci-uyusturucunun-siber-inlerinde-43145237", "date": "2026-04-07", "written_by": "independent", "quote": "İstanbul Emniyet Müdürlüğü tarafından hazırlanan yapay zekâ destekli Açık Kaynak İstihbaratı Analiz Sistemi (AVCI), uçtan uca şifreli iletişim uygulamalarının kapalı grup ve kanallarına sızarak, uyuşturucu tacirlerine göz açtırmıyor." }, { "kind": "news report", "citation": "Gökmen, E. (2026). İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı. Anadolu Agency, 17 July 2026.", "title": "İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı", "url": "https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "date": "2026-07-17", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0160-source-0001", "AIBD-0160-source-0002", "AIBD-0160-source-0003", "AIBD-0160-source-0004", "AIBD-0160-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Anadolu Ajansı (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. aa.com.tr, 6 April 2026.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.aa.com.tr/tr/gundem/istanbul-emniyeti-yapay-zeka-destekli-avciyla-uyusturucu-saticilarinin-siber-inlerine-giriyor/3894439", "date": "2026-04-06", "written_by": "independent", "quote": "AVCI ilk kez, İstanbul Narkotik Suçlarla Mücadele ve Siber Suçlarla Mücadele Şube müdürlükleri ekiplerince 10 Şubat'ta kent merkezli 14 ilde düzenlenen uyuşturucu operasyonunda kullanıldı." }, { "kind": "news report", "citation": "Haberler.com (2026). İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor. haberler.com, 6 April 2026, crediting Anadolu Ajansı.", "title": "İstanbul Emniyeti yapay zeka destekli AVCI'yla uyuşturucu satıcılarının 'siber' inlerine giriyor", "url": "https://www.haberler.com/guncel/istanbul-emniyeti-yapay-zeka-destekli-avci-yla-19720800-haberi/", "date": "2026-04-06", "written_by": "independent", "quote": "adliyeye sevk edilen 325 zanlının tamamı sevk edildikleri adli makamlarca tutuklanmıştı." }, { "kind": "news report", "citation": "Akşam (2026). Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor. aksam.com.tr, 6 April 2026, crediting Anadolu Ajansı.", "title": "Zehir tacirleriyle mücadelede yeni dönem: AVCI göz açtırmıyor", "url": "https://www.aksam.com.tr/guncel/zehir-tacirleriyle-mucadelede-yeni-donem-avci-goz-actirmiyor/haber-1656417", "date": "2026-04-06", "written_by": "independent", "quote": "Yapay zeka destekli projenin ilk kez kullanıldığı operasyonda 325 şüpheli gözaltına alınmış" }, { "kind": "news report", "citation": "Hürriyet (2026). AVCI uyuşturucunun 'siber' inlerinde. hurriyet.com.tr, 7 April 2026, Anadolu Ajansı byline.", "title": "AVCI uyuşturucunun 'siber' inlerinde", "url": "https://www.hurriyet.com.tr/gundem/avci-uyusturucunun-siber-inlerinde-43145237", "date": "2026-04-07", "written_by": "independent", "quote": "İstanbul Emniyet Müdürlüğü tarafından hazırlanan yapay zekâ destekli Açık Kaynak İstihbaratı Analiz Sistemi (AVCI), uçtan uca şifreli iletişim uygulamalarının kapalı grup ve kanallarına sızarak, uyuşturucu tacirlerine göz açtırmıyor." }, { "kind": "news report", "citation": "Gökmen, E. (2026). İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı. Anadolu Agency, 17 July 2026.", "title": "İstanbul'da uyuşturucu satıcılarına yönelik operasyonda yapay zeka \"AVCI\" aktif rol aldı", "url": "https://www.aa.com.tr/tr/gundem/istanbulda-uyusturucu-saticilarina-yonelik-operasyonda-yapay-zeka-avci-aktif-rol-aldi/4000945", "date": "2026-07-17", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Istanbul police's AI-based AVCI system, first used 10 February 2026 in a 14-province drug operation: 325 detained and remanded, 69 groups closed.", "account": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi, Open Source Intelligence Analysis System) is an AI-supported program. The Istanbul Police Department's narcotics and cybercrime divisions built it in-house with a special software team. Police chief Selami Yıldız ordered the work. It infiltrates closed groups and channels on end-to-end encrypted messaging apps. It analyzes messages, voice notes, photos and location data. It decodes dealer slang, recovers deleted messages, links fake profiles and maps group hierarchies. Anadolu Agency reported on 6 April 2026 that AVCI was first used on 10 February 2026. The drug operation was centered on Istanbul and ran across 14 provinces. 325 suspects were detained. All 325 referred to court were remanded in custody. 69 messaging-app groups were closed." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi): AI-supported open-source intelligence analysis software that infiltrates closed groups on encrypted messaging apps and analyzes messages, media and location data with natural language processing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Istanbul Police Department (İstanbul Emniyet Müdürlüğü), narcotics and cybercrime divisions with an in-house software team" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Istanbul police's AVCI messaging-analysis system used for the first time in a 10 February 2026 drug operation across 14 provinces: 325 suspects detained and remanded, 69 dealer groups closed", "headline": "Istanbul police's AVCI detains 325 and closes 69 dealer groups" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "10a892784ce22659e8b0daf253441c77114008e569616d29537281bd52d9d8c1" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "AVCI (Açık Kaynak İstihbaratı Analiz Sistemi, Open Source Intelligence Analysis System) is an AI-supported program. The Istanbul Police Department's narcotics and cybercrime divisions built it in-house with a special software team. Police chief Selami Yıldız ordered the work. It infiltrates closed groups and channels on end-to-end encrypted messaging apps. It analyzes messages, voice notes, photos and location data. It decodes dealer slang, recovers deleted messages, links fake profiles and maps group hierarchies. Anadolu Agency reported on 6 April 2026 that AVCI was first used on 10 February 2026. The drug operation was centered on Istanbul and ran across 14 provinces. 325 suspects were detained. All 325 referred to court were remanded in custody. 69 messaging-app groups were closed.", "replication": { "produced_by": [ "In-house police software that infiltrates closed groups on encrypted messaging apps and analyzes messages, media and location data with natural language processing", "A coordinated same-day operation across 14 provinces in which the identified suspects were detained and referred to court" ], "must_hold": [ "Police have access to the closed groups and to the server-side data the sources say the system uses to recover deleted messages and link profiles", "Courts remand the suspects. The sources report all 325 remanded, but no later charging or conviction outcomes", "The attribution of the suspects to AVCI, rather than to conventional investigation, is the police's own. No independent evaluation exists" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0185", "case_url": "records/AIBD-0185.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "General-purpose conversational assistant that answers users' questions." }, { "label": "AI contribution", "value": "Both reports attribute the discovery to ChatGPT's response flagging complaints and credential inconsistencies when the victim queried the firm. That came after his own suspicion at refused withdrawals. The exact output is not reproduced. OpenAI had no role beyond providing the consumer product." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0185", "title": "ChatGPT flags red flags on a fake pre-IPO adviser; 77-year-old in Bengaluru reports Rs 67.10 lakh fraud to police", "summary": "After withdrawals were refused, a 77-year-old Binnypet resident checked the firm's credentials with ChatGPT, which flagged complaints and inconsistencies; he reported the Rs 67.10 lakh fraud to Ben...", "date_observed": "2026-01-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. No police official is named, no recovery is reported and the victim is unnamed.", "category": "protective response", "unexpected": false, "purpose": "General-purpose conversational assistant that answers users' questions.", "system": "ChatGPT", "model_version": null, "developer": "OpenAI", "deployer": "The victim, using the consumer app himself", "location": "Bengaluru, India", "domain": "Investment fraud", "segment": "Pre-IPO share scam", "benefit_type": "Reduced Risk", "taxonomy_note": "The loss had occurred; the good is recognition and reporting of the fraud.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "The victim recognized the fraud and filed a police complaint after ChatGPT's response. Deccan Herald says that this stopped further payments. No money was recovered in the reports read." }, { "group": "societal", "depth": null, "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "depth": null, "reason": "The police registered a case. No organizational outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "Both reports attribute the discovery to ChatGPT's response flagging complaints and credential inconsistencies when the victim queried the firm. That came after his own suspicion at refused withdrawals. The exact output is not reproduced. 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external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0185-source-0001", "AIBD-0185-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The New Indian Express, Bengaluru, 6 January 2026", "title": "ChatGPT alert helps Bengaluru senior citizen uncover Rs 67 lakh pre-IPO investment fraud", "url": "https://www.newindianexpress.com/cities/bengaluru/2026/Jan/06/chatgpt-alert-helps-bengaluru-senior-citizen-uncover-rs-67-lakh-pre-ipo-investment-fraud", "date": "2026-01-06", "written_by": "independent", "quote": "The fraud came to light after the victim checked the firm's credentials using ChatGPT, which flagged multiple warning signs." }, { "kind": "news report", "citation": "Prajwal D'Souza, Deccan Herald, 6 January 2026", "title": "Bengaluru: ChatGPT response saves 77-year-old man from losing more money to cybercriminals", "url": "https://www.deccanherald.com/india/karnataka/bengaluru/bengaluru-chatgpt-response-saves-77-year-old-man-from-losing-more-money-to-cybercriminals-3853142", "date": "2026-01-06", "written_by": "independent", "quote": "The scammers had already cheated him of Rs 67.10 lakh through a fake investment scam." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "After withdrawals were refused, a 77-year-old Binnypet resident checked the firm's credentials with ChatGPT, which flagged complaints and inconsistencies; he reported the Rs 67.10 lakh fraud to Ben...", "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. No police official is named, no recovery is reported and the victim is unnamed." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ChatGPT" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ChatGPT flags red flags on a fake pre-IPO adviser; 77-year-old in Bengaluru reports Rs 67.10 lakh fraud to police", "headline": "ChatGPT flags fake adviser; victim reports Rs 67.10 lakh fraud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f6c0a087d5b24db8a8b779d822e10293d456ab2962ad17de12e5fcfedd1835c7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The New Indian Express (6 January 2026) reported on a 77-year-old resident of Binnypet, Bengaluru. In November 2025 a man calling himself Yatin Shah contacted him. He claimed to be an adviser with IIFL Wealth Management and promised assured allotment of pre-IPO shares. The victim first transferred Rs 5 lakh for a supposed ICICI Prudential AMC pre-IPO and was shown profits in a digital wallet. Over the following weeks he made multiple RTGS transfers totaling Rs 67.10 lakh. When requests to withdraw even part of the funds were denied, he searched for details about the company and its representatives using ChatGPT. It reportedly flagged complaints, inconsistencies in company credentials and indicators associated with investment fraud. He then approached the police, who registered a case under the Information Technology Act. The police said that efforts were under way to trace the money. Deccan Herald (Prajwal D'Souza, 6 January 2026) framed the ChatGPT response as saving him from losing more money. No police official is named, no recovery is reported and the victim is unnamed.", "replication": { "produced_by": [ "A victim querying a large language model about a company and its representatives after withdrawals were refused", "Public complaints and credential inconsistencies about the scheme that the model could surface" ], "must_hold": [ "Suspicion arising before further payments", "Model output that points to specific red flags rather than generic caution", "Police registration for any tracing or recovery" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0154", "case_url": "records/AIBD-0154.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Ho Chi Minh City's street AI cameras are run by the police for security and investigation. VnExpress describes the technology analyzing and matching biometric features. Identifying a suspect from footage is the use the police describe. The system and its vendor are not named." }, { "label": "AI contribution", "value": "The system's contribution was identification. It matched the masked intruder's gait, body shape, clothing, movement and facial data and reconstructed his route. That pointed police to him. The detention, confession and case-building were police work. The system and its vendor are not named. No official is quoted by name. Every report draws on the police account. The OECD Monitor tags the entry under privacy and data governance and respect of human rights, the concerns that come with public facial recognition." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Vietnam" ] }, "record": { "id": "AIBD-0154", "title": "Ho Chi Minh City street AI cameras identify a masked burglar by gait, build, clothing and face; Tân Hưng ward police detain him within two days", "summary": "HCMC street AI cameras identified a masked burglar who took two laptops and 14 million VND on 23 December 2025; police held him on 24 Dec.", "date_observed": "2025-12-24", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Early on 23 December 2025 an intruder entered a rented room at 62 Street 51, Tân Hưng ward, Ho Chi Minh City. He took two laptops and the wallet, identity papers and bank card of the tenant, H.A.T., 33. From his account, 14 million VND was withdrawn. He reported it that morning. Tân Hưng Ward Police and the Criminal Police Division used the street AI camera system. It analyzed the masked man's gait, build, clothing, movement and facial data, identified him and reconstructed his route. At 11 p.m. on 24 December officers found Phạm Ngọc Huy, 23, of Ninh Bình, at an internet café on Tô Ký street. He was a convicted thief. He confessed. Property recovery is not reported.", "category": "benefit created", "unexpected": false, "purpose": "Ho Chi Minh City's street AI cameras are run by the police for security and investigation. VnExpress describes the technology analyzing and matching biometric features. Identifying a suspect from footage is the use the police describe. The system and its vendor are not named.", "system": "Ho Chi Minh City police street AI camera system (unnamed): facial recognition with gait, body-shape, clothing and movement analysis", "model_version": null, "developer": null, "deployer": "Ho Chi Minh City Police (Tân Hưng Ward Police with the Criminal Police Division, PC02)", "location": "Ho Chi Minh City, Vietnam", "domain": "Policing / burglary investigation", "segment": "Public-space surveillance with facial and gait recognition", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-12-24-ee38" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "H.A.T. had the intruder who took his laptops, papers and 14 million VND identified, detained and confessing within two days. No source reports recovery of the property or the money. So the benefit to him is resolution, not restitution." }, { "group": "organizational", "depth": 2, "reason": "Tân Hưng Ward Police and PC02 cleared a masked-intruder burglary in under 72 hours. Seven outlets carried the case as the police's demonstration of the AI camera system." } ], "ai_role": { "status": "assessed", "statement": "The system's contribution was identification. It matched the masked intruder's gait, body shape, clothing, movement and facial data and reconstructed his route. That pointed police to him. The detention, confession and case-building were police work. The system and its vendor are not named. No official is quoted by name. Every report draws on the police account. The OECD Monitor tags the entry under privacy and data governance and respect of human rights, the concerns that come with public facial recognition." }, "evidence": [ { "kind": "news report", "citation": "Quốc Thắng / VnExpress (2025). Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ. VnExpress, 25 December 2025.", "title": "Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ", "url": "https://vnexpress.net/camera-ai-giup-canh-sat-nhan-dang-ke-dot-nhap-phong-tro-4998114.html", "date": "2025-12-25", "written_by": "independent", "quote": "Công nghệ AI đã phân tích, đối sánh các đặc điểm sinh trắc học" }, { "kind": "news report", "citation": "Minh Hòa / Tuổi Trẻ Online (2025). Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang. Tuổi Trẻ, 25 December 2025.", "title": "Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang", "url": "https://tuoitre.vn/ung-dung-camera-ai-cong-an-phuong-tan-hung-bat-nhanh-ke-trom-deo-khau-trang-20251225112926111.htm", "date": "2025-12-25", "written_by": "independent", "quote": "Đến 23h ngày 24-12, Tổ truy xét Công an phường Tân Hưng phối hợp Đội 2 PC02 và Công an phường Trung Mỹ Tây kiểm tra tụ điểm net" }, { "kind": "news report", "citation": "Trần Duy Khánh and Ngọc Lê / Thanh Niên (2025). Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI. Thanh Niên, 25 December 2025.", "title": "Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI", "url": "https://thanhnien.vn/cong-an-tphcm-lam-ro-vu-trom-cap-chi-sau-72-gio-nho-camera-ai-185251225111822364.htm", "date": "2025-12-25", "written_by": "independent", "quote": "phân tích dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt, hệ thống đã hỗ trợ lực lượng công an" }, { "kind": "news report", "citation": "Đàm Đệ / VietNamNet (2025). Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM. VietNamNet, 25 December 2025.", "title": "Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM", "url": "https://vietnamnet.vn/bat-nhanh-ke-trom-deo-khau-trang-nho-camera-ai-duong-pho-o-tphcm-2475850.html", "date": "2025-12-25", "written_by": "independent", "quote": "dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt" }, { "kind": "news report", "citation": "M.Đ / Công an nhân dân (2025). Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án. Báo Công an nhân dân điện tử, 25 December 2025.", "title": "Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án", "url": "https://cand.com.vn/Ban-tin-113/tu-du-lieu-cua-camera-ai-bat-doi-tuong-trom-sau-72-gio-gay-an-i792189/", "date": "2025-12-25", "written_by": "deployer", "quote": "qua phân tích, đối chiếu, khuôn mặt (kể cả đeo khẩu trang) dáng đi, hình thể, trang phục, hành vi, hệ thống đã nhận dạng được đối tượng" } ], "replication": { "produced_by": [ "Street AI cameras covering the route the intruder took, with footage retained for police review.", "A model that matches people on gait, body shape, clothing and movement as well as face, so a mask does not defeat identification.", "A prompt police report, the morning of the burglary, and a ward investigation team able to act on the match within hours." ], "must_hold": [ "The suspect passes through camera coverage and can be found at a fixed location afterwards, here an internet café.", "Identification is confirmed by police work and a confession. The sources report no independent check of the match.", "The system and vendor are unnamed, so performance beyond this case cannot be checked." ], "echoes": [] }, "paired_incident": null, "headline": "HCMC AI cameras identify a masked burglar; police detain him", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The 25 December 2025 reports end with the suspect's confession and the ward police consolidating the file; no prosecution or trial report was found.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "Vietnam", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and five Vietnamese reports of 25 December 2025. Benefit created or restored as the classifier proposed: a post-hoc investigation that resolved a burglary, not a prevented harm. The detention time differs between outlets: Tuổi Trẻ gives 23h on 24–12, Công an nhân dân the night of 24 to 25 December, VnExpress and VietNamNet date the arrest 25 December, and Thanh Niên prints “23 giờ ngày 25.12”, which postdates its own 11:52 publication and is treated as a misprint; the date observed is 24 December. The Monitor's Pháp Luật TP.HCM link resolved only to a headline stub and is not cited; the Công an nhân dân link redirected from cand.com.vn to cand.vn when read. The victim's year of birth is 1992 (VietNamNet, CAND); the suspect's is 2002. Property recovery is not mentioned in any source; the suspect is reported to have sold the items.", "source": "https://oecd.ai/en/incidents/2025-12-24-ee38", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_Thành_phố_Hồ_Chí_Minh", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports and the deployer's news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0154", "catalog_version": "1.0.0", "source_fingerprint": "b08bbc97e23b6b86a8da84a22c5067cd06278e55df97e383c15244bc848386f4", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0154-source-0001", "AIBD-0154-source-0002", "AIBD-0154-source-0003", "AIBD-0154-source-0004", "AIBD-0154-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Ho Chi Minh City Police (Tân Hưng Ward Police with the Criminal Police Division, PC02)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system's contribution was identification. It matched the masked intruder's gait, body shape, clothing, movement and facial data and reconstructed his route. That pointed police to him. The detention, confession and case-building were police work. The system and its vendor are not named. No official is quoted by name. Every report draws on the police account. The OECD Monitor tags the entry under privacy and data governance and respect of human rights, the concerns that come with public facial recognition." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The system's contribution was identification. It matched the masked intruder's gait, body shape, clothing, movement and facial data and reconstructed his route. That pointed police to him. The detention, confession and case-building were police work. The system and its vendor are not named. No official is quoted by name. Every report draws on the police account. The OECD Monitor tags the entry under privacy and data governance and respect of human rights, the concerns that come with public facial recognition." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "H.A.T. had the intruder who took his laptops, papers and 14 million VND identified, detained and confessing within two days. No source reports recovery of the property or the money. So the benefit to him is resolution, not restitution." }, { "group": "organizational", "depth": 2, "reason": "Tân Hưng Ward Police and PC02 cleared a masked-intruder burglary in under 72 hours. Seven outlets carried the case as the police's demonstration of the AI camera system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports and the deployer's news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "H.A.T. had the intruder who took his laptops, papers and 14 million VND identified, detained and confessing within two days. No source reports recovery of the property or the money. So the benefit to him is resolution, not restitution." }, { "group": "organizational", "depth": 2, "reason": "Tân Hưng Ward Police and PC02 cleared a masked-intruder burglary in under 72 hours. Seven outlets carried the case as the police's demonstration of the AI camera system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "H.A.T. had the intruder who took his laptops, papers and 14 million VND identified, detained and confessing within two days. No source reports recovery of the property or the money. So the benefit to him is resolution, not restitution." }, { "group": "organizational", "depth": 2, "reason": "Tân Hưng Ward Police and PC02 cleared a masked-intruder burglary in under 72 hours. Seven outlets carried the case as the police's demonstration of the AI camera system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-12-24-ee38" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The 25 December 2025 reports end with the suspect's confession and the ward police consolidating the file; no prosecution or trial report was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0154" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Early on 23 December 2025 an intruder entered a rented room at 62 Street 51, Tân Hưng ward, Ho Chi Minh City. He took two laptops and the wallet, identity papers and bank card of the tenant, H.A.T., 33. From his account, 14 million VND was withdrawn. He reported it that morning. Tân Hưng Ward Police and the Criminal Police Division used the street AI camera system. It analyzed the masked man's gait, build, clothing, movement and facial data, identified him and reconstructed his route. At 11 p.m. on 24 December officers found Phạm Ngọc Huy, 23, of Ninh Bình, at an internet café on Tô Ký street. He was a convicted thief. He confessed. Property recovery is not reported.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "H.A.T. had the intruder who took his laptops, papers and 14 million VND identified, detained and confessing within two days. No source reports recovery of the property or the money. So the benefit to him is resolution, not restitution." }, { "group": "organizational", "depth": 2, "reason": "Tân Hưng Ward Police and PC02 cleared a masked-intruder burglary in under 72 hours. Seven outlets carried the case as the police's demonstration of the AI camera system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Ho Chi Minh City's street AI cameras are run by the police for security and investigation. VnExpress describes the technology analyzing and matching biometric features. Identifying a suspect from footage is the use the police describe. The system and its vendor are not named." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Ho Chi Minh City, Vietnam", "deployer_country": "Vietnam" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Ho Chi Minh City's street AI cameras are run by the police for security and investigation. VnExpress describes the technology analyzing and matching biometric features. Identifying a suspect from footage is the use the police describe. The system and its vendor are not named." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-12-24", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and five Vietnamese reports of 25 December 2025. Benefit created or restored as the classifier proposed: a post-hoc investigation that resolved a burglary, not a prevented harm. The detention time differs between outlets: Tuổi Trẻ gives 23h on 24–12, Công an nhân dân the night of 24 to 25 December, VnExpress and VietNamNet date the arrest 25 December, and Thanh Niên prints “23 giờ ngày 25.12”, which postdates its own 11:52 publication and is treated as a misprint; the date observed is 24 December. The Monitor's Pháp Luật TP.HCM link resolved only to a headline stub and is not cited; the Công an nhân dân link redirected from cand.com.vn to cand.vn when read. The victim's year of birth is 1992 (VietNamNet, CAND); the suspect's is 2002. Property recovery is not mentioned in any source; the suspect is reported to have sold the items.", "source": "https://oecd.ai/en/incidents/2025-12-24-ee38", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_Thành_phố_Hồ_Chí_Minh", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Street AI cameras covering the route the intruder took, with footage retained for police review.", "A model that matches people on gait, body shape, clothing and movement as well as face, so a mask does not defeat identification.", "A prompt police report, the morning of the burglary, and a ward investigation team able to act on the match within hours." ], "must_hold": [ "The suspect passes through camera coverage and can be found at a fixed location afterwards, here an internet café.", "Identification is confirmed by police work and a confession. The sources report no independent check of the match.", "The system and vendor are unnamed, so performance beyond this case cannot be checked." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and five Vietnamese reports of 25 December 2025. Benefit created or restored as the classifier proposed: a post-hoc investigation that resolved a burglary, not a prevented harm. The detention time differs between outlets: Tuổi Trẻ gives 23h on 24–12, Công an nhân dân the night of 24 to 25 December, VnExpress and VietNamNet date the arrest 25 December, and Thanh Niên prints “23 giờ ngày 25.12”, which postdates its own 11:52 publication and is treated as a misprint; the date observed is 24 December. The Monitor's Pháp Luật TP.HCM link resolved only to a headline stub and is not cited; the Công an nhân dân link redirected from cand.com.vn to cand.vn when read. The victim's year of birth is 1992 (VietNamNet, CAND); the suspect's is 2002. Property recovery is not mentioned in any source; the suspect is reported to have sold the items.", "source": "https://oecd.ai/en/incidents/2025-12-24-ee38", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_Thành_phố_Hồ_Chí_Minh", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / burglary investigation", "segment": "Public-space surveillance with facial and gait recognition" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Quốc Thắng / VnExpress (2025). Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ. VnExpress, 25 December 2025.", "title": "Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ", "url": "https://vnexpress.net/camera-ai-giup-canh-sat-nhan-dang-ke-dot-nhap-phong-tro-4998114.html", "date": "2025-12-25", "written_by": "independent", "quote": "Công nghệ AI đã phân tích, đối sánh các đặc điểm sinh trắc học" }, { "kind": "news report", "citation": "Minh Hòa / Tuổi Trẻ Online (2025). Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang. Tuổi Trẻ, 25 December 2025.", "title": "Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang", "url": "https://tuoitre.vn/ung-dung-camera-ai-cong-an-phuong-tan-hung-bat-nhanh-ke-trom-deo-khau-trang-20251225112926111.htm", "date": "2025-12-25", "written_by": "independent", "quote": "Đến 23h ngày 24-12, Tổ truy xét Công an phường Tân Hưng phối hợp Đội 2 PC02 và Công an phường Trung Mỹ Tây kiểm tra tụ điểm net" }, { "kind": "news report", "citation": "Trần Duy Khánh and Ngọc Lê / Thanh Niên (2025). Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI. Thanh Niên, 25 December 2025.", "title": "Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI", "url": "https://thanhnien.vn/cong-an-tphcm-lam-ro-vu-trom-cap-chi-sau-72-gio-nho-camera-ai-185251225111822364.htm", "date": "2025-12-25", "written_by": "independent", "quote": "phân tích dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt, hệ thống đã hỗ trợ lực lượng công an" }, { "kind": "news report", "citation": "Đàm Đệ / VietNamNet (2025). Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM. VietNamNet, 25 December 2025.", "title": "Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM", "url": "https://vietnamnet.vn/bat-nhanh-ke-trom-deo-khau-trang-nho-camera-ai-duong-pho-o-tphcm-2475850.html", "date": "2025-12-25", "written_by": "independent", "quote": "dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt" }, { "kind": "news report", "citation": "M.Đ / Công an nhân dân (2025). Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án. Báo Công an nhân dân điện tử, 25 December 2025.", "title": "Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án", "url": "https://cand.com.vn/Ban-tin-113/tu-du-lieu-cua-camera-ai-bat-doi-tuong-trom-sau-72-gio-gay-an-i792189/", "date": "2025-12-25", "written_by": "deployer", "quote": "qua phân tích, đối chiếu, khuôn mặt (kể cả đeo khẩu trang) dáng đi, hình thể, trang phục, hành vi, hệ thống đã nhận dạng được đối tượng" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0154-source-0001", "AIBD-0154-source-0002", "AIBD-0154-source-0003", "AIBD-0154-source-0004", "AIBD-0154-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Quốc Thắng / VnExpress (2025). Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ. VnExpress, 25 December 2025.", "title": "Camera AI giúp cảnh sát nhận dạng kẻ đột nhập phòng trọ", "url": "https://vnexpress.net/camera-ai-giup-canh-sat-nhan-dang-ke-dot-nhap-phong-tro-4998114.html", "date": "2025-12-25", "written_by": "independent", "quote": "Công nghệ AI đã phân tích, đối sánh các đặc điểm sinh trắc học" }, { "kind": "news report", "citation": "Minh Hòa / Tuổi Trẻ Online (2025). Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang. Tuổi Trẻ, 25 December 2025.", "title": "Ứng dụng camera AI, Công an phường Tân Hưng bắt nhanh kẻ trộm đeo khẩu trang", "url": "https://tuoitre.vn/ung-dung-camera-ai-cong-an-phuong-tan-hung-bat-nhanh-ke-trom-deo-khau-trang-20251225112926111.htm", "date": "2025-12-25", "written_by": "independent", "quote": "Đến 23h ngày 24-12, Tổ truy xét Công an phường Tân Hưng phối hợp Đội 2 PC02 và Công an phường Trung Mỹ Tây kiểm tra tụ điểm net" }, { "kind": "news report", "citation": "Trần Duy Khánh and Ngọc Lê / Thanh Niên (2025). Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI. Thanh Niên, 25 December 2025.", "title": "Công an TP.HCM làm rõ vụ trộm cắp chỉ sau 72 giờ nhờ camera AI", "url": "https://thanhnien.vn/cong-an-tphcm-lam-ro-vu-trom-cap-chi-sau-72-gio-nho-camera-ai-185251225111822364.htm", "date": "2025-12-25", "written_by": "independent", "quote": "phân tích dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt, hệ thống đã hỗ trợ lực lượng công an" }, { "kind": "news report", "citation": "Đàm Đệ / VietNamNet (2025). Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM. VietNamNet, 25 December 2025.", "title": "Bắt nhanh kẻ trộm đeo khẩu trang nhờ camera AI đường phố ở TPHCM", "url": "https://vietnamnet.vn/bat-nhanh-ke-trom-deo-khau-trang-nho-camera-ai-duong-pho-o-tphcm-2475850.html", "date": "2025-12-25", "written_by": "independent", "quote": "dáng đi, đặc điểm hình thể, trang phục, hành vi di chuyển và dữ liệu khuôn mặt" }, { "kind": "news report", "citation": "M.Đ / Công an nhân dân (2025). Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án. Báo Công an nhân dân điện tử, 25 December 2025.", "title": "Từ dữ liệu của camera AI, bắt đối tượng trộm sau 72 giờ gây án", "url": "https://cand.com.vn/Ban-tin-113/tu-du-lieu-cua-camera-ai-bat-doi-tuong-trom-sau-72-gio-gay-an-i792189/", "date": "2025-12-25", "written_by": "deployer", "quote": "qua phân tích, đối chiếu, khuôn mặt (kể cả đeo khẩu trang) dáng đi, hình thể, trang phục, hành vi, hệ thống đã nhận dạng được đối tượng" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "HCMC street AI cameras identified a masked burglar who took two laptops and 14 million VND on 23 December 2025; police held him on 24 Dec.", "account": "Early on 23 December 2025 an intruder entered a rented room at 62 Street 51, Tân Hưng ward, Ho Chi Minh City. He took two laptops and the wallet, identity papers and bank card of the tenant, H.A.T., 33. From his account, 14 million VND was withdrawn. He reported it that morning. Tân Hưng Ward Police and the Criminal Police Division used the street AI camera system. It analyzed the masked man's gait, build, clothing, movement and facial data, identified him and reconstructed his route. At 11 p.m. on 24 December officers found Phạm Ngọc Huy, 23, of Ninh Bình, at an internet café on Tô Ký street. He was a convicted thief. He confessed. Property recovery is not reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Ho Chi Minh City police street AI camera system (unnamed): facial recognition with gait, body-shape, clothing and movement analysis" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Ho Chi Minh City street AI cameras identify a masked burglar by gait, build, clothing and face; Tân Hưng ward police detain him within two days", "headline": "HCMC AI cameras identify a masked burglar; police detain him" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "b08bbc97e23b6b86a8da84a22c5067cd06278e55df97e383c15244bc848386f4" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Early on 23 December 2025 an intruder entered a rented room at 62 Street 51, Tân Hưng ward, Ho Chi Minh City. He took two laptops and the wallet, identity papers and bank card of the tenant, H.A.T., 33. From his account, 14 million VND was withdrawn. He reported it that morning. Tân Hưng Ward Police and the Criminal Police Division used the street AI camera system. It analyzed the masked man's gait, build, clothing, movement and facial data, identified him and reconstructed his route. At 11 p.m. on 24 December officers found Phạm Ngọc Huy, 23, of Ninh Bình, at an internet café on Tô Ký street. He was a convicted thief. He confessed. Property recovery is not reported.", "replication": { "produced_by": [ "Street AI cameras covering the route the intruder took, with footage retained for police review.", "A model that matches people on gait, body shape, clothing and movement as well as face, so a mask does not defeat identification.", "A prompt police report, the morning of the burglary, and a ward investigation team able to act on the match within hours." ], "must_hold": [ "The suspect passes through camera coverage and can be found at a fixed location afterwards, here an internet café.", "Identification is confirmed by police work and a confession. The sources report no independent check of the match.", "The system and vendor are unnamed, so performance beyond this case cannot be checked." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0153", "case_url": "records/AIBD-0153.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Autoland exists to take complete control of an aircraft and land it when the pilot cannot fly. It engages on its own when cabin altitude exceeds a set level. That is what happened here. The two pilots turned out not to be incapacitated, though, and chose to let it finish." }, { "label": "AI contribution", "value": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0153", "title": "Garmin Autoland engages after a King Air B200 loses cabin pressure at 23,000 ft and lands it at Broomfield, Colorado, in its first real emergency use", "summary": "On 20 December 2025 Garmin Autoland flew a depressurised King Air B200 to a landing at Rocky Mountain Metropolitan Airport; two pilots unhurt.", "date_observed": "2025-12-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Saturday 20 December 2025 Buffalo River Aviation's Beechcraft Super King Air B200, N479BR, left Aspen-Pitkin County Airport at 1:43 p.m. It carried two pilots and no passengers. At about 23,000 ft it suffered a rapid, uncommanded loss of pressurization. When cabin altitude passed the safe limit, Garmin Autoland engaged automatically. It sent automated emergency broadcasts to air traffic control. It flew the aircraft to a landing on Runway 30 at Rocky Mountain Metropolitan Airport, Broomfield, Colorado, shortly after 2 p.m. The crew chose to leave it engaged. Nobody was treated. The Federal Aviation Administration (FAA) said it was investigating and the NTSB that it was gathering information. Garmin called it the first use of Autoland from start to finish in an actual emergency.", "category": "protective response", "unexpected": false, "purpose": "Autoland exists to take complete control of an aircraft and land it when the pilot cannot fly. It engages on its own when cabin altitude exceeds a set level. That is what happened here. The two pilots turned out not to be incapacitated, though, and chose to let it finish.", "system": "Garmin Autoland (Emergency Autoland)", "model_version": null, "developer": "Garmin", "deployer": "Buffalo River Aviation", "location": "Broomfield, United States", "domain": "Aviation safety / automatic emergency landing", "segment": "Charter turboprop operations", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-12-22-41ce" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ], "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." }, "evidence": [ { "kind": "news report", "citation": "Sarah Horbacewicz / CBS News Colorado (2025). Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use. CBS News, 22 December 2025.", "title": "Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use", "url": "https://www.cbsnews.com/colorado/news/plane-emergency-landing-colorado-autoland/", "date": "2025-12-22", "written_by": "independent", "quote": "An onboard emergency autoland system was activated. Two people were on board. The FAA is investigating." }, { "kind": "news report", "citation": "Sam Sweeney and Ayesha Ali / ABC News (2025). Small plane lands safely with Autoland system after pilot is incapacitated. ABC News, 22 December 2025.", "title": "Small plane lands safely with Autoland system after pilot is incapacitated", "url": "https://abcnews.com/US/small-plane-lands-safely-autoland-system-after-pilot/story?id=128629603", "date": "2025-12-22", "written_by": "independent", "quote": "first use of Autoland from start-to-finish in an actual emergency" }, { "kind": "news report", "citation": "Jude Karabus / The Register (2025). Garmin autopilot lands small plane without pilot's help. The Register, 24 December 2025.", "title": "Garmin autopilot lands small plane without pilot's help", "url": "https://www.theregister.com/2025/12/24/autocomms/", "date": "2025-12-24", "written_by": "independent", "quote": "automatically engaged exactly as designed when the cabin altitude exceeded the prescribed safe levels" }, { "kind": "news report", "citation": "Katie Parkins and Óscar Contreras / Denver7 (2025). In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'. Denver7 (KMGH), 22 December 2025.", "title": "In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'", "url": "https://www.denver7.com/news/front-range/broomfield/in-a-first-plane-makes-emergency-landing-at-rmma-using-automated-technology-due-to-pilot-incapacitation", "date": "2025-12-22", "written_by": "independent", "quote": "no one on the plane was treated on scene or taken to a hospital" }, { "kind": "news report", "citation": "Matt Ryan / AVweb (2025). Garmin Autoland Safely Lands King Air After Pilot Incapacitation. AVweb, 22 December 2025.", "title": "Garmin Autoland Safely Lands King Air After Pilot Incapacitation", "url": "https://avweb.com/aviation-news/garmin-autoland-safely-lands-king-air/", "date": "2025-12-22", "written_by": "independent", "quote": "transmitting automated broadcasts indicating a pilot incapacitation and intent to conduct an emergency autoland" }, { "kind": "news report", "citation": "Rachel Dobkin / The Independent (2025). Airplane automatically lands itself after an inflight emergency. The Independent, syndicated on Yahoo News, 24 December 2025.", "title": "Airplane automatically lands itself after an inflight emergency", "url": "https://www.yahoo.com/news/articles/airplane-automatically-lands-itself-inflight-044612392.html", "date": "2025-12-24", "written_by": "independent", "quote": "No patients were treated on scene or transported to local hospitals." } ], "replication": { "produced_by": [ "An aircraft fitted with Garmin Autoland. Garmin says more than 1,700 are equipped.", "A rapid, uncommanded loss of pressurization that pushed cabin altitude past the system's automatic trigger.", "A crew that decided to let the automatic sequence run to touchdown rather than take over." ], "must_hold": [ "The aircraft remains airworthy. Autoland is a fail-safe for a working aircraft whose pilot cannot fly it, in the words of the journalist CBS quoted.", "A suitable airport is in range. The Register describes selection by runway length, distance and fuel range.", "Controllers and ground services act on the automated broadcasts, as Broomfield's two fire departments did." ], "echoes": [] }, "paired_incident": null, "headline": "Garmin Autoland lands depressurised King Air B200; pilots unhurt", "metric": "two pilots unhurt", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The FAA said it was investigating and the NTSB that it was collecting information to decide whether the event met its criteria; no FAA or NTSB report or other development after December 2025 was found. The aircraft flew on to Oklahoma City the next morning.", "quote": null, "url": null }, "value": null, "developer_country": "Switzerland", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six news reports. The Monitor dates the event 22 December 2025; every report puts the landing on Saturday 20 December, which is used here. Early reports (AVweb, Denver7, ABC's headline) said the pilot was incapacitated; the operator's CEO, quoted by CBS, ABC and The Register, says the system engaged on cabin-altitude exceedance and the crew were not incapacitated. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the crew were conscious and elected to leave the system engaged, so no counterfactual is established. Aircraft type is given as Super King Air 200 (ABC, Denver7) and King Air B200 (AVweb, The Register). The Monitor's General Aviation News and FOX31 follow-up links returned HTTP 403, AVweb's “crew decision” piece returned 404, and the Independent blocked fetching, so its Yahoo syndication is cited. Garmin's Autonomí pages returned only navigation, so no vendor source is cited; Garmin's statements are quoted via the news reports.", "source": "https://oecd.ai/en/incidents/2025-12-22-41ce", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Garmin; https://buffaloriveraviation.com/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0153", "catalog_version": "1.0.0", "source_fingerprint": "e067d3612528a25b90b00340bad1e609571c414ab9142cd0bea9cf9db371b9b5", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0153-source-0001", "AIBD-0153-source-0002", "AIBD-0153-source-0003", "AIBD-0153-source-0004", "AIBD-0153-source-0005", "AIBD-0153-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Garmin", "deployer": "Buffalo River Aviation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "Autoland engaged itself when cabin altitude exceeded the limit. It declared the emergency to controllers with automated “pilot incapacitation” broadcasts. It flew the descent, approach and landing. The pilots elected to leave it engaged rather than take over. So the landing is the system's work. But the counterfactual, that they could not have landed, is not established. Garmin, the FAA and the operator all call it an automated system. None calls it AI. It is here because the OECD Monitor lists it as an AI system. The Register describes it choosing landing sites from runway length, distance and fuel range." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": 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"not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "two pilots unhurt", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-12-22-41ce" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The FAA said it was investigating and the NTSB that it was collecting information to decide whether the event met its criteria; no FAA or NTSB report or other development after December 2025 was found. The aircraft flew on to Oklahoma City the next morning." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0153" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Saturday 20 December 2025 Buffalo River Aviation's Beechcraft Super King Air B200, N479BR, left Aspen-Pitkin County Airport at 1:43 p.m. It carried two pilots and no passengers. At about 23,000 ft it suffered a rapid, uncommanded loss of pressurization. When cabin altitude passed the safe limit, Garmin Autoland engaged automatically. It sent automated emergency broadcasts to air traffic control. It flew the aircraft to a landing on Runway 30 at Rocky Mountain Metropolitan Airport, Broomfield, Colorado, shortly after 2 p.m. The crew chose to leave it engaged. Nobody was treated. The Federal Aviation Administration (FAA) said it was investigating and the NTSB that it was gathering information. Garmin called it the first use of Autoland from start to finish in an actual emergency.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Two pilots who had lost cabin pressure at 23,000 ft were on the ground in Broomfield within about 35 minutes of departure, untreated and uninjured. They were conscious and could have flown the descent themselves. So the benefit is a safe automatic recovery, not a documented life saved." }, { "group": "organizational", "depth": 1, "reason": "Buffalo River Aviation's aircraft “landed safely without incident”, in the airport spokeswoman's words. No damage is reported, and the FAA investigation was still open." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Autoland exists to take complete control of an aircraft and land it when the pilot cannot fly. It engages on its own when cabin altitude exceeds a set level. That is what happened here. The two pilots turned out not to be incapacitated, though, and chose to let it finish." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Broomfield, United States", "developer_country": "Switzerland", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Autoland exists to take complete control of an aircraft and land it when the pilot cannot fly. It engages on its own when cabin altitude exceeds a set level. That is what happened here. The two pilots turned out not to be incapacitated, though, and chose to let it finish." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-12-20", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six news reports. The Monitor dates the event 22 December 2025; every report puts the landing on Saturday 20 December, which is used here. Early reports (AVweb, Denver7, ABC's headline) said the pilot was incapacitated; the operator's CEO, quoted by CBS, ABC and The Register, says the system engaged on cabin-altitude exceedance and the crew were not incapacitated. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the crew were conscious and elected to leave the system engaged, so no counterfactual is established. Aircraft type is given as Super King Air 200 (ABC, Denver7) and King Air B200 (AVweb, The Register). The Monitor's General Aviation News and FOX31 follow-up links returned HTTP 403, AVweb's “crew decision” piece returned 404, and the Independent blocked fetching, so its Yahoo syndication is cited. Garmin's Autonomí pages returned only navigation, so no vendor source is cited; Garmin's statements are quoted via the news reports.", "source": "https://oecd.ai/en/incidents/2025-12-22-41ce", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Garmin; https://buffaloriveraviation.com/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "An aircraft fitted with Garmin Autoland. Garmin says more than 1,700 are equipped.", "A rapid, uncommanded loss of pressurization that pushed cabin altitude past the system's automatic trigger.", "A crew that decided to let the automatic sequence run to touchdown rather than take over." ], "must_hold": [ "The aircraft remains airworthy. Autoland is a fail-safe for a working aircraft whose pilot cannot fly it, in the words of the journalist CBS quoted.", "A suitable airport is in range. The Register describes selection by runway length, distance and fuel range.", "Controllers and ground services act on the automated broadcasts, as Broomfield's two fire departments did." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six news reports. The Monitor dates the event 22 December 2025; every report puts the landing on Saturday 20 December, which is used here. Early reports (AVweb, Denver7, ABC's headline) said the pilot was incapacitated; the operator's CEO, quoted by CBS, ABC and The Register, says the system engaged on cabin-altitude exceedance and the crew were not incapacitated. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the crew were conscious and elected to leave the system engaged, so no counterfactual is established. Aircraft type is given as Super King Air 200 (ABC, Denver7) and King Air B200 (AVweb, The Register). The Monitor's General Aviation News and FOX31 follow-up links returned HTTP 403, AVweb's “crew decision” piece returned 404, and the Independent blocked fetching, so its Yahoo syndication is cited. Garmin's Autonomí pages returned only navigation, so no vendor source is cited; Garmin's statements are quoted via the news reports.", "source": "https://oecd.ai/en/incidents/2025-12-22-41ce", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Garmin; https://buffaloriveraviation.com/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Aviation safety / automatic emergency landing", "segment": "Charter turboprop operations" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Sarah Horbacewicz / CBS News Colorado (2025). Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use. CBS News, 22 December 2025.", "title": "Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use", "url": "https://www.cbsnews.com/colorado/news/plane-emergency-landing-colorado-autoland/", "date": "2025-12-22", "written_by": "independent", "quote": "An onboard emergency autoland system was activated. Two people were on board. The FAA is investigating." }, { "kind": "news report", "citation": "Sam Sweeney and Ayesha Ali / ABC News (2025). Small plane lands safely with Autoland system after pilot is incapacitated. ABC News, 22 December 2025.", "title": "Small plane lands safely with Autoland system after pilot is incapacitated", "url": "https://abcnews.com/US/small-plane-lands-safely-autoland-system-after-pilot/story?id=128629603", "date": "2025-12-22", "written_by": "independent", "quote": "first use of Autoland from start-to-finish in an actual emergency" }, { "kind": "news report", "citation": "Jude Karabus / The Register (2025). Garmin autopilot lands small plane without pilot's help. The Register, 24 December 2025.", "title": "Garmin autopilot lands small plane without pilot's help", "url": "https://www.theregister.com/2025/12/24/autocomms/", "date": "2025-12-24", "written_by": "independent", "quote": "automatically engaged exactly as designed when the cabin altitude exceeded the prescribed safe levels" }, { "kind": "news report", "citation": "Katie Parkins and Óscar Contreras / Denver7 (2025). In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'. Denver7 (KMGH), 22 December 2025.", "title": "In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'", "url": "https://www.denver7.com/news/front-range/broomfield/in-a-first-plane-makes-emergency-landing-at-rmma-using-automated-technology-due-to-pilot-incapacitation", "date": "2025-12-22", "written_by": "independent", "quote": "no one on the plane was treated on scene or taken to a hospital" }, { "kind": "news report", "citation": "Matt Ryan / AVweb (2025). Garmin Autoland Safely Lands King Air After Pilot Incapacitation. AVweb, 22 December 2025.", "title": "Garmin Autoland Safely Lands King Air After Pilot Incapacitation", "url": "https://avweb.com/aviation-news/garmin-autoland-safely-lands-king-air/", "date": "2025-12-22", "written_by": "independent", "quote": "transmitting automated broadcasts indicating a pilot incapacitation and intent to conduct an emergency autoland" }, { "kind": "news report", "citation": "Rachel Dobkin / The Independent (2025). Airplane automatically lands itself after an inflight emergency. The Independent, syndicated on Yahoo News, 24 December 2025.", "title": "Airplane automatically lands itself after an inflight emergency", "url": "https://www.yahoo.com/news/articles/airplane-automatically-lands-itself-inflight-044612392.html", "date": "2025-12-24", "written_by": "independent", "quote": "No patients were treated on scene or transported to local hospitals." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0153-source-0001", "AIBD-0153-source-0002", "AIBD-0153-source-0003", "AIBD-0153-source-0004", "AIBD-0153-source-0005", "AIBD-0153-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Sarah Horbacewicz / CBS News Colorado (2025). Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use. CBS News, 22 December 2025.", "title": "Plane makes safe emergency landing in Colorado without a pilot's help, first 'Autoland' use", "url": "https://www.cbsnews.com/colorado/news/plane-emergency-landing-colorado-autoland/", "date": "2025-12-22", "written_by": "independent", "quote": "An onboard emergency autoland system was activated. Two people were on board. The FAA is investigating." }, { "kind": "news report", "citation": "Sam Sweeney and Ayesha Ali / ABC News (2025). Small plane lands safely with Autoland system after pilot is incapacitated. ABC News, 22 December 2025.", "title": "Small plane lands safely with Autoland system after pilot is incapacitated", "url": "https://abcnews.com/US/small-plane-lands-safely-autoland-system-after-pilot/story?id=128629603", "date": "2025-12-22", "written_by": "independent", "quote": "first use of Autoland from start-to-finish in an actual emergency" }, { "kind": "news report", "citation": "Jude Karabus / The Register (2025). Garmin autopilot lands small plane without pilot's help. The Register, 24 December 2025.", "title": "Garmin autopilot lands small plane without pilot's help", "url": "https://www.theregister.com/2025/12/24/autocomms/", "date": "2025-12-24", "written_by": "independent", "quote": "automatically engaged exactly as designed when the cabin altitude exceeded the prescribed safe levels" }, { "kind": "news report", "citation": "Katie Parkins and Óscar Contreras / Denver7 (2025). In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'. Denver7 (KMGH), 22 December 2025.", "title": "In a first, plane makes emergency landing at RMMA using automated technology due to 'pilot incapacitation'", "url": "https://www.denver7.com/news/front-range/broomfield/in-a-first-plane-makes-emergency-landing-at-rmma-using-automated-technology-due-to-pilot-incapacitation", "date": "2025-12-22", "written_by": "independent", "quote": "no one on the plane was treated on scene or taken to a hospital" }, { "kind": "news report", "citation": "Matt Ryan / AVweb (2025). Garmin Autoland Safely Lands King Air After Pilot Incapacitation. AVweb, 22 December 2025.", "title": "Garmin Autoland Safely Lands King Air After Pilot Incapacitation", "url": "https://avweb.com/aviation-news/garmin-autoland-safely-lands-king-air/", "date": "2025-12-22", "written_by": "independent", "quote": "transmitting automated broadcasts indicating a pilot incapacitation and intent to conduct an emergency autoland" }, { "kind": "news report", "citation": "Rachel Dobkin / The Independent (2025). Airplane automatically lands itself after an inflight emergency. The Independent, syndicated on Yahoo News, 24 December 2025.", "title": "Airplane automatically lands itself after an inflight emergency", "url": "https://www.yahoo.com/news/articles/airplane-automatically-lands-itself-inflight-044612392.html", "date": "2025-12-24", "written_by": "independent", "quote": "No patients were treated on scene or transported to local hospitals." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "On 20 December 2025 Garmin Autoland flew a depressurised King Air B200 to a landing at Rocky Mountain Metropolitan Airport; two pilots unhurt.", "account": "On Saturday 20 December 2025 Buffalo River Aviation's Beechcraft Super King Air B200, N479BR, left Aspen-Pitkin County Airport at 1:43 p.m. It carried two pilots and no passengers. At about 23,000 ft it suffered a rapid, uncommanded loss of pressurization. When cabin altitude passed the safe limit, Garmin Autoland engaged automatically. It sent automated emergency broadcasts to air traffic control. It flew the aircraft to a landing on Runway 30 at Rocky Mountain Metropolitan Airport, Broomfield, Colorado, shortly after 2 p.m. The crew chose to leave it engaged. Nobody was treated. The Federal Aviation Administration (FAA) said it was investigating and the NTSB that it was gathering information. Garmin called it the first use of Autoland from start to finish in an actual emergency." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Garmin Autoland (Emergency Autoland)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Garmin" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Garmin Autoland engages after a King Air B200 loses cabin pressure at 23,000 ft and lands it at Broomfield, Colorado, in its first real emergency use", "headline": "Garmin Autoland lands depressurised King Air B200; pilots unhurt" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "e067d3612528a25b90b00340bad1e609571c414ab9142cd0bea9cf9db371b9b5" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Saturday 20 December 2025 Buffalo River Aviation's Beechcraft Super King Air B200, N479BR, left Aspen-Pitkin County Airport at 1:43 p.m. It carried two pilots and no passengers. At about 23,000 ft it suffered a rapid, uncommanded loss of pressurization. When cabin altitude passed the safe limit, Garmin Autoland engaged automatically. It sent automated emergency broadcasts to air traffic control. It flew the aircraft to a landing on Runway 30 at Rocky Mountain Metropolitan Airport, Broomfield, Colorado, shortly after 2 p.m. The crew chose to leave it engaged. Nobody was treated. The Federal Aviation Administration (FAA) said it was investigating and the NTSB that it was gathering information. Garmin called it the first use of Autoland from start to finish in an actual emergency.", "replication": { "produced_by": [ "An aircraft fitted with Garmin Autoland. Garmin says more than 1,700 are equipped.", "A rapid, uncommanded loss of pressurization that pushed cabin altitude past the system's automatic trigger.", "A crew that decided to let the automatic sequence run to touchdown rather than take over." ], "must_hold": [ "The aircraft remains airworthy. Autoland is a fail-safe for a working aircraft whose pilot cannot fly it, in the words of the journalist CBS quoted.", "A suitable airport is in range. The Register describes selection by runway length, distance and fuel range.", "Controllers and ground services act on the automated broadcasts, as Broomfield's two fire departments did." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0152", "case_url": "records/AIBD-0152.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Hanoi's AI traffic-camera system has 1,837 cameras with AI-controlled signals and was due to enter official operation in December 2025, per VOV. The Traffic Police run it, in VOV's words, “to ensure security and order and support the people” as well as to manage traffic. Tracing a lost resident for a ward police search falls within that stated remit." }, { "label": "AI contribution", "value": "The system's contribution was the trace. Police searched it in the direction the family gave. It returned images of the man walking along Nguyễn Tuân street toward Thanh Xuân Park and across other streets. That gave the police a route and pictures for a public appeal. A passing resident found him in Dịch Vọng about eleven hours after the report. No source says the finder had seen the appeal or that officers reached him. So the discovery itself is not attributable to the system. The vendor is not named and every account comes from the police." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Vietnam" ] }, "record": { "id": "AIBD-0152", "title": "Hanoi traffic police trace a missing 78-year-old on the city's new AI camera network; a passer-by finds him hypoglycemic in Dịch Vọng at 3 a.m.", "summary": "Hanoi police traced a lost 78-year-old on AI traffic cameras on 15 December 2025; a passer-by found him at 3 a.m. the next day and took him home.", "date_observed": "2025-12-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Mr Đ.V.T., 78, of Thanh Xuân ward, Hanoi, went out to exercise at 3:30 p.m. on 15 December 2025 and did not return. At 8:15 p.m. his family reported him to Thanh Xuân Ward Police. Ward officers worked with the Hanoi Traffic Police Department. Major Nguyễn Tá Khanh's team there searched the AI traffic-camera system in the direction the family gave. It traced him along Nguyễn Tuân street toward Thanh Xuân Park and other streets. VietNamNet and Tiền Phong say the images were posted on social media. At about 3 a.m. on 16 December a passing resident found him in Dịch Vọng, hypoglycemic and close to fainting, and took him home. No source links the finder to the appeal.", "category": "benefit created", "unexpected": false, "purpose": "Hanoi's AI traffic-camera system has 1,837 cameras with AI-controlled signals and was due to enter official operation in December 2025, per VOV. The Traffic Police run it, in VOV's words, “to ensure security and order and support the people” as well as to manage traffic. Tracing a lost resident for a ward police search falls within that stated remit.", "system": "Hanoi Traffic Police AI traffic-camera system (1,837 cameras)", "model_version": null, "developer": null, "deployer": "Hanoi City Police, Traffic Police Department (Phòng Cảnh sát giao thông), with Thanh Xuân Ward Police", "location": "Hanoi, Vietnam", "domain": "Policing / missing persons", "segment": "Missing elderly person traced on urban traffic cameras", "benefit_type": "Improved Physical Health", "taxonomy_note": "Tracing a lost elderly man is read as a rescue; a passer-by made the actual find.", "registry_ids": { "oecd_aim": "2025-12-16-205c" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "A 78-year-old who had been out for nearly twelve hours on a December night was home by about 3 a.m. He was found hypoglycemic and close to fainting. He and his family got him back safely, though the finder was a passer-by rather than the police." }, { "group": "organizational", "depth": 1, "reason": "Thanh Xuân Ward Police and the Traffic Police got a direction of travel and images from the camera trace instead of a blind search. The police present the case as evidence of the new system's value." } ], "ai_role": { "status": "assessed", "statement": "The system's contribution was the trace. Police searched it in the direction the family gave. It returned images of the man walking along Nguyễn Tuân street toward Thanh Xuân Park and across other streets. That gave the police a route and pictures for a public appeal. A passing resident found him in Dịch Vọng about eleven hours after the report. No source says the finder had seen the appeal or that officers reached him. So the discovery itself is not attributable to the system. The vendor is not named and every account comes from the police." }, "evidence": [ { "kind": "news report", "citation": "Việt An / VnExpress (2025). Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội. VnExpress, 17 December 2025.", "title": "Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội", "url": "https://vnexpress.net/camera-ai-ho-tro-tim-cu-ong-di-lac-o-ha-noi-4994889.html", "date": "2025-12-17", "written_by": "independent", "quote": "rà soát, truy vết hành trình của cụ ông qua hệ thống camera AI" }, { "kind": "news report", "citation": "Đình Hiếu / VietNamNet (2025). Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội. VietNamNet, 17 December 2025.", "title": "Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội", "url": "https://vietnamnet.vn/canh-sat-trich-xuat-camera-ai-giup-truy-vet-tim-nguoi-dan-ong-di-lac-o-ha-noi-2473262.html", "date": "2025-12-17", "written_by": "independent", "quote": "Công an phường Thanh Xuân đã cử cán bộ phối hợp với Phòng CSGT tổ chức rà soát, truy vết hình ảnh từ hệ thống camera AI" }, { "kind": "news report", "citation": "Hồng Quang / Tuổi Trẻ Online (2025). Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố. Tuổi Trẻ, 17 December 2025.", "title": "Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố", "url": "https://tuoitre.vn/camera-ai-truy-vet-tim-cu-ong-di-lac-qua-nhieu-tuyen-pho-20251217102042429.htm", "date": "2025-12-17", "written_by": "independent", "quote": "Kết quả ban đầu thu được các hình ảnh cụ ông đi dọc tuyến đường Nguyễn Tuân" }, { "kind": "news report", "citation": "Thanh Hà / Tiền Phong (2025). Camera AI theo dấu cụ ông đi lạc ở Hà Nội. Báo điện tử Tiền Phong, 17 December 2025.", "title": "Camera AI theo dấu cụ ông đi lạc ở Hà Nội", "url": "https://tienphong.vn/camera-ai-theo-dau-cu-ong-di-lac-o-ha-noi-post1805284.tpo", "date": "2025-12-17", "written_by": "independent", "quote": "Đến gần 3h sáng 16/12, một người dân trong quá trình tham gia giao thông đã phát hiện cụ ông trong tình trạng hạ đường huyết" }, { "kind": "news report", "citation": "VOV (2025). Camera AI tìm người đi lạc ở Hà Nội. VOV.vn, 17 December 2025.", "title": "Camera AI tìm người đi lạc ở Hà Nội", "url": "https://vov.vn/xa-hoi/camera-ai-tim-nguoi-di-lac-o-ha-noi-post1254446.vov", "date": "2025-12-17", "written_by": "independent", "quote": "ứng dụng hệ thống camera AI trong công tác bảo đảm an ninh, trật tự và hỗ trợ nhân dân" }, { "kind": "news report", "citation": "Anh Ngọc / Phụ Nữ Online (2025). Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn. Phụ Nữ Online, 17 December 2025.", "title": "Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn", "url": "https://www.phunuonline.com.vn/tu-hinh-anh-qua-camera-ai-canh-sat-giup-cu-ong-di-lac-ve-nha-an-toan-a1569649.html", "date": "2025-12-17", "written_by": "independent", "quote": "Rà soát, truy vết hình ảnh từ hệ thống camera giao thông AI theo hướng di chuyển do người nhà cung cấp." } ], "replication": { "produced_by": [ "A dense AI camera network along the route the person walked, here 1,837 cameras across Hanoi, with footage searchable by appearance and direction of travel.", "A family report the same evening giving a starting point and a direction for the camera search.", "A ward police unit able to hand the search to the Traffic Police's camera-control team the same night." ], "must_hold": [ "The missing person stays within camera coverage. Here the trace ended at a route, not at the man, and a passer-by made the find.", "Someone acts on the trace or the appeal. The sources do not establish that link in this case.", "The vendor and the matching method are undisclosed, so the trace cannot be checked beyond the police account." ], "echoes": [] }, "paired_incident": null, "headline": "Hanoi traffic police trace a lost 78-year-old on AI cameras", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "Vietnam", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six Vietnamese reports of 17 December 2025. Kept at benefit created or restored as the classifier proposed: the camera trace is documented, but the man was found by a passing resident and no source links the finder to the trace or the appeal, so no prevented harm is evidenced. The rank Thiếu tá is rendered as Major. VOV reports that the 1,837-camera system was to begin official operation in December 2025, so this was among its first publicized cases. The Phụ Nữ Online link redirected to a tuoitre.vn mirror path when read on 20 October 2026; the original URL is cited. The 8:15 p.m. report time and the 3 a.m. find are consistent across VnExpress, VietNamNet, Tuổi Trẻ, Tiền Phong, VOV and Phụ Nữ Online.", "source": "https://oecd.ai/en/incidents/2025-12-16-205c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_thành_phố_Hà_Nội", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0152", "catalog_version": "1.0.0", "source_fingerprint": "4f37900df8535e39dd84d65a5243a1198edcd7578b78db82935ccf2cb5eae3dc", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0152-source-0001", "AIBD-0152-source-0002", "AIBD-0152-source-0003", "AIBD-0152-source-0004", "AIBD-0152-source-0005", "AIBD-0152-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Hanoi City Police, Traffic Police Department (Phòng Cảnh sát giao thông), with Thanh Xuân Ward Police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system's contribution was the trace. Police searched it in the direction the family gave. It returned images of the man walking along Nguyễn Tuân street toward Thanh Xuân Park and across other streets. That gave the police a route and pictures for a public appeal. A passing resident found him in Dịch Vọng about eleven hours after the report. No source says the finder had seen the appeal or that officers reached him. So the discovery itself is not attributable to the system. The vendor is not named and every account comes from the police." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The system's contribution was the trace. Police searched it in the direction the family gave. It returned images of the man walking along Nguyễn Tuân street toward Thanh Xuân Park and across other streets. That gave the police a route and pictures for a public appeal. A passing resident found him in Dịch Vọng about eleven hours after the report. No source says the finder had seen the appeal or that officers reached him. So the discovery itself is not attributable to the system. The vendor is not named and every account comes from the police." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A 78-year-old who had been out for nearly twelve hours on a December night was home by about 3 a.m. He was found hypoglycemic and close to fainting. He and his family got him back safely, though the finder was a passer-by rather than the police." }, { "group": "organizational", "depth": 1, "reason": "Thanh Xuân Ward Police and the Traffic Police got a direction of travel and images from the camera trace instead of a blind search. The police present the case as evidence of the new system's value." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: six independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A 78-year-old who had been out for nearly twelve hours on a December night was home by about 3 a.m. He was found hypoglycemic and close to fainting. He and his family got him back safely, though the finder was a passer-by rather than the police." }, { "group": "organizational", "depth": 1, "reason": "Thanh Xuân Ward Police and the Traffic Police got a direction of travel and images from the camera trace instead of a blind search. The police present the case as evidence of the new system's value." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A 78-year-old who had been out for nearly twelve hours on a December night was home by about 3 a.m. He was found hypoglycemic and close to fainting. He and his family got him back safely, though the finder was a passer-by rather than the police." }, { "group": "organizational", "depth": 1, "reason": "Thanh Xuân Ward Police and the Traffic Police got a direction of travel and images from the camera trace instead of a blind search. The police present the case as evidence of the new system's value." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-12-16-205c" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0152" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "Tracing a lost elderly man is read as a rescue; a passer-by made the actual find." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Mr Đ.V.T., 78, of Thanh Xuân ward, Hanoi, went out to exercise at 3:30 p.m. on 15 December 2025 and did not return. At 8:15 p.m. his family reported him to Thanh Xuân Ward Police. Ward officers worked with the Hanoi Traffic Police Department. Major Nguyễn Tá Khanh's team there searched the AI traffic-camera system in the direction the family gave. It traced him along Nguyễn Tuân street toward Thanh Xuân Park and other streets. VietNamNet and Tiền Phong say the images were posted on social media. At about 3 a.m. on 16 December a passing resident found him in Dịch Vọng, hypoglycemic and close to fainting, and took him home. No source links the finder to the appeal.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A 78-year-old who had been out for nearly twelve hours on a December night was home by about 3 a.m. He was found hypoglycemic and close to fainting. He and his family got him back safely, though the finder was a passer-by rather than the police." }, { "group": "organizational", "depth": 1, "reason": "Thanh Xuân Ward Police and the Traffic Police got a direction of travel and images from the camera trace instead of a blind search. The police present the case as evidence of the new system's value." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Hanoi's AI traffic-camera system has 1,837 cameras with AI-controlled signals and was due to enter official operation in December 2025, per VOV. The Traffic Police run it, in VOV's words, “to ensure security and order and support the people” as well as to manage traffic. Tracing a lost resident for a ward police search falls within that stated remit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Hanoi, Vietnam", "deployer_country": "Vietnam" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Hanoi's AI traffic-camera system has 1,837 cameras with AI-controlled signals and was due to enter official operation in December 2025, per VOV. The Traffic Police run it, in VOV's words, “to ensure security and order and support the people” as well as to manage traffic. Tracing a lost resident for a ward police search falls within that stated remit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-12-16", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six Vietnamese reports of 17 December 2025. Kept at benefit created or restored as the classifier proposed: the camera trace is documented, but the man was found by a passing resident and no source links the finder to the trace or the appeal, so no prevented harm is evidenced. The rank Thiếu tá is rendered as Major. VOV reports that the 1,837-camera system was to begin official operation in December 2025, so this was among its first publicized cases. The Phụ Nữ Online link redirected to a tuoitre.vn mirror path when read on 20 October 2026; the original URL is cited. The 8:15 p.m. report time and the 3 a.m. find are consistent across VnExpress, VietNamNet, Tuổi Trẻ, Tiền Phong, VOV and Phụ Nữ Online.", "source": "https://oecd.ai/en/incidents/2025-12-16-205c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_thành_phố_Hà_Nội", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A dense AI camera network along the route the person walked, here 1,837 cameras across Hanoi, with footage searchable by appearance and direction of travel.", "A family report the same evening giving a starting point and a direction for the camera search.", "A ward police unit able to hand the search to the Traffic Police's camera-control team the same night." ], "must_hold": [ "The missing person stays within camera coverage. Here the trace ended at a route, not at the man, and a passer-by made the find.", "Someone acts on the trace or the appeal. The sources do not establish that link in this case.", "The vendor and the matching method are undisclosed, so the trace cannot be checked beyond the police account." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and six Vietnamese reports of 17 December 2025. Kept at benefit created or restored as the classifier proposed: the camera trace is documented, but the man was found by a passing resident and no source links the finder to the trace or the appeal, so no prevented harm is evidenced. The rank Thiếu tá is rendered as Major. VOV reports that the 1,837-camera system was to begin official operation in December 2025, so this was among its first publicized cases. The Phụ Nữ Online link redirected to a tuoitre.vn mirror path when read on 20 October 2026; the original URL is cited. The 8:15 p.m. report time and the 3 a.m. find are consistent across VnExpress, VietNamNet, Tuổi Trẻ, Tiền Phong, VOV and Phụ Nữ Online.", "source": "https://oecd.ai/en/incidents/2025-12-16-205c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://vi.wikipedia.org/wiki/Công_an_thành_phố_Hà_Nội", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / missing persons", "segment": "Missing elderly person traced on urban traffic cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Việt An / VnExpress (2025). Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội. VnExpress, 17 December 2025.", "title": "Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội", "url": "https://vnexpress.net/camera-ai-ho-tro-tim-cu-ong-di-lac-o-ha-noi-4994889.html", "date": "2025-12-17", "written_by": "independent", "quote": "rà soát, truy vết hành trình của cụ ông qua hệ thống camera AI" }, { "kind": "news report", "citation": "Đình Hiếu / VietNamNet (2025). Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội. VietNamNet, 17 December 2025.", "title": "Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội", "url": "https://vietnamnet.vn/canh-sat-trich-xuat-camera-ai-giup-truy-vet-tim-nguoi-dan-ong-di-lac-o-ha-noi-2473262.html", "date": "2025-12-17", "written_by": "independent", "quote": "Công an phường Thanh Xuân đã cử cán bộ phối hợp với Phòng CSGT tổ chức rà soát, truy vết hình ảnh từ hệ thống camera AI" }, { "kind": "news report", "citation": "Hồng Quang / Tuổi Trẻ Online (2025). Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố. Tuổi Trẻ, 17 December 2025.", "title": "Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố", "url": "https://tuoitre.vn/camera-ai-truy-vet-tim-cu-ong-di-lac-qua-nhieu-tuyen-pho-20251217102042429.htm", "date": "2025-12-17", "written_by": "independent", "quote": "Kết quả ban đầu thu được các hình ảnh cụ ông đi dọc tuyến đường Nguyễn Tuân" }, { "kind": "news report", "citation": "Thanh Hà / Tiền Phong (2025). Camera AI theo dấu cụ ông đi lạc ở Hà Nội. Báo điện tử Tiền Phong, 17 December 2025.", "title": "Camera AI theo dấu cụ ông đi lạc ở Hà Nội", "url": "https://tienphong.vn/camera-ai-theo-dau-cu-ong-di-lac-o-ha-noi-post1805284.tpo", "date": "2025-12-17", "written_by": "independent", "quote": "Đến gần 3h sáng 16/12, một người dân trong quá trình tham gia giao thông đã phát hiện cụ ông trong tình trạng hạ đường huyết" }, { "kind": "news report", "citation": "VOV (2025). Camera AI tìm người đi lạc ở Hà Nội. VOV.vn, 17 December 2025.", "title": "Camera AI tìm người đi lạc ở Hà Nội", "url": "https://vov.vn/xa-hoi/camera-ai-tim-nguoi-di-lac-o-ha-noi-post1254446.vov", "date": "2025-12-17", "written_by": "independent", "quote": "ứng dụng hệ thống camera AI trong công tác bảo đảm an ninh, trật tự và hỗ trợ nhân dân" }, { "kind": "news report", "citation": "Anh Ngọc / Phụ Nữ Online (2025). Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn. Phụ Nữ Online, 17 December 2025.", "title": "Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn", "url": "https://www.phunuonline.com.vn/tu-hinh-anh-qua-camera-ai-canh-sat-giup-cu-ong-di-lac-ve-nha-an-toan-a1569649.html", "date": "2025-12-17", "written_by": "independent", "quote": "Rà soát, truy vết hình ảnh từ hệ thống camera giao thông AI theo hướng di chuyển do người nhà cung cấp." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0152-source-0001", "AIBD-0152-source-0002", "AIBD-0152-source-0003", "AIBD-0152-source-0004", "AIBD-0152-source-0005", "AIBD-0152-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Việt An / VnExpress (2025). Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội. VnExpress, 17 December 2025.", "title": "Camera AI hỗ trợ tìm cụ ông đi lạc ở Hà Nội", "url": "https://vnexpress.net/camera-ai-ho-tro-tim-cu-ong-di-lac-o-ha-noi-4994889.html", "date": "2025-12-17", "written_by": "independent", "quote": "rà soát, truy vết hành trình của cụ ông qua hệ thống camera AI" }, { "kind": "news report", "citation": "Đình Hiếu / VietNamNet (2025). Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội. VietNamNet, 17 December 2025.", "title": "Cảnh sát trích xuất camera AI, giúp truy vết, tìm người đàn ông đi lạc ở Hà Nội", "url": "https://vietnamnet.vn/canh-sat-trich-xuat-camera-ai-giup-truy-vet-tim-nguoi-dan-ong-di-lac-o-ha-noi-2473262.html", "date": "2025-12-17", "written_by": "independent", "quote": "Công an phường Thanh Xuân đã cử cán bộ phối hợp với Phòng CSGT tổ chức rà soát, truy vết hình ảnh từ hệ thống camera AI" }, { "kind": "news report", "citation": "Hồng Quang / Tuổi Trẻ Online (2025). Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố. Tuổi Trẻ, 17 December 2025.", "title": "Camera AI truy vết tìm cụ ông đi lạc qua nhiều tuyến phố", "url": "https://tuoitre.vn/camera-ai-truy-vet-tim-cu-ong-di-lac-qua-nhieu-tuyen-pho-20251217102042429.htm", "date": "2025-12-17", "written_by": "independent", "quote": "Kết quả ban đầu thu được các hình ảnh cụ ông đi dọc tuyến đường Nguyễn Tuân" }, { "kind": "news report", "citation": "Thanh Hà / Tiền Phong (2025). Camera AI theo dấu cụ ông đi lạc ở Hà Nội. Báo điện tử Tiền Phong, 17 December 2025.", "title": "Camera AI theo dấu cụ ông đi lạc ở Hà Nội", "url": "https://tienphong.vn/camera-ai-theo-dau-cu-ong-di-lac-o-ha-noi-post1805284.tpo", "date": "2025-12-17", "written_by": "independent", "quote": "Đến gần 3h sáng 16/12, một người dân trong quá trình tham gia giao thông đã phát hiện cụ ông trong tình trạng hạ đường huyết" }, { "kind": "news report", "citation": "VOV (2025). Camera AI tìm người đi lạc ở Hà Nội. VOV.vn, 17 December 2025.", "title": "Camera AI tìm người đi lạc ở Hà Nội", "url": "https://vov.vn/xa-hoi/camera-ai-tim-nguoi-di-lac-o-ha-noi-post1254446.vov", "date": "2025-12-17", "written_by": "independent", "quote": "ứng dụng hệ thống camera AI trong công tác bảo đảm an ninh, trật tự và hỗ trợ nhân dân" }, { "kind": "news report", "citation": "Anh Ngọc / Phụ Nữ Online (2025). Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn. Phụ Nữ Online, 17 December 2025.", "title": "Từ hình ảnh qua camera AI, cảnh sát giúp cụ ông đi lạc về nhà an toàn", "url": "https://www.phunuonline.com.vn/tu-hinh-anh-qua-camera-ai-canh-sat-giup-cu-ong-di-lac-ve-nha-an-toan-a1569649.html", "date": "2025-12-17", "written_by": "independent", "quote": "Rà soát, truy vết hình ảnh từ hệ thống camera giao thông AI theo hướng di chuyển do người nhà cung cấp." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Hanoi police traced a lost 78-year-old on AI traffic cameras on 15 December 2025; a passer-by found him at 3 a.m. the next day and took him home.", "account": "Mr Đ.V.T., 78, of Thanh Xuân ward, Hanoi, went out to exercise at 3:30 p.m. on 15 December 2025 and did not return. At 8:15 p.m. his family reported him to Thanh Xuân Ward Police. Ward officers worked with the Hanoi Traffic Police Department. Major Nguyễn Tá Khanh's team there searched the AI traffic-camera system in the direction the family gave. It traced him along Nguyễn Tuân street toward Thanh Xuân Park and other streets. VietNamNet and Tiền Phong say the images were posted on social media. At about 3 a.m. on 16 December a passing resident found him in Dịch Vọng, hypoglycemic and close to fainting, and took him home. No source links the finder to the appeal." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Hanoi Traffic Police AI traffic-camera system (1,837 cameras)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Hanoi traffic police trace a missing 78-year-old on the city's new AI camera network; a passer-by finds him hypoglycemic in Dịch Vọng at 3 a.m.", "headline": "Hanoi traffic police trace a lost 78-year-old on AI cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "4f37900df8535e39dd84d65a5243a1198edcd7578b78db82935ccf2cb5eae3dc" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Mr Đ.V.T., 78, of Thanh Xuân ward, Hanoi, went out to exercise at 3:30 p.m. on 15 December 2025 and did not return. At 8:15 p.m. his family reported him to Thanh Xuân Ward Police. Ward officers worked with the Hanoi Traffic Police Department. Major Nguyễn Tá Khanh's team there searched the AI traffic-camera system in the direction the family gave. It traced him along Nguyễn Tuân street toward Thanh Xuân Park and other streets. VietNamNet and Tiền Phong say the images were posted on social media. At about 3 a.m. on 16 December a passing resident found him in Dịch Vọng, hypoglycemic and close to fainting, and took him home. No source links the finder to the appeal.", "replication": { "produced_by": [ "A dense AI camera network along the route the person walked, here 1,837 cameras across Hanoi, with footage searchable by appearance and direction of travel.", "A family report the same evening giving a starting point and a direction for the camera search.", "A ward police unit able to hand the search to the Traffic Police's camera-control team the same night." ], "must_hold": [ "The missing person stays within camera coverage. Here the trace ended at a route, not at the man, and a passer-by made the find.", "Someone acts on the trace or the appeal. The sources do not establish that link in this case.", "The vendor and the matching method are undisclosed, so the trace cannot be checked beyond the police account." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0342", "case_url": "records/AIBD-0342.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose." }, { "label": "AI contribution", "value": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Kazakhstan" ] }, "record": { "id": "AIBD-0342", "title": "UNEP MARS notification leads an operator to stop a 1,200-tonne methane pipeline leak in Kazakhstan (KAZ_S_136); IMEO observed emissions at the site again by May 2026", "summary": "UNEP IMEO case KAZ_S_136: pipeline leak first detected Sept 2025, operator rerouted gas, mitigation reported 26 Nov 2025, last emissions 12 Mar 2026; subsequent emissions seen as of 21 May 2026.", "date_observed": "2025-11-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed pipeline operator in Kazakhstan (mitigation)", "location": "Kazakhstan, 43.39767°, 52.49505° (no place name in the UNEP document)", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas transmission; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ], "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." }, "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": null, "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ], "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "model_version": null, "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "model_version": null, "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "model_version": null, "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] }, "paired_incident": null, "headline": "UNEP MARS alert stops Kazakhstan leak; emissions seen again", "metric": "estimated emissions of 1,200 tonnes", "comparator": null, "later": null, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the makers' case report, the makers' report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0342", "catalog_version": "1.0.0", "source_fingerprint": "a32e17efc837fdd8167a283df56b125c7814e01b21df66f2b2a10f3d8cba07fc", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0342-source-0001", "AIBD-0342-source-0002", "AIBD-0342-source-0003", "AIBD-0342-source-0004", "AIBD-0342-source-0005", "AIBD-0342-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed pipeline operator in Kazakhstan (mitigation)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "UNEP's case document records detection, notification, the operator's rerouting of gas and satellite confirmation. It names DLR's EnMAP for the plume image. The only description of an AI role is UNEP's general statement that IMEO analyzes satellite data with advanced AI models. The AI contribution to this specific detection is therefore not isolated. The sources are UNEP's own (system operator) plus trade press relaying UNEP. No operator or government statement is among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report, the makers' case report, the makers' report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "estimated emissions of 1,200 tonnes", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0342" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "benefit_type": "Reduced Carbon Emissions" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified. The benefit is an avoided greenhouse-gas release." }, { "group": "organizational", "depth": 2, "reason": "The operator stopped a leak of an estimated 1,200 tonnes of methane by rerouting gas after the notification, a material correction to its operations. The operator is unnamed and the leak was later observed again." }, { "group": "societal", "depth": 1, "reason": "One source among thousands of MARS alerts (the trade press counts over 3,500 alerts and about 43 confirmed cases since 2023). Climate benefit limited by the subsequent emissions observed in May 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kazakhstan, 43.39767°, 52.49505° (no place name in the UNEP document)", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Satellite-based detection and notification of large methane plumes so that operators and governments mitigate them. This case was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-11-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; 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facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas transmission; intergovernmental observatory" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0342-source-0001", "AIBD-0342-source-0002", "AIBD-0342-source-0003", "AIBD-0342-source-0004", "AIBD-0342-source-0005", "AIBD-0342-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2026). Methane Alert and Response System (MARS) case document, Kazakhstan KAZ_S_136 (pipeline), updated 15 Apr 2026. wedocs.unep.org.", "title": "MARS case KAZ_S_136, Kazakhstan", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, footnote on subsequent emissions.", "title": "MARS case KAZ_S_136, Kazakhstan (subsequent emissions footnote)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "As of 21 May 2026, IMEO observed subsequent methane emissions at this location." }, { "kind": "case report", "citation": "UNEP IMEO (2026). Same case document, image caption naming the detecting instrument.", "title": "MARS case KAZ_S_136, Kazakhstan (detection)", "url": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download", "date": "2026-04-15", "written_by": "makers", "quote": "The image shows a plume detected by DLR's EnMAP in Kazakhstan on 29 October 2025." }, { "kind": "news report", "citation": "Baker, S. (2026). UN satellite programme curbs more than 40 methane 'super-emitters' since 2023. The Chemical Engineer, 5 May 2026.", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "date": "2026-05-05", "written_by": "independent", "quote": "one each in Libya, Iraq and Kazakhstan" }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (the snapshot gives no case ID for the Kazakhstan mitigation).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "written_by": "makers", "quote": "A gas pipeline leak resulted in the release of an estimated 365 tonnes of methane over the course of one month." }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "Using data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models, IMEO notifies governments and companies of emissions" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "UNEP IMEO case KAZ_S_136: pipeline leak first detected Sept 2025, operator rerouted gas, mitigation reported 26 Nov 2025, last emissions 12 Mar 2026; subsequent emissions seen as of 21 May 2026.", "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP MARS notification leads an operator to stop a 1,200-tonne methane pipeline leak in Kazakhstan (KAZ_S_136); IMEO observed emissions at the site again by May 2026", "headline": "UNEP MARS alert stops Kazakhstan leak; emissions seen again" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a32e17efc837fdd8167a283df56b125c7814e01b21df66f2b2a10f3d8cba07fc" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's International Methane Emissions Observatory (IMEO) publishes a Methane Alert and Response System (MARS) case document for source KAZ_S_136. It is an oil-and-gas pipeline in Kazakhstan at 43.39767°, 52.49505°. The document gives coordinates only. The document records first detection in September 2025, a plume imaged by DLR's EnMAP on 29 October 2025 and estimated emissions of 1,200 tonnes. The operator transferred the gas flow from the affected pipeline to another pipeline, stopping the emissions. Mitigation was reported on 26 November 2025. Last emissions were detected on 12 March 2026 (document updated 15 April 2026). A footnote states that as of 21 May 2026 IMEO observed subsequent methane emissions at the location and continues to engage stakeholders. UNEP's MARS page states that IMEO uses data from more than 30 satellite instruments coupled with scientific expertise and advanced AI models. It uses them to notify governments and companies. The Chemical Engineer (5 May 2026), relaying UNEP's snapshot, counts one Kazakhstan source among six that stopped emitting in 2026 after MARS alerts. UNEP's September 2026 snapshot reports a confirmed Kazakhstan gas-pipeline mitigation of an estimated 365 tonnes over one month. Neither gives a case ID, so their correspondence to KAZ_S_136 is not established.", "replication": { "produced_by": [ "Satellite plume detection (EnMAP image of 29 October 2025) analyzed by IMEO", "A MARS notification reaching an operator able to reroute gas flow", "Follow-up satellite observation to confirm cessation" ], "must_hold": [ "A country focal point or operator that answers notifications (UNEP's snapshot says most mitigation occurs where focal points direct information)", "A source type that can be isolated quickly (pipeline rerouting)", "Sustained repair: at this site emissions were observed again by 21 May 2026" ], "echoes": [ { "date": "2026-05-21", "setting": "Same site KAZ_S_136: IMEO observed subsequent methane emissions after the confirmed mitigation", "outcome": "did not recur", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2024-01-01", "setting": "Hassi Messaoud, Algeria (DZA_S_012): MARS notification followed by repair of a decades-old source (existing record AIBD-0091)", "outcome": "recurred", "source": "AIBD-0091" }, { "date": "2026-04-01", "setting": "Aerial field, Alberta, Canada: ball-valve repair one week after detection, per trade press relaying UNEP's August 2026 snapshot", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0237", "case_url": "records/AIBD-0237.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "AI-powered static analysis of source code to find vulnerabilities and bugs." }, { "label": "AI contribution", "value": "The maintainer confirms the reports' quality and the merged fixes. He notes he does not know the exact split between tool and human. Rogers attributes ~98% of the fixed curl bugs to ZeroPath. Both the deployer and the confirming maintainer are independent of the vendor." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0237", "title": "curl merges about 50 bugfixes from Joshua Rogers' ZeroPath-assisted scans; maintainer calls the reports a new breed of analyzer output", "summary": "In Sept 2025 curl received hundreds of candidate issues found mainly with the ZeroPath AI analyzer by Joshua Rogers; maintainer Daniel Stenberg merged about 50 bugfixes from the first list.", "date_observed": "2025-10-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The reports arrived from mid-September 2025. Stenberg writes that a security report received on 16 September accurately identified a possible stack-based buffer overflow in curl's Kerberos FTP code. It was not turned into a CVE only because the code could not be reached. The project then removed krb5-ftp support. The reporter, Joshua Rogers, then forwarded a large list of further potential issues found mostly with ZeroPath, an AI-powered code analyzer. That produced over two hundred possible problems despite curl's existing compilers, static analyzers and fuzzing. From the initial list about 50 separately identifiable bugfixes were merged. Second and third lists of 47 and 158 items followed. Stenberg told The Register that about 50 bugfixes based on Rogers' reports had been merged. Rogers' retrospective states that about 98% of the curl bugs reported and fixed were discovered by ZeroPath. 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The merged fixes are described as mostly small mistakes." } ], "ai_role": { "status": "assessed", "statement": "The maintainer confirms the reports' quality and the merged fixes. He notes he does not know the exact split between tool and human. Rogers attributes ~98% of the fixed curl bugs to ZeroPath. 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external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A large, mature C codebase already covered by conventional analyzers and fuzzing", "An expert researcher filtering scanner output before reporting", "Maintainers willing to work through long candidate lists" ], "must_hold": [ "Human triage to remove false positives", "Maintainer capacity to review hundreds of items" ], "echoes": [ { "date": "2025-09-23", "setting": "Aisle's AI-powered pipeline (Stanislav Fort) reports a confirmed curl vulnerability", "outcome": "recurred", "source": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/; https://joshua.hu/retrospective-zeropath-ai-sast-source-code-security-scanners-vulnerability", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://zeropath.com/privacy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Cybersecurity / open-source software quality", "segment": "Technology" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Daniel Stenberg, daniel.haxx.se, 10 Oct 2025", "title": "A new breed of analyzers", "url": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/", "date": "2025-10-10", "written_by": "beneficiary", "quote": "Out of that initial list, we merged about 50 separately identifiable bugfixes." }, { "kind": "news report", "citation": "Thomas Claburn, The Register, 2 Oct 2025", "title": "Curl project, swamped with AI slop, finds not all AI is bad", "url": "https://www.theregister.com/software/2025/10/02/curl-project-swamped-with-ai-slop-finds-not-all-ai-is-bad/588432", "date": "2025-10-02", "written_by": "independent", "quote": "Stenberg told The Register that about 50 bugfixes based on Rogers' reports have been merged." }, { "kind": "report", "citation": "Joshua Rogers, personal blog (retrospective), 2025", "title": "Retrospective: AI-powered security engineers and source code scanners", "url": "https://joshua.hu/retrospective-zeropath-ai-sast-source-code-security-scanners-vulnerability", "written_by": "deployer", "quote": "Approximately 98% of the bugs reported and fixed in the curl codebase were discovered by ZeroPath." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0237-source-0001", "AIBD-0237-source-0002", "AIBD-0237-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Daniel Stenberg, daniel.haxx.se, 10 Oct 2025", "title": "A new breed of analyzers", "url": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/", "date": "2025-10-10", "written_by": "beneficiary", "quote": "Out of that initial list, we merged about 50 separately identifiable bugfixes." }, { "kind": "news report", "citation": "Thomas Claburn, The Register, 2 Oct 2025", "title": "Curl project, swamped with AI slop, finds not all AI is bad", "url": "https://www.theregister.com/software/2025/10/02/curl-project-swamped-with-ai-slop-finds-not-all-ai-is-bad/588432", "date": "2025-10-02", "written_by": "independent", "quote": "Stenberg told The Register that about 50 bugfixes based on Rogers' reports have been merged." }, { "kind": "report", "citation": "Joshua Rogers, personal blog (retrospective), 2025", "title": "Retrospective: AI-powered security engineers and source code scanners", "url": "https://joshua.hu/retrospective-zeropath-ai-sast-source-code-security-scanners-vulnerability", "written_by": "deployer", "quote": "Approximately 98% of the bugs reported and fixed in the curl codebase were discovered by ZeroPath." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "In Sept 2025 curl received hundreds of candidate issues found mainly with the ZeroPath AI analyzer by Joshua Rogers; maintainer Daniel Stenberg merged about 50 bugfixes from the first list.", "account": "The reports arrived from mid-September 2025. Stenberg writes that a security report received on 16 September accurately identified a possible stack-based buffer overflow in curl's Kerberos FTP code. It was not turned into a CVE only because the code could not be reached. The project then removed krb5-ftp support. The reporter, Joshua Rogers, then forwarded a large list of further potential issues found mostly with ZeroPath, an AI-powered code analyzer. That produced over two hundred possible problems despite curl's existing compilers, static analyzers and fuzzing. From the initial list about 50 separately identifiable bugfixes were merged. Second and third lists of 47 and 158 items followed. Stenberg told The Register that about 50 bugfixes based on Rogers' reports had been merged. Rogers' retrospective states that about 98% of the curl bugs reported and fixed were discovered by ZeroPath. It puts the running total of fixed bugs around 150." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ZeroPath AI code analyzer (with other scanners tested by Rogers)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "ZeroPath" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "curl merges about 50 bugfixes from Joshua Rogers' ZeroPath-assisted scans; maintainer calls the reports a new breed of analyzer output", "headline": "curl merges about 50 bugfixes from ZeroPath-assisted scans" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0ab5f9fe26d8b28b6cc01d061326b1f7281c807d2f8b66f6ae744af8a0a80b2c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The reports arrived from mid-September 2025. Stenberg writes that a security report received on 16 September accurately identified a possible stack-based buffer overflow in curl's Kerberos FTP code. It was not turned into a CVE only because the code could not be reached. The project then removed krb5-ftp support. The reporter, Joshua Rogers, then forwarded a large list of further potential issues found mostly with ZeroPath, an AI-powered code analyzer. That produced over two hundred possible problems despite curl's existing compilers, static analyzers and fuzzing. From the initial list about 50 separately identifiable bugfixes were merged. Second and third lists of 47 and 158 items followed. Stenberg told The Register that about 50 bugfixes based on Rogers' reports had been merged. Rogers' retrospective states that about 98% of the curl bugs reported and fixed were discovered by ZeroPath. It puts the running total of fixed bugs around 150.", "replication": { "produced_by": [ "A large, mature C codebase already covered by conventional analyzers and fuzzing", "An expert researcher filtering scanner output before reporting", "Maintainers willing to work through long candidate lists" ], "must_hold": [ "Human triage to remove false positives", "Maintainer capacity to review hundreds of items" ], "echoes": [ { "date": "2025-09-23", "setting": "Aisle's AI-powered pipeline (Stanislav Fort) reports a confirmed curl vulnerability", "outcome": "recurred", "source": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0148", "case_url": "records/AIBD-0148.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use." }, { "label": "AI contribution", "value": "By the operators' account, the Veesion analysis flagged the shopper's concealment gesture in real time and alerted the remote dispatcher. The camera footage let police catch the perpetrator within hours. No police statement, loss figure or description of what the dispatcher did at the moment of the alert was found. Whether the theft was stopped in progress or only documented, and whether the goods came back, is unknown. The claim rests on a joint press release by the deployer and its integrator, relayed by independent outlets." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Hungary" ] }, "record": { "id": "AIBD-0148", "title": "Veesion gesture-recognition cameras in Coop Szeged's 247 Shop on Szent István tér, unstaffed at night, may have flagged a shopper hiding goods under clothing and led police to the perpetrator within hours, one of three attempts exposed since May 2025", "summary": "AI cameras in Szeged's unstaffed-at-night Coop flagged a shopper hiding goods; police caught them within hours, the operators said.", "date_observed": "2025-10-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "category": "protective response", "unexpected": false, "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use.", "system": "Veesion AI gesture-recognition camera analysis integrated into Laurel Kft.'s 247 Shop hybrid-store platform, with alerts to a remote dispatch service", "model_version": "Not reported", "developer": "Veesion", "deployer": "Coop Szeged Zrt. (store operator), with Laurel Kft. as technology partner", "location": "Szeged, Hungary", "domain": "Retail loss prevention", "segment": "Grocery retail", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-10-09-70ff" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ], "ai_role": { "status": "unassessed", "statement": "By the operators' account, the Veesion analysis flagged the shopper's concealment gesture in real time and alerted the remote dispatcher. The camera footage let police catch the perpetrator within hours. No police statement, loss figure or description of what the dispatcher did at the moment of the alert was found. Whether the theft was stopped in progress or only documented, and whether the goods came back, is unknown. The claim rests on a joint press release by the deployer and its integrator, relayed by independent outlets." }, "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] }, "paired_incident": null, "headline": "Veesion alert may have led police to a Szeged shoplifter", "metric": "over four and a half months the system had exposed three theft attempts", "comparator": { "against": "inventory shrinkage under the store's previous staffed-only opening hours", "before": null, "after": "nem nőtt meg a leltárhiány", "basis": "measured", "quote": "Tájékoztatásuk szerint a két cég együttműködésével elindult szegedi üzletben négy és fél hónap alatt már 8500-an vásároltak az éjszakai, automata rendszerben, és az egész naposra kiterjesztett nyitvatartással járó jelentős forgalomtöbblet dacára nem nőtt meg a leltárhiány.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176" }, "later": { "status": "scaled", "date": "2026-09-30", "note": "Coop Szeged and Laurel opened a second staffless-after-hours store, a full supermarket on Temesvári körút, on 30 September 2026, after more than 27,000 unstaffed purchases in a year at the first store; the report describes camera monitoring and a remote dispatch service but does not name Veesion.", "quote": "most pedig ugyanebben a városban, a Temesvári körúton nyílt meg a második, ami immáron a méreteit tekintve egy kész szupermarket.", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/" }, "value": null, "developer_country": "France", "deployer_country": "Hungary", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0148", "catalog_version": "1.0.0", "source_fingerprint": "426c9b02e8e63eb5fd423b6a1cf78ec516aaabb2e070563d9f80bd9468b3faa2", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0148-source-0001", "AIBD-0148-source-0002", "AIBD-0148-source-0003", "AIBD-0148-source-0004", "AIBD-0148-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Veesion", "deployer": "Coop Szeged Zrt. (store operator), with Laurel Kft. as technology partner" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "By the operators' account, the Veesion analysis flagged the shopper's concealment gesture in real time and alerted the remote dispatcher. The camera footage let police catch the perpetrator within hours. No police statement, loss figure or description of what the dispatcher did at the moment of the alert was found. Whether the theft was stopped in progress or only documented, and whether the goods came back, is unknown. The claim rests on a joint press release by the deployer and its integrator, relayed by independent outlets." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "over four and a half months the system had exposed three theft attempts", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-10-09-70ff" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-09-30", "note": "Coop Szeged and Laurel opened a second staffless-after-hours store, a full supermarket on Temesvári körút, on 30 September 2026, after more than 27,000 unstaffed purchases in a year at the first store; the report describes camera monitoring and a remote dispatch service but does not name Veesion.", "quote": "most pedig ugyanebben a városban, a Temesvári körúton nyílt meg a második, ami immáron a méreteit tekintve egy kész szupermarket.", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0148" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The store operator gained a flagged concealment and footage that led police to the perpetrator within hours. Three attempts were exposed over four and a half months of unstaffed night trading. The operators say shrinkage did not rise. All figures are the companies' own. No loss amount or recovery is reported." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Szeged, Hungary", "developer_country": "France", "deployer_country": "Hungary" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Veesion's gesture-recognition software is built to spot shoplifting movements such as concealing goods and to raise an immediate alert. In Laurel's 247 Shop it is the loss-prevention layer for unstaffed operation. Flagging a concealment was its intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-10-09", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-10-09-70ff", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176; https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://veesion.io/mentions-legales/; https://www.coopszeged.hu/kapcsolat", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Retail loss prevention", "segment": "Grocery retail" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0148-source-0001", "AIBD-0148-source-0002", "AIBD-0148-source-0003", "AIBD-0148-source-0004", "AIBD-0148-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Simon B K (2025). Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni. Index.hu, 9 October 2025, 20:39; the article says its facts are 'olvasható a sajtóközleményben', i.e. taken from the companies' press release.", "title": "Tolvajokat buktatott le az AI, az első magyar okosboltból próbáltak meg árut kicsenni", "url": "https://index.hu/gazdasag/2025/10/09/magyar-okosbolt-szeged-ai-lopas-lebuktatas/", "date": "2025-10-09", "written_by": "independent", "quote": "a rendőrség a kamerafelvételek alapján néhány órán belül elfogta az elkövetőt" }, { "kind": "news report", "citation": "Mínuszos.hu (2025). AI leplezett le tolvajokat a szegedi hibrid okosboltban. Mínuszos.hu, 11 October 2025; names Veesion as a French company and the store as the Szent István tér Coop opened at the end of May.", "title": "AI leplezett le tolvajokat a szegedi hibrid okosboltban", "url": "https://www.minuszos.hu/ai-leplezett-le-tolvajokat-a-szegedi-hibrid-okosboltban/", "date": "2025-10-11", "written_by": "independent", "quote": "A mesterséges intelligenciával (AI) támogatott rendszer az elmúlt hónapokban három lopási kísérletet is sikeresen azonosított és akadályozott meg." }, { "kind": "news report", "citation": "ProfitLine.hu / MTI (2025). Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt. ProfitLine.hu, 11 October 2025, carrying the MTI agency report of the companies' statement; gives 150 square metres, 4,000 SKUs, and quotes István Bessenyei (Laurel) and János Kelemen (Coop Szeged).", "title": "Újabb hibrid okosboltok kialakítását tervezi a Coop Szeged Zrt.", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176", "date": "2025-10-11", "written_by": "independent", "quote": "az arcok azonosítása nélkül elemzi a vásárlói mozgásokat, és azonnal jelzi a távoli diszpécserszolgálatnak a gyanús viselkedést" }, { "kind": "news report", "citation": "Turizmus Online / MTI (2025). A szegediek szeretik az okosboltokat – újabbakat nyitnak. Turizmus Online, 13 October 2025; reports 8,500 purchases in four and a half months and no rise in shrinkage despite the longer hours, per the operators.", "title": "A szegediek szeretik az okosboltokat – újabbakat nyitnak", "url": "https://turizmusonline.hu/cikkek/gasztro/a-szegediek-szeretik-az-okosboltokat--ujabbakat-nyitnak.html", "date": "2025-10-13", "written_by": "independent", "quote": "az új típusú bolt az indulás óta stabilan, a várakozások felett teljesít" }, { "kind": "news report", "citation": "Economx (2026). Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket. economx.hu, 30 September 2026.", "title": "Nincs pénztáros, nincs eladó: megnyílt az első magyar személyzet nélküli szupermarket", "url": "https://www.economx.hu/vallalatok/2026/09/30/coop-szupermarket-szeged-szemelyzet-nelkuli-laurel/", "date": "2026-09-30", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AI cameras in Szeged's unstaffed-at-night Coop flagged a shopper hiding goods; police caught them within hours, the operators said.", "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Not reported" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Veesion AI gesture-recognition camera analysis integrated into Laurel Kft.'s 247 Shop hybrid-store platform, with alerts to a remote dispatch service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Veesion" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Veesion gesture-recognition cameras in Coop Szeged's 247 Shop on Szent István tér, unstaffed at night, may have flagged a shopper hiding goods under clothing and led police to the perpetrator within hours, one of three attempts exposed since May 2025", "headline": "Veesion alert may have led police to a Szeged shoplifter" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "426c9b02e8e63eb5fd423b6a1cf78ec516aaabb2e070563d9f80bd9468b3faa2" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Coop Szeged Zrt. and Laurel Kft. opened the 247 Shop on Szent István tér in Szeged at the end of May 2025. It is staffed by day, automated and unstaffed outside normal hours. Cameras running the French company Veesion's software analyze shoppers' movements without identifying faces. They alert a remote dispatch service to gestures such as concealing goods. On 9 October 2025 the operators issued a press release relayed by Index.hu and MTI. It said over four and a half months the system had exposed three theft attempts. In one, a person hid products under their clothing during night mode. Police caught the perpetrator within hours using the footage. Dates, recovered goods and any police statement are not reported.", "replication": { "produced_by": [ "Gesture-recognition analysis watching every shopper during the hours when no staff are present, tuned to concealment movements rather than faces", "A remote dispatch service that receives the alert immediately, and camera footage that can be handed to police", "A sales floor small enough (150 square meters, 4,000 SKUs) for the cameras to cover it entirely" ], "must_hold": [ "The flagged gesture is a real theft rather than an innocent movement. The operators report no false-alarm figures", "Police are willing and able to act on the footage quickly, here within hours", "The system keeps analyzing movements without identifying faces, as the operators describe it" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0225", "case_url": "records/AIBD-0225.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "Actively search for and find unknown security vulnerabilities in software using a large language model (LLM) agent (Google DeepMind and Project Zero)." }, { "label": "AI contribution", "value": "Per Google's spokesperson, human threat analysts supplied limited indicators and Big Sleep isolated the specific vulnerability. The CVE credit names a human finder “with assistance from Google Big Sleep”. Developer and the organization reporting the benefit are the same company. The Record and the SQLite release log are the independent sources. Neither confirms the threat-actor staging." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0225", "title": "Google's Big Sleep agent may have isolated SQLite CVE-2025–6965 from threat-intel artifacts; fixed in 3.50.2 before exploitation", "summary": "Google says Big Sleep, fed indicators that attackers were staging a zero-day, isolated SQLite CVE-2025–6965; SQLite shipped the fix in 3.50.2 on 28 June 2025, announced 15 July 2025.", "date_observed": "2025-07-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The SQLite 3.50.2 release carrying the fix is dated 28 June 2025. The CVE record was published 15 July 2025. Google's post says that, based on intel from Google Threat Intelligence, the Big Sleep agent discovered an SQLite vulnerability known only to threat actors. It was at risk of being exploited, the post says. A Google spokesperson told The Record that its threat intelligence group found artifacts indicating a zero-day was being staged but could not identify the vulnerability. Big Sleep was used to isolate it, the spokesperson said. Google declined to name the actors or describe the indicators. The CVE record, assigned by Google, credits Vlad Stolyarov of Google's Threat Analysis Group with assistance from Google Big Sleep. SQLite's release log lists the corresponding fix. Independent confirmation that exploitation was imminent is absent. 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The foiled-exploitation claim is Google's." } ], "ai_role": { "status": "assessed", "statement": "Per Google's spokesperson, human threat analysts supplied limited indicators and Big Sleep isolated the specific vulnerability. The CVE credit names a human finder “with assistance from Google Big Sleep”. Developer and the organization reporting the benefit are the same company. The Record and the SQLite release log are the independent sources. 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The foiled-exploitation claim is Google's." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Actively search for and find unknown security vulnerabilities in software using a large language model (LLM) agent (Google DeepMind and Project Zero)." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer_country": "United Kingdom", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Cybersecurity / vulnerability research", "segment": "Technology" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Jonathan Greig, The Record from Recorded Future News, 15 Jul 2025", "title": "Google says ‘Big Sleep’ AI tool found bug hackers planned to use", "url": "https://therecord.media/google-big-sleep-ai-tool-found-bug", "date": "2025-07-15", "written_by": "independent", "quote": "who leveraged Big Sleep to isolate the vulnerability the adversary was preparing to exploit in their operations" }, { "kind": "registry label", "citation": "CVE Program record CVE-2025-6965 (CNA: Google), published 15 Jul 2025", "title": "CVE-2025-6965", "url": "https://www.cve.org/CVERecord?id=CVE-2025-6965", "date": "2025-07-15", "written_by": "makers", "quote": "Vlad Stolyarov of Google's Threat Analysis Group, with assistance from Google Big Sleep" }, { "kind": "other", "citation": "SQLite release log, version 3.50.2, 28 Jun 2025", "title": "SQLite Release 3.50.2 On 2025-06-28", "url": "https://sqlite.org/releaselog/3_50_2.html", "date": "2025-06-28", "written_by": "independent", "quote": "Raise an error early if the number of aggregate terms in a query exceeds the maximum number of columns, to avoid downstream assertion faults." }, { "kind": "press release", "citation": "Kent Walker, Google blog, 15 Jul 2025", "title": "A summer of security: empowering cyber defenders with AI", "url": "https://blog.google/technology/safety-security/cybersecurity-updates-summer-2025/", "date": "2025-07-15", "written_by": "makers", "quote": "Google was able to actually predict that a vulnerability was imminently going to be used and we were able to cut it off beforehand." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0225-source-0001", "AIBD-0225-source-0002", "AIBD-0225-source-0003", "AIBD-0225-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Jonathan Greig, The Record from Recorded Future News, 15 Jul 2025", "title": "Google says ‘Big Sleep’ AI tool found bug hackers planned to use", "url": "https://therecord.media/google-big-sleep-ai-tool-found-bug", "date": "2025-07-15", "written_by": "independent", "quote": "who leveraged Big Sleep to isolate the vulnerability the adversary was preparing to exploit in their operations" }, { "kind": "registry label", "citation": "CVE Program record CVE-2025-6965 (CNA: Google), published 15 Jul 2025", "title": "CVE-2025-6965", "url": "https://www.cve.org/CVERecord?id=CVE-2025-6965", "date": "2025-07-15", "written_by": "makers", "quote": "Vlad Stolyarov of Google's Threat Analysis Group, with assistance from Google Big Sleep" }, { "kind": "other", "citation": "SQLite release log, version 3.50.2, 28 Jun 2025", "title": "SQLite Release 3.50.2 On 2025-06-28", "url": "https://sqlite.org/releaselog/3_50_2.html", "date": "2025-06-28", "written_by": "independent", "quote": "Raise an error early if the number of aggregate terms in a query exceeds the maximum number of columns, to avoid downstream assertion faults." }, { "kind": "press release", "citation": "Kent Walker, Google blog, 15 Jul 2025", "title": "A summer of security: empowering cyber defenders with AI", "url": "https://blog.google/technology/safety-security/cybersecurity-updates-summer-2025/", "date": "2025-07-15", "written_by": "makers", "quote": "Google was able to actually predict that a vulnerability was imminently going to be used and we were able to cut it off beforehand." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Google says Big Sleep, fed indicators that attackers were staging a zero-day, isolated SQLite CVE-2025–6965; SQLite shipped the fix in 3.50.2 on 28 June 2025, announced 15 July 2025.", "account": "The SQLite 3.50.2 release carrying the fix is dated 28 June 2025. The CVE record was published 15 July 2025. Google's post says that, based on intel from Google Threat Intelligence, the Big Sleep agent discovered an SQLite vulnerability known only to threat actors. It was at risk of being exploited, the post says. A Google spokesperson told The Record that its threat intelligence group found artifacts indicating a zero-day was being staged but could not identify the vulnerability. Big Sleep was used to isolate it, the spokesperson said. Google declined to name the actors or describe the indicators. The CVE record, assigned by Google, credits Vlad Stolyarov of Google's Threat Analysis Group with assistance from Google Big Sleep. SQLite's release log lists the corresponding fix. Independent confirmation that exploitation was imminent is absent. The claim rests on Google." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Big Sleep" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "DeepMind and Google Project Zero" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Big Sleep agent may have isolated SQLite CVE-2025–6965 from threat-intel artifacts; fixed in 3.50.2 before exploitation", "headline": "Big Sleep may have isolated SQLite zero-day CVE-2025–6965" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "47231bc70e9d37ed9a559b05a9b21f33873fdd7ae329ca0d2869200f920f1727" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The SQLite 3.50.2 release carrying the fix is dated 28 June 2025. The CVE record was published 15 July 2025. Google's post says that, based on intel from Google Threat Intelligence, the Big Sleep agent discovered an SQLite vulnerability known only to threat actors. It was at risk of being exploited, the post says. A Google spokesperson told The Record that its threat intelligence group found artifacts indicating a zero-day was being staged but could not identify the vulnerability. Big Sleep was used to isolate it, the spokesperson said. Google declined to name the actors or describe the indicators. The CVE record, assigned by Google, credits Vlad Stolyarov of Google's Threat Analysis Group with assistance from Google Big Sleep. SQLite's release log lists the corresponding fix. Independent confirmation that exploitation was imminent is absent. The claim rests on Google.", "replication": { "produced_by": [ "Threat-intelligence artifacts narrowing the target without identifying the bug", "An LLM agent with code navigation, sandboxed execution and debugging tools pointed at a specific codebase", "A responsive upstream maintainer shipping a fix before disclosure" ], "must_hold": [ "Partial indicators of a staged exploit must exist and be shared with the agent's operators", "Target code must be small enough for the agent to reason over", "Coordinated disclosure before the actor's exploitation window" ], "echoes": [ { "date": "2024-11-04", "setting": "Big Sleep's first real-world find: a stack buffer underflow in a SQLite development branch, fixed in October 2024", "outcome": "recurred", "source": "https://thehackernews.com/2024/11/googles-ai-tool-big-sleep-finds-zero.html" }, { "date": "2025-08-11", "setting": "Big Sleep team reports a curl vulnerability later published as CVE-2025–9086, confirmed by the curl maintainer", "outcome": "recurred", "source": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0384", "case_url": "records/AIBD-0384.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Built to confirm the identity of a person an officer suspects is missing, at risk or wanted when they cannot or will not give details. This identification was that use." }, { "label": "AI contribution", "value": "The app compared a photograph taken on a police phone to the reference database and returned the match that identified the man. Officers then confirmed the warrant and detained him. The force says National Physical Laboratory testing found the correct match returned as the top result for persons in the database. It says the app returns nothing for people not in it. Neither report names the vendor, the algorithm or a match score. Both rest on the force's figures." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0384", "title": "South Wales Police's Operator Initiated Facial Recognition app identifies a 38-year-old in Cardiff wanted on a 2012 European arrest warrant from Poland; he is remanded to Westminster magistrates", "summary": "A South Wales Police officer's OIFR phone app matched a 38-year-old in Cardiff to a 2012 Polish European arrest warrant for drug dealing and assaulting an officer; he was remanded (July 2025).", "date_observed": "2025-07-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "South Wales Police and Gwent Police are the first UK forces with the Operator Initiated Facial Recognition (OIFR) app. It lets an officer photograph a person on a police phone and compare the image with a reference database. It is for when the person refuses or gives false details. It launched in December 2024. More than 500 officers had been trained by July 2025 (Emergency Services Times, 15 July 2025). In one use, an officer in Cardiff identified a 38-year-old man wanted on a European arrest warrant since 2012. The warrant was for drug dealing and assaulting a police officer with a weapon in Poland. He was remanded in custody to Westminster magistrates' court (Wales Online, 14 July 2025). The forces report 50 South Wales searches producing 10 arrests, and six Gwent uses with three matches and one arrest. They cite National Physical Laboratory testing in which the correct match came top for people in the database (Wales Online). The arrest date is not given.", "category": "benefit created", "unexpected": false, "purpose": "Built to confirm the identity of a person an officer suspects is missing, at risk or wanted when they cannot or will not give details. This identification was that use.", "system": "Operator Initiated Facial Recognition (OIFR) mobile app, South Wales Police and Gwent Police", "model_version": null, "developer": null, "deployer": "South Wales Police (with Gwent Police)", "location": "Cardiff, United Kingdom", "domain": "Law enforcement / identity verification", "segment": "Street identity checks of people who refuse or give false details", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-07-14-7341" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "South Wales Police identified in Cardiff a man wanted on a 2012 European arrest warrant and remanded him. It reports 10 arrests from 50 app searches. A measurable result for the forces, on their own figures as relayed by the press." }, { "group": "societal", "depth": 1, "reason": "One man wanted for drug dealing and assaulting an officer with a weapon in Poland was taken into custody for extradition proceedings. The reports give no further harm prevented. So the public benefit is the execution of a warrant." } ], "ai_role": { "status": "assessed", "statement": "The app compared a photograph taken on a police phone to the reference database and returned the match that identified the man. Officers then confirmed the warrant and detained him. The force says National Physical Laboratory testing found the correct match returned as the top result for persons in the database. It says the app returns nothing for people not in it. Neither report names the vendor, the algorithm or a match score. Both rest on the force's figures." }, "evidence": [ { "kind": "news report", "citation": "Dalling, R. (2025). Man wanted since 2012 arrested after facial recognition app match. Wales Online, July 14, 2025.", "title": "Man wanted since 2012 arrested after facial recognition app match", "url": "https://www.walesonline.co.uk/news/wales-news/man-wanted-2012-arrested-after-32057351", "date": "2025-07-14", "written_by": "independent", "quote": "The 38-year-old man was remanded in custody to Westminster magistrates' court." }, { "kind": "news report", "citation": "Devonshire, J. (2025). More than 500 officers in Wales now trained to use real-time facial recognition app. Emergency Services Times, July 15, 2025.", "title": "More than 500 officers in Wales now trained to use real-time facial recognition app", "url": "https://emergencyservicestimes.com/2025/07/15/more-than-500-officers-in-wales-now-trained-to-use-real-time-facial-recognition-app/", "date": "2025-07-15", "written_by": "independent", "quote": "Thanks to the app, he was swiftly identified, remanded in custody, and brought before Westminster Magistrates' Court." }, { "kind": "registry label", "citation": "OECD.AI (2025). Facial Recognition AI App Leads to Arrest of Fugitive Drug Dealer After 13 Years. OECD AI Incidents and Hazards Monitor, 2025-07-14.", "title": "Facial Recognition AI App Leads to Arrest of Fugitive Drug Dealer After 13 Years", "url": "https://oecd.ai/en/incidents/2025-07-14-7341", "date": "2025-07-14", "written_by": "independent", "quote": "used a new AI-powered facial recognition app to identify and arrest a fugitive drug dealer wanted since 2012" } ], "replication": { "produced_by": [ "A street encounter in which the person refuses or gives false details", "A phone app linked to a reference database that holds the wanted person's image", "A warrant record current enough for officers to act on the match" ], "must_hold": [ "The app is used after an attempted engagement and only where necessary and proportionate, per the force's policy", "The match is confirmed against the warrant before detention" ], "echoes": [] }, "paired_incident": null, "headline": "Police phone app identifies man wanted since 2012 in Cardiff", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-07-14-7341", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/South_Wales_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. 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UK forces with the Operator Initiated Facial Recognition (OIFR) app. It lets an officer photograph a person on a police phone and compare the image with a reference database. It is for when the person refuses or gives false details. It launched in December 2024. More than 500 officers had been trained by July 2025 (Emergency Services Times, 15 July 2025). In one use, an officer in Cardiff identified a 38-year-old man wanted on a European arrest warrant since 2012. The warrant was for drug dealing and assaulting a police officer with a weapon in Poland. He was remanded in custody to Westminster magistrates' court (Wales Online, 14 July 2025). The forces report 50 South Wales searches producing 10 arrests, and six Gwent uses with three matches and one arrest. They cite National Physical Laboratory testing in which the correct match came top for people in the database (Wales Online). 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(2025). Man wanted since 2012 arrested after facial recognition app match. Wales Online, July 14, 2025.", "title": "Man wanted since 2012 arrested after facial recognition app match", "url": "https://www.walesonline.co.uk/news/wales-news/man-wanted-2012-arrested-after-32057351", "date": "2025-07-14", "written_by": "independent", "quote": "The 38-year-old man was remanded in custody to Westminster magistrates' court." }, { "kind": "news report", "citation": "Devonshire, J. (2025). 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OECD AI Incidents and Hazards Monitor, 2025-07-14.", "title": "Facial Recognition AI App Leads to Arrest of Fugitive Drug Dealer After 13 Years", "url": "https://oecd.ai/en/incidents/2025-07-14-7341", "date": "2025-07-14", "written_by": "independent", "quote": "used a new AI-powered facial recognition app to identify and arrest a fugitive drug dealer wanted since 2012" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0384-source-0001", "AIBD-0384-source-0002", "AIBD-0384-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Dalling, R. (2025). Man wanted since 2012 arrested after facial recognition app match. Wales Online, July 14, 2025.", "title": "Man wanted since 2012 arrested after facial recognition app match", "url": "https://www.walesonline.co.uk/news/wales-news/man-wanted-2012-arrested-after-32057351", "date": "2025-07-14", "written_by": "independent", "quote": "The 38-year-old man was remanded in custody to Westminster magistrates' court." }, { "kind": "news report", "citation": "Devonshire, J. (2025). More than 500 officers in Wales now trained to use real-time facial recognition app. Emergency Services Times, July 15, 2025.", "title": "More than 500 officers in Wales now trained to use real-time facial recognition app", "url": "https://emergencyservicestimes.com/2025/07/15/more-than-500-officers-in-wales-now-trained-to-use-real-time-facial-recognition-app/", "date": "2025-07-15", "written_by": "independent", "quote": "Thanks to the app, he was swiftly identified, remanded in custody, and brought before Westminster Magistrates' Court." }, { "kind": "registry label", "citation": "OECD.AI (2025). Facial Recognition AI App Leads to Arrest of Fugitive Drug Dealer After 13 Years. OECD AI Incidents and Hazards Monitor, 2025-07-14.", "title": "Facial Recognition AI App Leads to Arrest of Fugitive Drug Dealer After 13 Years", "url": "https://oecd.ai/en/incidents/2025-07-14-7341", "date": "2025-07-14", "written_by": "independent", "quote": "used a new AI-powered facial recognition app to identify and arrest a fugitive drug dealer wanted since 2012" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "A South Wales Police officer's OIFR phone app matched a 38-year-old in Cardiff to a 2012 Polish European arrest warrant for drug dealing and assaulting an officer; he was remanded (July 2025).", "account": "South Wales Police and Gwent Police are the first UK forces with the Operator Initiated Facial Recognition (OIFR) app. It lets an officer photograph a person on a police phone and compare the image with a reference database. It is for when the person refuses or gives false details. It launched in December 2024. More than 500 officers had been trained by July 2025 (Emergency Services Times, 15 July 2025). In one use, an officer in Cardiff identified a 38-year-old man wanted on a European arrest warrant since 2012. The warrant was for drug dealing and assaulting a police officer with a weapon in Poland. He was remanded in custody to Westminster magistrates' court (Wales Online, 14 July 2025). The forces report 50 South Wales searches producing 10 arrests, and six Gwent uses with three matches and one arrest. They cite National Physical Laboratory testing in which the correct match came top for people in the database (Wales Online). The arrest date is not given." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Operator Initiated Facial Recognition (OIFR) mobile app, South Wales Police and Gwent Police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "South Wales Police's Operator Initiated Facial Recognition app identifies a 38-year-old in Cardiff wanted on a 2012 European arrest warrant from Poland; he is remanded to Westminster magistrates", "headline": "Police phone app identifies man wanted since 2012 in Cardiff" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "bfe51851999dc8d362daa237a555c84a3337b06a8cd2979fee1b8168eac29d33" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "South Wales Police and Gwent Police are the first UK forces with the Operator Initiated Facial Recognition (OIFR) app. It lets an officer photograph a person on a police phone and compare the image with a reference database. It is for when the person refuses or gives false details. It launched in December 2024. More than 500 officers had been trained by July 2025 (Emergency Services Times, 15 July 2025). In one use, an officer in Cardiff identified a 38-year-old man wanted on a European arrest warrant since 2012. The warrant was for drug dealing and assaulting a police officer with a weapon in Poland. He was remanded in custody to Westminster magistrates' court (Wales Online, 14 July 2025). The forces report 50 South Wales searches producing 10 arrests, and six Gwent uses with three matches and one arrest. They cite National Physical Laboratory testing in which the correct match came top for people in the database (Wales Online). The arrest date is not given.", "replication": { "produced_by": [ "A street encounter in which the person refuses or gives false details", "A phone app linked to a reference database that holds the wanted person's image", "A warrant record current enough for officers to act on the match" ], "must_hold": [ "The app is used after an attempted engagement and only where necessary and proportionate, per the force's policy", "The match is confirmed against the warrant before detention" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0373", "case_url": "records/AIBD-0373.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Vigia Mais MT is the state's video-monitoring program. Its facial recognition compares faces seen on public cameras with wanted persons so that Ciosp can direct patrols to them. Locating a person with an open warrant is that purpose." }, { "label": "AI contribution", "value": "The reports credit the facial recognition system with the identification. Cameras in central Cuiabá matched her face and the system confirmed her identity. Ciosp operators then tracked her and a Military Police team made the arrest. The sources do not name the vendor, the algorithm or any match score. They come from a single text that reads as the state's release." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Brazil" ] }, "record": { "id": "AIBD-0373", "title": "Vigia Mais MT facial recognition identifies a woman wanted for drug trafficking in downtown Cuiabá; Ciosp tracks her in real time and the 1st Military Police Battalion arrests her", "summary": "Diário de Cuiabá (14 July 2025): Sesp-MT's Vigia Mais MT cameras matched a woman with a June 2024 trafficking warrant in central Cuiabá on 12 July; Ciosp tracked her live and the 1st BPM arrested her.", "date_observed": "2025-07-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Saturday 12 July 2025 the video-monitoring system of Mato Grosso's State Secretariat of Public Security (Sesp) helped arrest a woman in downtown Cuiabá. It acted through the Vigia Mais MT program. She had an open warrant for drug trafficking. That is per the same text carried by Diário de Cuiabá and O Documento on 14 July and CliqueF5 on 15 July 2025. Cameras in the city center identified her by facial recognition. After the system confirmed her identity, the monitoring center of the Integrated Public Security Operations Center (Ciosp) tracked her movements in real time. It passed them to a team of the 1st Military Police Battalion, which made the approach. Her warrant had been issued on 19 June 2024 by the 9th Criminal Court Specialized in Drug Offenses. She was taken to a Civil Judicial Police unit. The reports add that from April to July 2025 about 60 fugitives were located and arrested by state security forces.", "category": "benefit created", "unexpected": false, "purpose": "Vigia Mais MT is the state's video-monitoring program. Its facial recognition compares faces seen on public cameras with wanted persons so that Ciosp can direct patrols to them. Locating a person with an open warrant is that purpose.", "system": "Vigia Mais MT video-monitoring facial recognition (Sesp-MT / Ciosp)", "model_version": null, "developer": null, "deployer": "Secretaria de Estado de Segurança Pública de Mato Grosso (Sesp-MT), through the Centro Integrado de Operações de Segurança Pública (Ciosp)", "location": "Cuiabá, Brazil", "domain": "Policing / fugitive identification", "segment": "Public-space facial recognition against warrant lists", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-07-14-f4e4" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "Sesp-MT's Ciosp located a woman wanted since a 19 June 2024 warrant and tracked her in real time, per the 14 July 2025 reports. It handed her to the 1st Military Police Battalion, which arrested her. A measurable result for the unit." }, { "group": "societal", "depth": 1, "reason": "One person with an open drug-trafficking warrant was taken into custody. The reports give no evidence of any further harm prevented. So the public benefit is the execution of a warrant." } ], "ai_role": { "status": "assessed", "statement": "The reports credit the facial recognition system with the identification. Cameras in central Cuiabá matched her face and the system confirmed her identity. Ciosp operators then tracked her and a Military Police team made the arrest. The sources do not name the vendor, the algorithm or any match score. They come from a single text that reads as the state's release." }, "evidence": [ { "kind": "news report", "citation": "Diário de Cuiabá (2025). Reconhecimento facial leva à prisão procurada por tráfico. Diário de Cuiabá, Cidades, 14 July 2025.", "title": "Reconhecimento facial leva à prisão procurada por tráfico", "url": "https://www.diariodecuiaba.com.br/cidades/reconhecimento-facial-leva-a-prisao-procurada-por-trafico/713555", "date": "2025-07-14", "written_by": "independent", "quote": "Após a confirmação da identidade por meio do sistema, a central de monitoramento do Centro Integrado de Operações de Segurança Pública (Ciosp) rastreou os deslocamentos da mulher em tempo real" }, { "kind": "news report", "citation": "O Documento (2025). Mulher procurada por tráfico é presa por reconhecimento facial no centro de Cuiabá. O Documento, 14 July 2025.", "title": "Mulher procurada por tráfico é presa por reconhecimento facial no centro de Cuiabá", "url": "https://odocumento.com.br/mulher-procurada-por-trafico-e-presa-por-reconhecimento-facial-em-cuiaba", "date": "2025-07-14", "written_by": "independent", "quote": "De abril a julho, aproximadamente 60 foragidos da Justiça foram localizados e presos pelas forças de segurança do Estado." }, { "kind": "news report", "citation": "CliqueF5 (2025). Mulher procurada por tráfico é presa, após ser reconhecida por câmeras de monitoramento. CliqueF5, Polícia, 15 July 2025.", "title": "Mulher procurada por tráfico é presa, após ser reconhecida por câmeras de monitoramento", "url": "https://www.cliquef5.com.br/policia/mulher-procurada-por-trafico-e-presa-apos-ser-reconhecida-por-cameras-de-monitoramento/395259", "date": "2025-07-15", "written_by": "independent", "quote": "A captura foi possível graças à tecnologia de reconhecimento facial. A suspeita foi identificada pelas câmeras instaladas na região central da capital." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2025). Facial Recognition AI Leads to Arrest of Fugitive in Cuiabá, Brazil. OECD.AI, 14 July 2025.", "title": "Facial Recognition AI Leads to Arrest of Fugitive in Cuiabá, Brazil", "url": "https://oecd.ai/en/incidents/2025-07-14-f4e4", "date": "2025-07-14", "written_by": "independent", "quote": "identified and enabled the real-time tracking and arrest of a woman wanted for drug trafficking in Cuiabá" } ], "replication": { "produced_by": [ "Public cameras in central Cuiabá feeding the Vigia Mais MT facial recognition", "A warrant record for the woman in the list the system compares against", "Ciosp operators able to confirm the match and follow her across cameras in real time", "A Military Police team near enough to make the approach" ], "must_hold": [ "The person passes a camera with a usable view of the face", "The warrant is current in the database the system queries", "Operators confirm the match before a patrol is sent" ], "echoes": [] }, "paired_incident": null, "headline": "Facial recognition finds a wanted woman in downtown Cuiabá", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "Brazil", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-07-14-f4e4", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "http://www.sesp.mt.gov.br/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0373", "catalog_version": "1.0.0", "source_fingerprint": "8fc21882c27f4b8e1bab0c9e3d454396fb600eca31279f33c3b8d5aed62c3981", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0373-source-0001", "AIBD-0373-source-0002", "AIBD-0373-source-0003", "AIBD-0373-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Secretaria de Estado de Segurança Pública de Mato Grosso (Sesp-MT), through the Centro Integrado de Operações de Segurança Pública (Ciosp)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The reports credit the facial recognition system with the identification. Cameras in central Cuiabá matched her face and the system confirmed her identity. Ciosp operators then tracked her and a Military Police team made the arrest. The sources do not name the vendor, the algorithm or any match score. They come from a single text that reads as the state's release." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The reports credit the facial recognition system with the identification. Cameras in central Cuiabá matched her face and the system confirmed her identity. Ciosp operators then tracked her and a Military Police team made the arrest. The sources do not name the vendor, the algorithm or any match score. They come from a single text that reads as the state's release." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Sesp-MT's Ciosp located a woman wanted since a 19 June 2024 warrant and tracked her in real time, per the 14 July 2025 reports. It handed her to the 1st Military Police Battalion, which arrested her. A measurable result for the unit." }, { "group": "societal", "depth": 1, "reason": "One person with an open drug-trafficking warrant was taken into custody. The reports give no evidence of any further harm prevented. So the public benefit is the execution of a warrant." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Sesp-MT's Ciosp located a woman wanted since a 19 June 2024 warrant and tracked her in real time, per the 14 July 2025 reports. It handed her to the 1st Military Police Battalion, which arrested her. A measurable result for the unit." }, { "group": "societal", "depth": 1, "reason": "One person with an open drug-trafficking warrant was taken into custody. The reports give no evidence of any further harm prevented. So the public benefit is the execution of a warrant." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Sesp-MT's Ciosp located a woman wanted since a 19 June 2024 warrant and tracked her in real time, per the 14 July 2025 reports. It handed her to the 1st Military Police Battalion, which arrested her. A measurable result for the unit." }, { "group": "societal", "depth": 1, "reason": "One person with an open drug-trafficking warrant was taken into custody. The reports give no evidence of any further harm prevented. So the public benefit is the execution of a warrant." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": 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Reconhecimento facial leva à prisão procurada por tráfico. Diário de Cuiabá, Cidades, 14 July 2025.", "title": "Reconhecimento facial leva à prisão procurada por tráfico", "url": "https://www.diariodecuiaba.com.br/cidades/reconhecimento-facial-leva-a-prisao-procurada-por-trafico/713555", "date": "2025-07-14", "written_by": "independent", "quote": "Após a confirmação da identidade por meio do sistema, a central de monitoramento do Centro Integrado de Operações de Segurança Pública (Ciosp) rastreou os deslocamentos da mulher em tempo real" }, { "kind": "news report", "citation": "O Documento (2025). Mulher procurada por tráfico é presa por reconhecimento facial no centro de Cuiabá. 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The reports add that from April to July 2025 about 60 fugitives were located and arrested by state security forces." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Vigia Mais MT video-monitoring facial recognition (Sesp-MT / Ciosp)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Vigia Mais MT facial recognition identifies a woman wanted for drug trafficking in downtown Cuiabá; Ciosp tracks her in real time and the 1st Military Police Battalion arrests her", "headline": "Facial recognition finds a wanted woman in downtown Cuiabá" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "8fc21882c27f4b8e1bab0c9e3d454396fb600eca31279f33c3b8d5aed62c3981" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Saturday 12 July 2025 the video-monitoring system of Mato Grosso's State Secretariat of Public Security (Sesp) helped arrest a woman in downtown Cuiabá. It acted through the Vigia Mais MT program. She had an open warrant for drug trafficking. That is per the same text carried by Diário de Cuiabá and O Documento on 14 July and CliqueF5 on 15 July 2025. Cameras in the city center identified her by facial recognition. After the system confirmed her identity, the monitoring center of the Integrated Public Security Operations Center (Ciosp) tracked her movements in real time. It passed them to a team of the 1st Military Police Battalion, which made the approach. Her warrant had been issued on 19 June 2024 by the 9th Criminal Court Specialized in Drug Offenses. She was taken to a Civil Judicial Police unit. The reports add that from April to July 2025 about 60 fugitives were located and arrested by state security forces.", "replication": { "produced_by": [ "Public cameras in central Cuiabá feeding the Vigia Mais MT facial recognition", "A warrant record for the woman in the list the system compares against", "Ciosp operators able to confirm the match and follow her across cameras in real time", "A Military Police team near enough to make the approach" ], "must_hold": [ "The person passes a camera with a usable view of the face", "The warrant is current in the database the system queries", "Operators confirm the match before a patrol is sent" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0147", "case_url": "records/AIBD-0147.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The EPB introduced AI smoke-recognition cameras in 2022 to recognize abnormal smoke around the clock at open-burning hotspots. They send automatic alerts so inspectors can locate and stop pollution sources and reduce fire risk. Detecting and recording this roadside burning was that intended use." }, { "label": "AI contribution", "value": "The AI smoke-recognition system and micro-sensors supplied the detection that sent inspectors to the burning site. CNA dates it first to 4 July 2025, then repeatedly in July and August. It also supplied the video that recorded the offender. It could not read his faded motorcycle plate. So identification came from private CCTV, dashcam footage and a police officer's recognition of a local resident. The system's contribution is detection and documentation. Removal of the source depended on human investigation. No measured air-quality change or proven counterfactual is reported. So the record sits at protective response with prevention unproven rather than specific harm prevented." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Taiwan" ] }, "record": { "id": "AIBD-0147", "title": "Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner in Dadu District", "summary": "Taichung EPB AI smoke detection flagged roadside waste burning in Dadu District; a 74-year-old repeat offender was caught and fined.", "date_observed": "2025-07-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Taichung's Environmental Protection Bureau (EPB) installed its AI smoke-recognition system, air micro-sensors and cameras beside Hugan Road, Dadu District. That followed a March 2025 complaint about smoke from a dumping spot. CNA reports that shortly after 08:00 on 4 July 2025 the instruments flagged abnormal air quality. Inspectors found dumped waste burning. The system recorded the same man in July and August. His motorcycle plate was too faded to read. So inspectors collected neighbors' closed-circuit television (CCTV) and dashcam footage. A Wuri Precinct officer recognized a 74-year-old man surnamed Zhao, who confessed. On 13 September 2025 the EPB announced a fine of up to NT$100,000 under the Air Pollution Control Act. It asked the land manager to clear the site.", "category": "protective response", "unexpected": false, "purpose": "The EPB introduced AI smoke-recognition cameras in 2022 to recognize abnormal smoke around the clock at open-burning hotspots. They send automatic alerts so inspectors can locate and stop pollution sources and reduce fire risk. Detecting and recording this roadside burning was that intended use.", "system": "AI智慧判煙系統 (AI smart smoke-recognition system) with air micro-sensors and surveillance cameras, operated by the Taichung City Environmental Protection Bureau; 36 hotspot monitoring stations as of July 2025 and over 100 AI and sensor sites citywide by September 2025", "model_version": null, "developer": null, "deployer": "Taichung City Government Environmental Protection Bureau (臺中市政府環境保護局)", "location": "Taichung, Taiwan", "domain": "Environmental protection (air-quality enforcement)", "segment": "Public sector, municipal environmental enforcement", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "An illegal burner caught and fined removes a pollution source; no air-quality change is measured.", "registry_ids": { "oecd_aim": "2025-09-13-b5dc" }, "ratings": [ { "group": "societal", "depth": 2, "reason": "The EPB says the case removed “a potential pollution source threatening air quality” for residents near Hugan Road in Dadu District. CTS and PTS describe repeated burns at the same spot in February, July and August 2025 next to weeds. No exposure figures or air-quality measurements are reported. So the benefit is material but local and unquantified." }, { "group": "organizational", "depth": 1, "reason": "The EPB and Wuri Precinct police closed a repeat-offender case that the faded plate had stalled. The system's alert and video supplied the inspection trigger and the core evidence. The EPB presents the case as proof that its “smart technology city” enforcement works." } ], "ai_role": { "status": "assessed", "statement": "The AI smoke-recognition system and micro-sensors supplied the detection that sent inspectors to the burning site. CNA dates it first to 4 July 2025, then repeatedly in July and August. It also supplied the video that recorded the offender. It could not read his faded motorcycle plate. So identification came from private CCTV, dashcam footage and a police officer's recognition of a local resident. The system's contribution is detection and documentation. Removal of the source depended on human investigation. No measured air-quality change or proven counterfactual is reported. So the record sits at protective response with prevention unproven rather than specific harm prevented." }, "evidence": [ { "kind": "press release", "citation": "Taichung City Government Environmental Protection Bureau (2025). 真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯 [The truth is one: citizens help the EPB and police crack a serial arsonist]. EPB news release, 13 September 2025.", "title": "真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯", "url": "https://www.epb.taichung.gov.tw/3092215/post", "date": "2025-09-13", "written_by": "deployer", "quote": "共同為大肚區市民解決了一起威脅空氣品質的潛在污染源" }, { "kind": "news report", "citation": "Su Mu-chun / Central News Agency (2025). AI偵測空污異常 台中男非法焚燒廢棄物遭查獲 [AI detects abnormal air pollution; Taichung man caught illegally burning waste]. CNA, 13 September 2025.", "title": "AI偵測空污異常 台中男非法焚燒廢棄物遭查獲", "url": "https://www.cna.com.tw/news/asoc/202509130143.aspx", "date": "2025-09-13", "written_by": "independent", "quote": "環保局今年7月4日上午8時多，運用科學儀器偵測出" }, { "kind": "news report", "citation": "Peng Huan-chun / Public Television Service (2025). 台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾 [Taichung EPB's AI smoke-recognition system catches roadside garbage burner]. PTS News, 13 September 2025.", "title": "台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾", "url": "https://news.pts.org.tw/article/770398", "date": "2025-09-13", "written_by": "independent", "quote": "3月接獲民眾檢舉，大肚區護岸路旁的產業道路常有濃煙" }, { "kind": "news report", "citation": "Wu Shu-ping and Lo Yi-hsin / TVBS (2025). 空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人 [Elderly garbage burner caught by AI; 74-year-old living alone identified by police]. TVBS News, 13 September 2025.", "title": "空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人", "url": "https://news.tvbs.com.tw/local/2988180", "date": "2025-09-13", "written_by": "independent", "quote": "環保局接獲系統通報後立即趕到現場滅火，避免了可能的火災蔓延" }, { "kind": "news report", "citation": "Chinese Television System / 華視 (2025). 男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬 [Man repeatedly burned waste; with no casualties, only a NT$100,000 fine]. CTS via Yahoo News Taiwan, 13 September 2025.", "title": "男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬", "url": "http://web.archive.org/web/20250913113400/https://tw.news.yahoo.com/%E7%94%B7%E5%A4%9A%E6%AC%A1%E7%B8%B1%E7%81%AB%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E5%8D%B1%E5%AE%B3%E5%85%AC%E5%85%B1%E5%AE%89%E5%85%A8-%E6%9C%AA%E9%80%A0%E6%88%90%E5%82%B7%E4%BA%A1-%E5%8F%AA%E8%83%BD%E7%BD%B0%E5%8D%81%E8%90%AC-101000875.html", "date": "2025-09-13", "written_by": "independent", "quote": "分別在2月、7月和8月都亂丟廢棄物、還縱火燃燒" }, { "kind": "news report", "citation": "Tsai Shu-yuan / Liberty Times (2025). 台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟 [Black smoke over Dadu traced to a serial arsonist]. Liberty Times Net, 13 September 2025.", "title": "台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟", "url": "https://news.ltn.com.tw/news/Taichung/breakingnews/5176646", "date": "2025-09-13", "written_by": "independent", "quote": "環保局7、8月以AI智慧判煙系統及空氣微型感測器接連發現" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). AI神助攻 台中揪出焚燒廢棄物縱火慣犯 [AI assist: Taichung catches habitual waste-burning arsonist]. NOWnews via Yahoo News Taiwan, 13 September 2025.", "title": "AI神助攻 台中揪出焚燒廢棄物縱火慣犯", "url": "https://tw.news.yahoo.com/ai%E7%A5%9E%E5%8A%A9%E6%94%BB-%E5%8F%B0%E4%B8%AD%E6%8F%AA%E5%87%BA%E7%84%9A%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E7%B8%B1%E7%81%AB%E6%85%A3%E7%8A%AF-032546731.html", "date": "2025-09-13", "written_by": "independent", "quote": "成功查獲一名長期縱火焚燒廢棄物的行為人，有效消除潛在污染源" }, { "kind": "news report", "citation": "Chuang Yu-hsuan / CTWANT (2025). AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝 [AI catches arsonist: abnormal smoke in Dadu]. CTWANT, 13 September 2025.", "title": "AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝", "url": "https://www.ctwant.com/article/445301/", "date": "2025-09-13", "written_by": "independent", "quote": "透過系統即時告警，結合民眾熱心通報" }, { "kind": "press release", "citation": "Taichung City Government (2025). AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法 [AI as a disaster-prevention tool: real-time alerts on open burning]. City news release, 5 July 2025.", "title": "AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法", "url": "https://www.taichung.gov.tw/8868/8872/9962/3034821", "date": "2025-07-05", "written_by": "deployer", "quote": "系統隨即自動發送告警訊息，值勤人員立即派遣稽巡查人員趕赴現場" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). 露天燃燒小心被抓包 中市AI盯哨無所遁形 [Open burners beware: Taichung's AI is watching]. NOWnews, 5 July 2025.", "title": "露天燃燒小心被抓包 中市AI盯哨無所遁形", "url": "https://www.nownews.com/news/6703456", "date": "2025-07-05", "written_by": "independent", "quote": "自111年起導入AI判煙技術，透過智慧監控、即時通報" }, { "kind": "news report", "citation": "Taiwan News (2026). Taichung uses AI cameras, drones to crack down on illegal open burning. Taiwan News, 16 August 2026.", "title": "Taichung uses AI cameras, drones to crack down on illegal open burning", "url": "https://www.taiwannews.com.tw/news/6422182", "date": "2026-08-16", "written_by": "independent", "quote": "it began deploying AI smoke-detecting cameras along with drones in 2022" } ], "replication": { "produced_by": [ "Fixed AI smoke-recognition cameras and air micro-sensors installed at a known open-burning hotspot after a public complaint, with automatic alerts to inspectors", "Inspectors able to reach the site while the fire burned. They could assemble third-party CCTV and dashcam footage when its video could not read the faded plate", "Local police (Wuri Precinct Dadu Station) familiar enough with residents to match the recorded build, clothing and gait to a known 74-year-old resident", "A legal basis for penalties (Air Pollution Control Act Article 32) and a land manager that could be asked to clear the dump site" ], "must_hold": [ "The AI alert must reach staff who can respond while the fire is burning and who have the mandate to enter the site", "Identification must not rest on the camera alone. Plate recognition failed here and human evidence-gathering closed the case", "The offender must be identifiable and reachable. A stranger with no local ties would likely not have been recognized" ], "echoes": [] }, "paired_incident": null, "headline": "Taichung EPB's AI smoke detection catches a serial waste burner", "metric": "a fine of up to NT$100,000", "comparator": null, "later": { "status": "scaled", "date": "2026-08-16", "note": "By August 2026 the bureau reported nearly 170 AI smoke-detection systems across Taichung, up from 36 stations in July 2025 and over 100 sites in September 2025, with hundreds of open-burning cases caught and NT$1.28 million in fines.", "quote": "The bureau added that nearly 170 AI smoke-detection systems have been installed across Taichung to improve enforcement efficiency.", "url": "https://www.taiwannews.com.tw/news/6422182" }, "value": { "amount": "新臺幣10萬元以下 (up to NT$100,000)", "what": "fine under Article 32 of the Air Pollution Control Act for the open burning; CTS reports the fine as NT$100,000", "who": "the 74-year-old burner pays", "estimate": true, "quote": "依違反空氣污染防制法第32條規定，處新臺幣10萬元以下罰鍰", "url": "https://www.epb.taichung.gov.tw/3092215/post" }, "developer_country": null, "deployer_country": "Taiwan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-09-13-b5dc", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the value as stated from the cited sources.", "source": "https://www.taiwannews.com.tw/news/6422182; https://www.epb.taichung.gov.tw/3092215/post", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.epb.taichung.gov.tw/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: nine independent news reports and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0147", "catalog_version": "1.0.0", "source_fingerprint": "7f516b70e55a6ccadb8762561b98177a908055267bf55b453125a59c66807224", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0147-source-0001", "AIBD-0147-source-0002", "AIBD-0147-source-0003", "AIBD-0147-source-0004", "AIBD-0147-source-0005", "AIBD-0147-source-0006", "AIBD-0147-source-0007", "AIBD-0147-source-0008", "AIBD-0147-source-0009", "AIBD-0147-source-0010", "AIBD-0147-source-0011" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0147-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Taichung City Government Environmental Protection Bureau (臺中市政府環境保護局)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI smoke-recognition system and micro-sensors supplied the detection that sent inspectors to the burning site. CNA dates it first to 4 July 2025, then repeatedly in July and August. It also supplied the video that recorded the offender. It could not read his faded motorcycle plate. So identification came from private CCTV, dashcam footage and a police officer's recognition of a local resident. The system's contribution is detection and documentation. Removal of the source depended on human investigation. No measured air-quality change or proven counterfactual is reported. So the record sits at protective response with prevention unproven rather than specific harm prevented." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The AI smoke-recognition system and micro-sensors supplied the detection that sent inspectors to the burning site. CNA dates it first to 4 July 2025, then repeatedly in July and August. It also supplied the video that recorded the offender. It could not read his faded motorcycle plate. So identification came from private CCTV, dashcam footage and a police officer's recognition of a local resident. The system's contribution is detection and documentation. Removal of the source depended on human investigation. No measured air-quality change or proven counterfactual is reported. So the record sits at protective response with prevention unproven rather than specific harm prevented." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "The EPB says the case removed “a potential pollution source threatening air quality” for residents near Hugan Road in Dadu District. CTS and PTS describe repeated burns at the same spot in February, July and August 2025 next to weeds. No exposure figures or air-quality measurements are reported. So the benefit is material but local and unquantified." }, { "group": "organizational", "depth": 1, "reason": "The EPB and Wuri Precinct police closed a repeat-offender case that the faded plate had stalled. The system's alert and video supplied the inspection trigger and the core evidence. The EPB presents the case as proof that its “smart technology city” enforcement works." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: nine independent news reports and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "The EPB says the case removed “a potential pollution source threatening air quality” for residents near Hugan Road in Dadu District. CTS and PTS describe repeated burns at the same spot in February, July and August 2025 next to weeds. No exposure figures or air-quality measurements are reported. So the benefit is material but local and unquantified." }, { "group": "organizational", "depth": 1, "reason": "The EPB and Wuri Precinct police closed a repeat-offender case that the faded plate had stalled. The system's alert and video supplied the inspection trigger and the core evidence. The EPB presents the case as proof that its “smart technology city” enforcement works." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "The EPB says the case removed “a potential pollution source threatening air quality” for residents near Hugan Road in Dadu District. CTS and PTS describe repeated burns at the same spot in February, July and August 2025 next to weeds. No exposure figures or air-quality measurements are reported. So the benefit is material but local and unquantified." }, { "group": "organizational", "depth": 1, "reason": "The EPB and Wuri Precinct police closed a repeat-offender case that the faded plate had stalled. The system's alert and video supplied the inspection trigger and the core evidence. The EPB presents the case as proof that its “smart technology city” enforcement works." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "新臺幣10萬元以下 (up to NT$100,000)", "what": "fine under Article 32 of the Air Pollution Control Act for the open burning; CTS reports the fine as NT$100,000", "who": "the 74-year-old burner pays", "estimate": true, "quote": "依違反空氣污染防制法第32條規定，處新臺幣10萬元以下罰鍰", "url": "https://www.epb.taichung.gov.tw/3092215/post" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "新臺幣10萬元以下 (up to NT$100,000)", "what": "fine under Article 32 of the Air Pollution Control Act for the open burning; CTS reports the fine as NT$100,000", "who": "the 74-year-old burner pays", "estimate": true, "quote": "依違反空氣污染防制法第32條規定，處新臺幣10萬元以下罰鍰", "url": "https://www.epb.taichung.gov.tw/3092215/post" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "新臺幣10萬元以下 (up to NT$100,000)", "what": "fine under Article 32 of the Air Pollution Control Act for the open burning; CTS reports the fine as NT$100,000", "who": "the 74-year-old burner pays", "estimate": true, "quote": "依違反空氣污染防制法第32條規定，處新臺幣10萬元以下罰鍰", "url": "https://www.epb.taichung.gov.tw/3092215/post" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "a fine of up to NT$100,000", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-09-13-b5dc" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-08-16", "note": "By August 2026 the bureau reported nearly 170 AI smoke-detection systems across Taichung, up from 36 stations in July 2025 and over 100 sites in September 2025, with hundreds of open-burning cases caught and NT$1.28 million in fines.", "quote": "The bureau added that nearly 170 AI smoke-detection systems have been installed across Taichung to improve enforcement efficiency.", "url": "https://www.taiwannews.com.tw/news/6422182" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0147" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "An illegal burner caught and fined removes a pollution source; no air-quality change is measured." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Taichung's Environmental Protection Bureau (EPB) installed its AI smoke-recognition system, air micro-sensors and cameras beside Hugan Road, Dadu District. That followed a March 2025 complaint about smoke from a dumping spot. CNA reports that shortly after 08:00 on 4 July 2025 the instruments flagged abnormal air quality. Inspectors found dumped waste burning. The system recorded the same man in July and August. His motorcycle plate was too faded to read. So inspectors collected neighbors' closed-circuit television (CCTV) and dashcam footage. A Wuri Precinct officer recognized a 74-year-old man surnamed Zhao, who confessed. On 13 September 2025 the EPB announced a fine of up to NT$100,000 under the Air Pollution Control Act. It asked the land manager to clear the site.", "benefit_type": "Improved Environmental Sustainability" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "The EPB says the case removed “a potential pollution source threatening air quality” for residents near Hugan Road in Dadu District. CTS and PTS describe repeated burns at the same spot in February, July and August 2025 next to weeds. No exposure figures or air-quality measurements are reported. So the benefit is material but local and unquantified." }, { "group": "organizational", "depth": 1, "reason": "The EPB and Wuri Precinct police closed a repeat-offender case that the faded plate had stalled. The system's alert and video supplied the inspection trigger and the core evidence. The EPB presents the case as proof that its “smart technology city” enforcement works." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The EPB introduced AI smoke-recognition cameras in 2022 to recognize abnormal smoke around the clock at open-burning hotspots. They send automatic alerts so inspectors can locate and stop pollution sources and reduce fire risk. Detecting and recording this roadside burning was that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Taichung, Taiwan", "deployer_country": "Taiwan" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The EPB introduced AI smoke-recognition cameras in 2022 to recognize abnormal smoke around the clock at open-burning hotspots. They send automatic alerts so inspectors can locate and stop pollution sources and reduce fire risk. Detecting and recording this roadside burning was that intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-07-04", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-09-13-b5dc", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the value as stated from the cited sources.", "source": "https://www.taiwannews.com.tw/news/6422182; https://www.epb.taichung.gov.tw/3092215/post", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.epb.taichung.gov.tw/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Fixed AI smoke-recognition cameras and air micro-sensors installed at a known open-burning hotspot after a public complaint, with automatic alerts to inspectors", "Inspectors able to reach the site while the fire burned. They could assemble third-party CCTV and dashcam footage when its video could not read the faded plate", "Local police (Wuri Precinct Dadu Station) familiar enough with residents to match the recorded build, clothing and gait to a known 74-year-old resident", "A legal basis for penalties (Air Pollution Control Act Article 32) and a land manager that could be asked to clear the dump site" ], "must_hold": [ "The AI alert must reach staff who can respond while the fire is burning and who have the mandate to enter the site", "Identification must not rest on the camera alone. Plate recognition failed here and human evidence-gathering closed the case", "The offender must be identifiable and reachable. A stranger with no local ties would likely not have been recognized" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-09-13-b5dc", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the value as stated from the cited sources.", "source": "https://www.taiwannews.com.tw/news/6422182; https://www.epb.taichung.gov.tw/3092215/post", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.epb.taichung.gov.tw/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Environmental protection (air-quality enforcement)", "segment": "Public sector, municipal environmental enforcement" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Taichung City Government Environmental Protection Bureau (2025). 真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯 [The truth is one: citizens help the EPB and police crack a serial arsonist]. EPB news release, 13 September 2025.", "title": "真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯", "url": "https://www.epb.taichung.gov.tw/3092215/post", "date": "2025-09-13", "written_by": "deployer", "quote": "共同為大肚區市民解決了一起威脅空氣品質的潛在污染源" }, { "kind": "news report", "citation": "Su Mu-chun / Central News Agency (2025). AI偵測空污異常 台中男非法焚燒廢棄物遭查獲 [AI detects abnormal air pollution; Taichung man caught illegally burning waste]. CNA, 13 September 2025.", "title": "AI偵測空污異常 台中男非法焚燒廢棄物遭查獲", "url": "https://www.cna.com.tw/news/asoc/202509130143.aspx", "date": "2025-09-13", "written_by": "independent", "quote": "環保局今年7月4日上午8時多，運用科學儀器偵測出" }, { "kind": "news report", "citation": "Peng Huan-chun / Public Television Service (2025). 台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾 [Taichung EPB's AI smoke-recognition system catches roadside garbage burner]. PTS News, 13 September 2025.", "title": "台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾", "url": "https://news.pts.org.tw/article/770398", "date": "2025-09-13", "written_by": "independent", "quote": "3月接獲民眾檢舉，大肚區護岸路旁的產業道路常有濃煙" }, { "kind": "news report", "citation": "Wu Shu-ping and Lo Yi-hsin / TVBS (2025). 空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人 [Elderly garbage burner caught by AI; 74-year-old living alone identified by police]. TVBS News, 13 September 2025.", "title": "空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人", "url": "https://news.tvbs.com.tw/local/2988180", "date": "2025-09-13", "written_by": "independent", "quote": "環保局接獲系統通報後立即趕到現場滅火，避免了可能的火災蔓延" }, { "kind": "news report", "citation": "Chinese Television System / 華視 (2025). 男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬 [Man repeatedly burned waste; with no casualties, only a NT$100,000 fine]. CTS via Yahoo News Taiwan, 13 September 2025.", "title": "男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬", "url": "http://web.archive.org/web/20250913113400/https://tw.news.yahoo.com/%E7%94%B7%E5%A4%9A%E6%AC%A1%E7%B8%B1%E7%81%AB%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E5%8D%B1%E5%AE%B3%E5%85%AC%E5%85%B1%E5%AE%89%E5%85%A8-%E6%9C%AA%E9%80%A0%E6%88%90%E5%82%B7%E4%BA%A1-%E5%8F%AA%E8%83%BD%E7%BD%B0%E5%8D%81%E8%90%AC-101000875.html", "date": "2025-09-13", "written_by": "independent", "quote": "分別在2月、7月和8月都亂丟廢棄物、還縱火燃燒" }, { "kind": "news report", "citation": "Tsai Shu-yuan / Liberty Times (2025). 台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟 [Black smoke over Dadu traced to a serial arsonist]. Liberty Times Net, 13 September 2025.", "title": "台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟", "url": "https://news.ltn.com.tw/news/Taichung/breakingnews/5176646", "date": "2025-09-13", "written_by": "independent", "quote": "環保局7、8月以AI智慧判煙系統及空氣微型感測器接連發現" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). AI神助攻 台中揪出焚燒廢棄物縱火慣犯 [AI assist: Taichung catches habitual waste-burning arsonist]. NOWnews via Yahoo News Taiwan, 13 September 2025.", "title": "AI神助攻 台中揪出焚燒廢棄物縱火慣犯", "url": "https://tw.news.yahoo.com/ai%E7%A5%9E%E5%8A%A9%E6%94%BB-%E5%8F%B0%E4%B8%AD%E6%8F%AA%E5%87%BA%E7%84%9A%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E7%B8%B1%E7%81%AB%E6%85%A3%E7%8A%AF-032546731.html", "date": "2025-09-13", "written_by": "independent", "quote": "成功查獲一名長期縱火焚燒廢棄物的行為人，有效消除潛在污染源" }, { "kind": "news report", "citation": "Chuang Yu-hsuan / CTWANT (2025). AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝 [AI catches arsonist: abnormal smoke in Dadu]. CTWANT, 13 September 2025.", "title": "AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝", "url": "https://www.ctwant.com/article/445301/", "date": "2025-09-13", "written_by": "independent", "quote": "透過系統即時告警，結合民眾熱心通報" }, { "kind": "press release", "citation": "Taichung City Government (2025). AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法 [AI as a disaster-prevention tool: real-time alerts on open burning]. City news release, 5 July 2025.", "title": "AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法", "url": "https://www.taichung.gov.tw/8868/8872/9962/3034821", "date": "2025-07-05", "written_by": "deployer", "quote": "系統隨即自動發送告警訊息，值勤人員立即派遣稽巡查人員趕赴現場" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). 露天燃燒小心被抓包 中市AI盯哨無所遁形 [Open burners beware: Taichung's AI is watching]. NOWnews, 5 July 2025.", "title": "露天燃燒小心被抓包 中市AI盯哨無所遁形", "url": "https://www.nownews.com/news/6703456", "date": "2025-07-05", "written_by": "independent", "quote": "自111年起導入AI判煙技術，透過智慧監控、即時通報" }, { "kind": "news report", "citation": "Taiwan News (2026). Taichung uses AI cameras, drones to crack down on illegal open burning. Taiwan News, 16 August 2026.", "title": "Taichung uses AI cameras, drones to crack down on illegal open burning", "url": "https://www.taiwannews.com.tw/news/6422182", "date": "2026-08-16", "written_by": "independent", "quote": "it began deploying AI smoke-detecting cameras along with drones in 2022" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0147-source-0001", "AIBD-0147-source-0002", "AIBD-0147-source-0003", "AIBD-0147-source-0004", "AIBD-0147-source-0005", "AIBD-0147-source-0006", "AIBD-0147-source-0007", "AIBD-0147-source-0008", "AIBD-0147-source-0009", "AIBD-0147-source-0010", "AIBD-0147-source-0011" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Taichung City Government Environmental Protection Bureau (2025). 真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯 [The truth is one: citizens help the EPB and police crack a serial arsonist]. EPB news release, 13 September 2025.", "title": "真相只有一個 臺中市多位熱心市民協助環保局與警察局通力合作破獲縱火慣犯", "url": "https://www.epb.taichung.gov.tw/3092215/post", "date": "2025-09-13", "written_by": "deployer", "quote": "共同為大肚區市民解決了一起威脅空氣品質的潛在污染源" }, { "kind": "news report", "citation": "Su Mu-chun / Central News Agency (2025). AI偵測空污異常 台中男非法焚燒廢棄物遭查獲 [AI detects abnormal air pollution; Taichung man caught illegally burning waste]. CNA, 13 September 2025.", "title": "AI偵測空污異常 台中男非法焚燒廢棄物遭查獲", "url": "https://www.cna.com.tw/news/asoc/202509130143.aspx", "date": "2025-09-13", "written_by": "independent", "quote": "環保局今年7月4日上午8時多，運用科學儀器偵測出" }, { "kind": "news report", "citation": "Peng Huan-chun / Public Television Service (2025). 台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾 [Taichung EPB's AI smoke-recognition system catches roadside garbage burner]. PTS News, 13 September 2025.", "title": "台中環保局設AI智慧判煙系統 取締路邊燒垃圾民眾", "url": "https://news.pts.org.tw/article/770398", "date": "2025-09-13", "written_by": "independent", "quote": "3月接獲民眾檢舉，大肚區護岸路旁的產業道路常有濃煙" }, { "kind": "news report", "citation": "Wu Shu-ping and Lo Yi-hsin / TVBS (2025). 空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人 [Elderly garbage burner caught by AI; 74-year-old living alone identified by police]. TVBS News, 13 September 2025.", "title": "空汙老翁「焚燒垃圾」遭AI逮到！74歲獨居翁騎車逃 警1招揪人", "url": "https://news.tvbs.com.tw/local/2988180", "date": "2025-09-13", "written_by": "independent", "quote": "環保局接獲系統通報後立即趕到現場滅火，避免了可能的火災蔓延" }, { "kind": "news report", "citation": "Chinese Television System / 華視 (2025). 男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬 [Man repeatedly burned waste; with no casualties, only a NT$100,000 fine]. CTS via Yahoo News Taiwan, 13 September 2025.", "title": "男多次縱火燒廢棄物危害公共安全 「未造成傷亡」只能罰十萬", "url": "http://web.archive.org/web/20250913113400/https://tw.news.yahoo.com/%E7%94%B7%E5%A4%9A%E6%AC%A1%E7%B8%B1%E7%81%AB%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E5%8D%B1%E5%AE%B3%E5%85%AC%E5%85%B1%E5%AE%89%E5%85%A8-%E6%9C%AA%E9%80%A0%E6%88%90%E5%82%B7%E4%BA%A1-%E5%8F%AA%E8%83%BD%E7%BD%B0%E5%8D%81%E8%90%AC-101000875.html", "date": "2025-09-13", "written_by": "independent", "quote": "分別在2月、7月和8月都亂丟廢棄物、還縱火燃燒" }, { "kind": "news report", "citation": "Tsai Shu-yuan / Liberty Times (2025). 台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟 [Black smoke over Dadu traced to a serial arsonist]. Liberty Times Net, 13 September 2025.", "title": "台中大肚區驚見「黑煙衝天」！真相竟是連環縱火犯作祟", "url": "https://news.ltn.com.tw/news/Taichung/breakingnews/5176646", "date": "2025-09-13", "written_by": "independent", "quote": "環保局7、8月以AI智慧判煙系統及空氣微型感測器接連發現" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). AI神助攻 台中揪出焚燒廢棄物縱火慣犯 [AI assist: Taichung catches habitual waste-burning arsonist]. NOWnews via Yahoo News Taiwan, 13 September 2025.", "title": "AI神助攻 台中揪出焚燒廢棄物縱火慣犯", "url": "https://tw.news.yahoo.com/ai%E7%A5%9E%E5%8A%A9%E6%94%BB-%E5%8F%B0%E4%B8%AD%E6%8F%AA%E5%87%BA%E7%84%9A%E7%87%92%E5%BB%A2%E6%A3%84%E7%89%A9%E7%B8%B1%E7%81%AB%E6%85%A3%E7%8A%AF-032546731.html", "date": "2025-09-13", "written_by": "independent", "quote": "成功查獲一名長期縱火焚燒廢棄物的行為人，有效消除潛在污染源" }, { "kind": "news report", "citation": "Chuang Yu-hsuan / CTWANT (2025). AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝 [AI catches arsonist: abnormal smoke in Dadu]. CTWANT, 13 September 2025.", "title": "AI智慧抓縱火犯！台中大肚異常濃煙曝光驚險追緝", "url": "https://www.ctwant.com/article/445301/", "date": "2025-09-13", "written_by": "independent", "quote": "透過系統即時告警，結合民眾熱心通報" }, { "kind": "press release", "citation": "Taichung City Government (2025). AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法 [AI as a disaster-prevention tool: real-time alerts on open burning]. City news release, 5 July 2025.", "title": "AI成為防災利器 即時通報露天燃燒 中市環保局提醒市民勿觸法", "url": "https://www.taichung.gov.tw/8868/8872/9962/3034821", "date": "2025-07-05", "written_by": "deployer", "quote": "系統隨即自動發送告警訊息，值勤人員立即派遣稽巡查人員趕赴現場" }, { "kind": "news report", "citation": "Liu Jung-chun / NOWnews (2025). 露天燃燒小心被抓包 中市AI盯哨無所遁形 [Open burners beware: Taichung's AI is watching]. NOWnews, 5 July 2025.", "title": "露天燃燒小心被抓包 中市AI盯哨無所遁形", "url": "https://www.nownews.com/news/6703456", "date": "2025-07-05", "written_by": "independent", "quote": "自111年起導入AI判煙技術，透過智慧監控、即時通報" }, { "kind": "news report", "citation": "Taiwan News (2026). Taichung uses AI cameras, drones to crack down on illegal open burning. Taiwan News, 16 August 2026.", "title": "Taichung uses AI cameras, drones to crack down on illegal open burning", "url": "https://www.taiwannews.com.tw/news/6422182", "date": "2026-08-16", "written_by": "independent", "quote": "it began deploying AI smoke-detecting cameras along with drones in 2022" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Taichung EPB AI smoke detection flagged roadside waste burning in Dadu District; a 74-year-old repeat offender was caught and fined.", "account": "Taichung's Environmental Protection Bureau (EPB) installed its AI smoke-recognition system, air micro-sensors and cameras beside Hugan Road, Dadu District. That followed a March 2025 complaint about smoke from a dumping spot. CNA reports that shortly after 08:00 on 4 July 2025 the instruments flagged abnormal air quality. Inspectors found dumped waste burning. The system recorded the same man in July and August. His motorcycle plate was too faded to read. So inspectors collected neighbors' closed-circuit television (CCTV) and dashcam footage. A Wuri Precinct officer recognized a 74-year-old man surnamed Zhao, who confessed. On 13 September 2025 the EPB announced a fine of up to NT$100,000 under the Air Pollution Control Act. It asked the land manager to clear the site." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI智慧判煙系統 (AI smart smoke-recognition system) with air micro-sensors and surveillance cameras, operated by the Taichung City Environmental Protection Bureau; 36 hotspot monitoring stations as of July 2025 and over 100 AI and sensor sites citywide by September 2025" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner in Dadu District", "headline": "Taichung EPB's AI smoke detection catches a serial waste burner" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7f516b70e55a6ccadb8762561b98177a908055267bf55b453125a59c66807224" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Taichung's Environmental Protection Bureau (EPB) installed its AI smoke-recognition system, air micro-sensors and cameras beside Hugan Road, Dadu District. That followed a March 2025 complaint about smoke from a dumping spot. CNA reports that shortly after 08:00 on 4 July 2025 the instruments flagged abnormal air quality. Inspectors found dumped waste burning. The system recorded the same man in July and August. His motorcycle plate was too faded to read. So inspectors collected neighbors' closed-circuit television (CCTV) and dashcam footage. A Wuri Precinct officer recognized a 74-year-old man surnamed Zhao, who confessed. On 13 September 2025 the EPB announced a fine of up to NT$100,000 under the Air Pollution Control Act. It asked the land manager to clear the site.", "replication": { "produced_by": [ "Fixed AI smoke-recognition cameras and air micro-sensors installed at a known open-burning hotspot after a public complaint, with automatic alerts to inspectors", "Inspectors able to reach the site while the fire burned. They could assemble third-party CCTV and dashcam footage when its video could not read the faded plate", "Local police (Wuri Precinct Dadu Station) familiar enough with residents to match the recorded build, clothing and gait to a known 74-year-old resident", "A legal basis for penalties (Air Pollution Control Act Article 32) and a land manager that could be asked to clear the dump site" ], "must_hold": [ "The AI alert must reach staff who can respond while the fire is burning and who have the mandate to enter the site", "Identification must not rest on the camera alone. Plate recognition failed here and human evidence-gathering closed the case", "The offender must be identifiable and reachable. A stranger with no local ties would likely not have been recognized" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0132", "case_url": "records/AIBD-0132.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The Met deploys Live Facial Recognition cameras on police vans to identify people of interest and alert officers. Its LFR lead, Lindsey Chiswick, describes the aim as using data and technology to identify offenders that pose a risk to our communities. Identifying a sex offender in breach of his order was that purpose." }, { "label": "AI contribution", "value": "The LFR system did the identification. Cameras on the back of a van at Denmark Hill matched Cheneler's face and alerted officers, Southwark News reports. Finding the child and the knife, the arrest, the charges and the sentence were human and judicial acts. The offense itself existed only because of his 2019 order. The counterfactual is the Met's own hedge. DC Adam Pearce said it is possible that, had he not been identified, Cheneler could have gone on to abuse the child. No allegation about this occasion is reported. The readable source does not name the vendor or algorithm. The Met's press release is not among the sources." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0132", "title": "Met Police live facial recognition flags David Cheneler, a sex offender breaching his order, alone with a six-year-old girl and a lock knife at Denmark Hill", "summary": "Met LFR flagged David Cheneler, 73, with a six-year-old and a lock knife at Denmark Hill, Jan 2025, breaching his SOPO; jailed 20 May 2025.", "date_observed": "2025-05-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In January 2025 Metropolitan Police Live Facial Recognition (LFR) cameras on a van at Denmark Hill identified David Cheneler, 73. He was under a 2019 Sexual Offences Prevention Order barring him from being alone with children under 14. The order followed a 2010 conviction for 15 counts of indecent assault on a female under 16 and five of gross indecency with a child. Officers found him with a six-year-old girl he had collected from school for her mother. The mother did not know his history. A lock knife was hidden in his belt buckle. He pleaded guilty at Wimbledon Magistrates' Court on 13 January 2025 to breaching the order and possessing an offensive weapon. On 20 May 2025 Kingston Crown Court jailed him for two years.", "category": "protective response", "unexpected": false, "purpose": "The Met deploys Live Facial Recognition cameras on police vans to identify people of interest and alert officers. Its LFR lead, Lindsey Chiswick, describes the aim as using data and technology to identify offenders that pose a risk to our communities. Identifying a sex offender in breach of his order was that purpose.", "system": "Live Facial Recognition (LFR) cameras mounted on a Metropolitan Police van", "model_version": null, "developer": null, "deployer": "Metropolitan Police Service", "location": "London, United Kingdom", "domain": "Policing / public protection", "segment": "Law enforcement use of live facial recognition in public places", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-05-20-368b" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "A six-year-old girl was taken out of unsupervised contact with a man convicted of 15 counts of indecent assault on a female under 16. He had collected her from school twice before and was carrying a lock knife. Material for one child, though the Met itself puts the averted abuse as possible rather than established." }, { "group": "societal", "depth": 1, "reason": "The public: a registered sex offender in breach of his order was jailed for two years. The Met presents this as removing a dangerous offender from the streets. The OECD Monitor tags the same event with human-rights, psychological, reputational and public-interest harm types. The wider benefit is therefore contested." } ], "ai_role": { "status": "assessed", "statement": "The LFR system did the identification. Cameras on the back of a van at Denmark Hill matched Cheneler's face and alerted officers, Southwark News reports. Finding the child and the knife, the arrest, the charges and the sentence were human and judicial acts. The offense itself existed only because of his 2019 order. The counterfactual is the Met's own hedge. DC Adam Pearce said it is possible that, had he not been identified, Cheneler could have gone on to abuse the child. No allegation about this occasion is reported. The readable source does not name the vendor or algorithm. The Met's press release is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Clarke I (2025). Sex offender caught in Denmark Hill thanks to facial recognition cameras. Southwark News, 21 May 2025.", "title": "Sex offender caught in Denmark Hill thanks to facial recognition cameras", "url": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/", "date": "2025-05-21", "written_by": "independent", "quote": "was caught in January in Denmark Hill after his face was identified by Live Facial Recognition cameras attached to the back of a van" }, { "kind": "other", "citation": "OECD.AI (2025). Live Facial Recognition Apprehends Sex Offender in Breach. OECD AI Incidents and Hazards Monitor, incident 2025-05-20-368b.", "title": "Live Facial Recognition Apprehends Sex Offender in Breach", "url": "https://oecd.ai/en/incidents/2025-05-20-368b", "date": null, "written_by": "independent", "quote": "prevented possible harm to the child and others" }, { "kind": "news report", "citation": "Biometric Update (2025). London police mark 1K arrests through live facial recognition. Biometric Update, 4 July 2025.", "title": "London police mark 1K arrests through live facial recognition", "url": "https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition", "date": "2025-07-04", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A van-mounted LFR deployment at Denmark Hill in January 2025 and a face the system was able to identify. The match alerted officers on the spot.", "Officers who stopped him at once and found the child and the lock knife hidden in his belt buckle. They knew the terms of his 2019 Sexual Offences Prevention Order.", "A court record of the order and his 2010 convictions. This turned the stop into a guilty plea for breaching the order and possessing an offensive weapon." ], "must_hold": [ "The person must be known to the system and subject to conditions that make his presence with a child an offense. The benefit depends on the prior order, not on the camera alone.", "Officers must verify the match and intervene immediately. The child was found only because he was stopped on the spot.", "The deployment must be lawful and proportionate. The OECD Monitor files this same event under human-rights, psychological, reputational and public-interest harm types. The protective benefit therefore sits beside a contested surveillance practice." ], "echoes": [] }, "paired_incident": null, "headline": "Met LFR flags David Cheneler, a sex offender, alone with a child", "metric": "Kingston Crown Court jailed him for two years", "comparator": { "against": "not being identified (no live facial recognition check)", "before": "it’s possible if he hadn’t been identified using this technology, he could have gone on to abuse this child", "after": "his face was identified by Live Facial Recognition cameras attached to the back of a van, alerting officers to his presence", "basis": "assumed", "quote": "Although there were no allegations made towards David Cheneler on this occasion, it’s possible if he hadn’t been identified using this technology, he could have gone on to abuse this child.", "url": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/" }, "later": { "status": "scaled", "date": "2025-07-04", "note": "Six weeks after the sentencing the Met announced more than 1,000 LFR arrests since the start of 2024, with 773 people charged or cautioned and 93 registered sex offenders identified, and published Cheneler's case as one of its case studies.", "quote": "The police service published several case studies, including 73-year-old David Cheneler, a registered sex offender who was found to be with a six-year-old girl in breach of a court order.", "url": "https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition" }, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated 20 May 2025, the sentencing at Kingston Crown Court and the Monitor's own date; the LFR identification happened in January 2025 (Southwark News), before his guilty plea at Wimbledon Magistrates' Court on Monday 13 January 2025, and no readable source gives the day. Of the Monitor's eleven sources only Southwark News could be read: BBC, Sky News, The Times, Daily Mail, Mirror, The Irish Sun, Daily Star and News Shopper are not fetchable by this tool, the London News Online and Parikiaki pages are gone, and the Met newsroom search did not render, so the Met's press release was not read and the vendor and algorithm are not named. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the Met's own officer put the averted abuse as possible and no allegation about this occasion is reported.", "source": "https://oecd.ai/en/incidents/2025-05-20-368b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/; https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Metropolitan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0132", "catalog_version": "1.0.0", "source_fingerprint": "7223682fc26832f65d4c5cca613d50d644e7ee22c73c627e7e0719274193e544", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0132-source-0001", "AIBD-0132-source-0002", "AIBD-0132-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Metropolitan Police Service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The LFR system did the identification. Cameras on the back of a van at Denmark Hill matched Cheneler's face and alerted officers, Southwark News reports. Finding the child and the knife, the arrest, the charges and the sentence were human and judicial acts. The offense itself existed only because of his 2019 order. The counterfactual is the Met's own hedge. DC Adam Pearce said it is possible that, had he not been identified, Cheneler could have gone on to abuse the child. No allegation about this occasion is reported. The readable source does not name the vendor or algorithm. The Met's press release is not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The LFR system did the identification. Cameras on the back of a van at Denmark Hill matched Cheneler's face and alerted officers, Southwark News reports. Finding the child and the knife, the arrest, the charges and the sentence were human and judicial acts. The offense itself existed only because of his 2019 order. The counterfactual is the Met's own hedge. DC Adam Pearce said it is possible that, had he not been identified, Cheneler could have gone on to abuse the child. No allegation about this occasion is reported. The readable source does not name the vendor or algorithm. The Met's press release is not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A six-year-old girl was taken out of unsupervised contact with a man convicted of 15 counts of indecent assault on a female under 16. He had collected her from school twice before and was carrying a lock knife. Material for one child, though the Met itself puts the averted abuse as possible rather than established." }, { "group": "societal", "depth": 1, "reason": "The public: a registered sex offender in breach of his order was jailed for two years. The Met presents this as removing a dangerous offender from the streets. The OECD Monitor tags the same event with human-rights, psychological, reputational and public-interest harm types. The wider benefit is therefore contested." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A six-year-old girl was taken out of unsupervised contact with a man convicted of 15 counts of indecent assault on a female under 16. He had collected her from school twice before and was carrying a lock knife. Material for one child, though the Met itself puts the averted abuse as possible rather than established." }, { "group": "societal", "depth": 1, "reason": "The public: a registered sex offender in breach of his order was jailed for two years. The Met presents this as removing a dangerous offender from the streets. The OECD Monitor tags the same event with human-rights, psychological, reputational and public-interest harm types. The wider benefit is therefore contested." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A six-year-old girl was taken out of unsupervised contact with a man convicted of 15 counts of indecent assault on a female under 16. He had collected her from school twice before and was carrying a lock knife. 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}, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Kingston Crown Court jailed him for two years", "editor_note": "" } }, "evaluations[].reproduction_materials": { 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announced more than 1,000 LFR arrests since the start of 2024, with 773 people charged or cautioned and 93 registered sex offenders identified, and published Cheneler's case as one of its case studies.", "quote": "The police service published several case studies, including 73-year-old David Cheneler, a registered sex offender who was found to be with a six-year-old girl in breach of a court order.", "url": "https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0132" } }, "provenance": { "kind": 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"value": { "kind": "record_fields", "fields": { "account": "In January 2025 Metropolitan Police Live Facial Recognition (LFR) cameras on a van at Denmark Hill identified David Cheneler, 73. He was under a 2019 Sexual Offences Prevention Order barring him from being alone with children under 14. The order followed a 2010 conviction for 15 counts of indecent assault on a female under 16 and five of gross indecency with a child. Officers found him with a six-year-old girl he had collected from school for her mother. The mother did not know his history. A lock knife was hidden in his belt buckle. He pleaded guilty at Wimbledon Magistrates' Court on 13 January 2025 to breaching the order and possessing an offensive weapon. On 20 May 2025 Kingston Crown Court jailed him for two years.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "A six-year-old girl was taken out of unsupervised contact with a man convicted of 15 counts of indecent assault on a female under 16. 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The wider benefit is therefore contested." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The Met deploys Live Facial Recognition cameras on police vans to identify people of interest and alert officers. Its LFR lead, Lindsey Chiswick, describes the aim as using data and technology to identify offenders that pose a risk to our communities. 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Its LFR lead, Lindsey Chiswick, describes the aim as using data and technology to identify offenders that pose a risk to our communities. Identifying a sex offender in breach of his order was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-20", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated 20 May 2025, the sentencing at Kingston Crown Court and the Monitor's own date; the LFR identification happened in January 2025 (Southwark News), before his guilty plea at Wimbledon Magistrates' Court on Monday 13 January 2025, and no readable source gives the day. Of the Monitor's eleven sources only Southwark News could be read: BBC, Sky News, The Times, Daily Mail, Mirror, The Irish Sun, Daily Star and News Shopper are not fetchable by this tool, the London News Online and Parikiaki pages are gone, and the Met newsroom search did not render, so the Met's press release was not read and the vendor and algorithm are not named. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the Met's own officer put the averted abuse as possible and no allegation about this occasion is reported.", "source": "https://oecd.ai/en/incidents/2025-05-20-368b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/; https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Metropolitan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A van-mounted LFR deployment at Denmark Hill in January 2025 and a face the system was able to identify. 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Of the Monitor's eleven sources only Southwark News could be read: BBC, Sky News, The Times, Daily Mail, Mirror, The Irish Sun, Daily Star and News Shopper are not fetchable by this tool, the London News Online and Parikiaki pages are gone, and the Met newsroom search did not render, so the Met's press release was not read and the vendor and algorithm are not named. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented because the Met's own officer put the averted abuse as possible and no allegation about this occasion is reported.", "source": "https://oecd.ai/en/incidents/2025-05-20-368b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/; https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Metropolitan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / public protection", "segment": "Law enforcement use of live facial recognition in public places" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Clarke I (2025). Sex offender caught in Denmark Hill thanks to facial recognition cameras. Southwark News, 21 May 2025.", "title": "Sex offender caught in Denmark Hill thanks to facial recognition cameras", "url": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/", "date": "2025-05-21", "written_by": "independent", "quote": "was caught in January in Denmark Hill after his face was identified by Live Facial Recognition cameras attached to the back of a van" }, { "kind": "other", "citation": "OECD.AI (2025). Live Facial Recognition Apprehends Sex Offender in Breach. OECD AI Incidents and Hazards Monitor, incident 2025-05-20-368b.", "title": "Live Facial Recognition Apprehends Sex Offender in Breach", "url": "https://oecd.ai/en/incidents/2025-05-20-368b", "written_by": "independent", "quote": "prevented possible harm to the child and others" }, { "kind": "news report", "citation": "Biometric Update (2025). London police mark 1K arrests through live facial recognition. 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London police mark 1K arrests through live facial recognition. Biometric Update, 4 July 2025.", "title": "London police mark 1K arrests through live facial recognition", "url": "https://www.biometricupdate.com/202507/london-police-mark-1000-arrests-through-live-facial-recognition", "date": "2025-07-04", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Met LFR flagged David Cheneler, 73, with a six-year-old and a lock knife at Denmark Hill, Jan 2025, breaching his SOPO; jailed 20 May 2025.", "account": "In January 2025 Metropolitan Police Live Facial Recognition (LFR) cameras on a van at Denmark Hill identified David Cheneler, 73. He was under a 2019 Sexual Offences Prevention Order barring him from being alone with children under 14. The order followed a 2010 conviction for 15 counts of indecent assault on a female under 16 and five of gross indecency with a child. Officers found him with a six-year-old girl he had collected from school for her mother. The mother did not know his history. A lock knife was hidden in his belt buckle. He pleaded guilty at Wimbledon Magistrates' Court on 13 January 2025 to breaching the order and possessing an offensive weapon. On 20 May 2025 Kingston Crown Court jailed him for two years." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Live Facial Recognition (LFR) cameras mounted on a Metropolitan Police van" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Met Police live facial recognition flags David Cheneler, a sex offender breaching his order, alone with a six-year-old girl and a lock knife at Denmark Hill", "headline": "Met LFR flags David Cheneler, a sex offender, alone with a child" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7223682fc26832f65d4c5cca613d50d644e7ee22c73c627e7e0719274193e544" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In January 2025 Metropolitan Police Live Facial Recognition (LFR) cameras on a van at Denmark Hill identified David Cheneler, 73. He was under a 2019 Sexual Offences Prevention Order barring him from being alone with children under 14. The order followed a 2010 conviction for 15 counts of indecent assault on a female under 16 and five of gross indecency with a child. Officers found him with a six-year-old girl he had collected from school for her mother. The mother did not know his history. A lock knife was hidden in his belt buckle. He pleaded guilty at Wimbledon Magistrates' Court on 13 January 2025 to breaching the order and possessing an offensive weapon. On 20 May 2025 Kingston Crown Court jailed him for two years.", "replication": { "produced_by": [ "A van-mounted LFR deployment at Denmark Hill in January 2025 and a face the system was able to identify. The match alerted officers on the spot.", "Officers who stopped him at once and found the child and the lock knife hidden in his belt buckle. They knew the terms of his 2019 Sexual Offences Prevention Order.", "A court record of the order and his 2010 convictions. This turned the stop into a guilty plea for breaching the order and possessing an offensive weapon." ], "must_hold": [ "The person must be known to the system and subject to conditions that make his presence with a child an offense. The benefit depends on the prior order, not on the camera alone.", "Officers must verify the match and intervene immediately. The child was found only because he was stopped on the spot.", "The deployment must be lawful and proportionate. The OECD Monitor files this same event under human-rights, psychological, reputational and public-interest harm types. The protective benefit therefore sits beside a contested surveillance practice." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0236", "case_url": "records/AIBD-0236.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "General-purpose reasoning model used by the researcher to audit kernel source code for vulnerabilities." }, { "label": "AI contribution", "value": "The model produced the finding unprompted during a benchmark. Heelan selected the code, validated the report and handled disclosure. He notes the model also produced false positives in other runs. The account is the independent deployer's own, corroborated by the CVE record and third-party coverage, not by OpenAI." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0236", "title": "OpenAI o3 finds CVE-2025–37899, a remote use-after-free in the Linux kernel's ksmbd; patched in all maintained branches", "summary": "Sean Heelan, benchmarking o3 on ksmbd with no scaffolding, had the model surface an unknown use-after-free in the SMB logoff handler; the kernel CNA published CVE-2025–37899 on 20 May 2025.", "date_observed": "2025-05-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Heelan's write-up followed on 22 May 2025. Heelan is an independent vulnerability researcher auditing ksmbd (the Linux kernel's in-kernel SMB3 server). He used a known bug (CVE-2025–37778) to benchmark OpenAI's o3 via the plain application programming interface (API), with no scaffolding, agents or tools. Given about 12,000 lines covering all SMB command handlers, o3 reported a previously unknown use-after-free in the handler for the SMB “logoff” command. sess->user can be freed while another connection bound to the same session still uses it. The Linux kernel CNA's record describes the fix (“ksmbd: fix use-after-free in session logoff”) with commits in five stable branches. Linuxiac reports the upstream response was fast and patches were merged for every maintained branch. Heelan calls it, to his knowledge, the first public case of a large language model (LLM) finding a vulnerability of that nature.", "category": "benefit created", "unexpected": false, "purpose": "General-purpose reasoning model used by the researcher to audit kernel source code for vulnerabilities.", "system": "OpenAI o3", "model_version": "o3", "developer": "OpenAI", "deployer": "Sean Heelan (independent researcher)", "location": null, "domain": "Cybersecurity / vulnerability research", "segment": "Technology", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "A remotely reachable kernel memory-safety bug was fixed across maintained branches before any reported exploitation. Exposure of ksmbd users is not quantified." }, { "group": "organizational", "depth": 1, "reason": "The kernel project received a report with a fix merged quickly, per Linuxiac." } ], "ai_role": { "status": "assessed", "statement": "The model produced the finding unprompted during a benchmark. Heelan selected the code, validated the report and handled disclosure. He notes the model also produced false positives in other runs. The account is the independent deployer's own, corroborated by the CVE record and third-party coverage, not by OpenAI." }, "evidence": [ { "kind": "report", "citation": "Sean Heelan, personal blog, 22 May 2025", "title": "How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel’s SMB implementation", "url": "https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-2025-37899-a-remote-zeroday-vulnerability-in-the-linux-kernels-smb-implementation/", "date": "2025-05-22", "written_by": "deployer", "quote": "I found the vulnerability with nothing more complicated than the o3 API – no scaffolding, no agentic frameworks, no tool use." }, { "kind": "registry label", "citation": "CVE Program record CVE-2025-37899 (CNA: Linux), published 20 May 2025", "title": "CVE-2025-37899", "url": "https://www.cve.org/CVERecord?id=CVE-2025-37899", "date": "2025-05-20", "written_by": "independent", "quote": "The sess->user object can currently be in use by another thread, for example if another connection has sent a session setup request to bind to the session being free'd." }, { "kind": "news report", "citation": "Bobby Borisov, Linuxiac, 29 May 2025", "title": "ChatGPT’s o3 Model Found Remote Zeroday in Linux Kernel Code", "url": "https://linuxiac.com/chatgpt-o3-model-found-remote-zeroday-in-linux-kernel-code/", "date": "2025-05-29", "written_by": "independent", "quote": "Upstream response was really fast, and the patches merged for every still-maintained kernel branch." } ], "replication": { "produced_by": [ "A code slice under roughly 10k-12k lines chosen by an expert", "Repeated model runs with reports triaged by a human", "A maintainer workflow that accepts and patches reports quickly" ], "must_hold": [ "Expert curation of context and validation of candidate bugs", "Tolerance for false positives across runs" ], "echoes": [] }, "paired_incident": null, "headline": "OpenAI o3 finds CVE-2025–37899, a use-after-free in ksmbd", "metric": null, "comparator": { "against": "Claude Sonnet 3.7 and Claude Sonnet 3.5 on the same 100-run benchmark (the known ksmbd Kerberos bug)", "before": "Claude Sonnet 3.7 finds it 3 out of 100 runs and Claude Sonnet 3.5 does not find it in 100 runs", "after": "o3 finds the kerberos authentication vulnerability in the benchmark in 8 of the 100 runs", "basis": "measured", "quote": "o3 finds the kerberos authentication vulnerability in the benchmark in 8 of the 100 runs. In another 66 of the runs o3 concludes there is no bug present in the code (false negatives), and the remaining 28 reports are false positives. 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https://linuxiac.com/chatgpt-o3-model-found-remote-zeroday-in-linux-kernel-code/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/OpenAI", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A code slice under roughly 10k-12k lines chosen by an expert", "Repeated model runs with reports triaged by a human", "A maintainer workflow that accepts and patches reports quickly" ], "must_hold": [ "Expert curation of context and validation of candidate bugs", "Tolerance for false positives across runs" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Cybersecurity / vulnerability research", "segment": "Technology" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Sean Heelan, personal blog, 22 May 2025", "title": "How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel’s SMB implementation", "url": "https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-2025-37899-a-remote-zeroday-vulnerability-in-the-linux-kernels-smb-implementation/", "date": "2025-05-22", "written_by": "deployer", "quote": "I found the vulnerability with nothing more complicated than the o3 API – no scaffolding, no agentic frameworks, no tool use." }, { "kind": "registry label", "citation": "CVE Program record CVE-2025-37899 (CNA: Linux), published 20 May 2025", "title": "CVE-2025-37899", "url": "https://www.cve.org/CVERecord?id=CVE-2025-37899", "date": "2025-05-20", "written_by": "independent", "quote": "The sess->user object can currently be in use by another thread, for example if another connection has sent a session setup request to bind to the session being free'd." }, { "kind": "news report", "citation": "Bobby Borisov, Linuxiac, 29 May 2025", "title": "ChatGPT’s o3 Model Found Remote Zeroday in Linux Kernel Code", "url": "https://linuxiac.com/chatgpt-o3-model-found-remote-zeroday-in-linux-kernel-code/", "date": "2025-05-29", "written_by": "independent", "quote": "Upstream response was really fast, and the patches merged for every still-maintained kernel branch." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0236-source-0001", "AIBD-0236-source-0002", "AIBD-0236-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Sean Heelan, personal blog, 22 May 2025", "title": "How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel’s SMB implementation", "url": "https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-2025-37899-a-remote-zeroday-vulnerability-in-the-linux-kernels-smb-implementation/", "date": "2025-05-22", "written_by": "deployer", "quote": "I found the vulnerability with nothing more complicated than the o3 API – no scaffolding, no agentic frameworks, no tool use." }, { "kind": "registry label", "citation": "CVE Program record CVE-2025-37899 (CNA: Linux), published 20 May 2025", "title": "CVE-2025-37899", "url": "https://www.cve.org/CVERecord?id=CVE-2025-37899", "date": "2025-05-20", "written_by": "independent", "quote": "The sess->user object can currently be in use by another thread, for example if another connection has sent a session setup request to bind to the session being free'd." }, { "kind": "news report", "citation": "Bobby Borisov, Linuxiac, 29 May 2025", "title": "ChatGPT’s o3 Model Found Remote Zeroday in Linux Kernel Code", "url": "https://linuxiac.com/chatgpt-o3-model-found-remote-zeroday-in-linux-kernel-code/", "date": "2025-05-29", "written_by": "independent", "quote": "Upstream response was really fast, and the patches merged for every still-maintained kernel branch." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Sean Heelan, benchmarking o3 on ksmbd with no scaffolding, had the model surface an unknown use-after-free in the SMB logoff handler; the kernel CNA published CVE-2025–37899 on 20 May 2025.", "account": "Heelan's write-up followed on 22 May 2025. Heelan is an independent vulnerability researcher auditing ksmbd (the Linux kernel's in-kernel SMB3 server). He used a known bug (CVE-2025–37778) to benchmark OpenAI's o3 via the plain application programming interface (API), with no scaffolding, agents or tools. Given about 12,000 lines covering all SMB command handlers, o3 reported a previously unknown use-after-free in the handler for the SMB “logoff” command. sess->user can be freed while another connection bound to the same session still uses it. The Linux kernel CNA's record describes the fix (“ksmbd: fix use-after-free in session logoff”) with commits in five stable branches. Linuxiac reports the upstream response was fast and patches were merged for every maintained branch. Heelan calls it, to his knowledge, the first public case of a large language model (LLM) finding a vulnerability of that nature." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model analyzed source code and identified a possible vulnerability for a researcher to verify." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "o3." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "o3" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "OpenAI o3" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "OpenAI o3 finds CVE-2025–37899, a remote use-after-free in the Linux kernel's ksmbd; patched in all maintained branches", "headline": "OpenAI o3 finds CVE-2025–37899, a use-after-free in ksmbd" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "9cb79554fbd39e1a6ca90976eab27ce993eb349e7861e95de4647ab3e6f9a36c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Heelan's write-up followed on 22 May 2025. Heelan is an independent vulnerability researcher auditing ksmbd (the Linux kernel's in-kernel SMB3 server). He used a known bug (CVE-2025–37778) to benchmark OpenAI's o3 via the plain application programming interface (API), with no scaffolding, agents or tools. Given about 12,000 lines covering all SMB command handlers, o3 reported a previously unknown use-after-free in the handler for the SMB “logoff” command. sess->user can be freed while another connection bound to the same session still uses it. The Linux kernel CNA's record describes the fix (“ksmbd: fix use-after-free in session logoff”) with commits in five stable branches. Linuxiac reports the upstream response was fast and patches were merged for every maintained branch. Heelan calls it, to his knowledge, the first public case of a large language model (LLM) finding a vulnerability of that nature.", "replication": { "produced_by": [ "A code slice under roughly 10k-12k lines chosen by an expert", "Repeated model runs with reports triaged by a human", "A maintainer workflow that accepts and patches reports quickly" ], "must_hold": [ "Expert curation of context and validation of candidate bugs", "Tolerance for false positives across runs" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0129", "case_url": "records/AIBD-0129.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The bureau places mobile AI sound cameras on main roads and residential streets to collect noise data. Combined with complaint data, this gives the time and place of illegally modified noisy vehicles. Locating this car was that purpose." }, { "label": "AI contribution", "value": "The bureau's own description is the only account of the system's contribution. The task force placed AI sound camera equipment at the relevant locations on the night of the complaint. Analysis of the noise data with the complaint reports gave the car's haunts and times. Environmental and police officers then found it near Daxing Road on 23 April. The sources do not say how the cameras tie noise to a vehicle. They report no decibel reading. They give no counterfactual for how long a conventional patrol would have taken. The stop, the referral and the pending penalty were the officers' work." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Taiwan" ] }, "record": { "id": "AIBD-0129", "title": "Taoyuan's AI sound cameras locate a white BMW that had been roaring down Jieshou Road at night, within a day of residents' complaint", "summary": "Taoyuan's AI sound cameras pinpointed a white BMW noise car the day after residents complained; officers caught it on 23 April 2025.", "date_observed": "2025-04-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Residents of Bade District, Taoyuan, complained in the Facebook group 記憶八德. A white BMW with a loud exhaust kept roaring down Jieshou Road late at night. The Taoyuan City Department of Environmental Protection received the complaint on the night of 22 April 2025. Its Quiet Taoyuan (靜桃) task force immediately set up AI sound camera equipment at the relevant spots. By analyzing the noise data against the complaints, it worked out where and when the car appeared. On 23 April environmental and police officers stopped it near Daxing Road. The car was referred to the motor vehicle office for a modification review. A Noise Control Act fine of up to NT$3,600 and an improvement order are to follow.", "category": "benefit created", "unexpected": false, "purpose": "The bureau places mobile AI sound cameras on main roads and residential streets to collect noise data. Combined with complaint data, this gives the time and place of illegally modified noisy vehicles. Locating this car was that purpose.", "system": "AI sound camera equipment (AI聲音照相) of the Quiet Taoyuan (靜桃) project, part of the bureau's network of 'improved mobile sound cameras' (改良式移動聲音照相)", "model_version": null, "developer": null, "deployer": "Taoyuan City Department of Environmental Protection (桃園市政府環境保護局), with the Taoyuan City Police Department for the stop", "location": "Taoyuan, Taiwan", "domain": "Environmental enforcement (vehicle noise control)", "segment": "Local government", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": "The residents' good, noise abatement, has no type; the documented gain is the bureau's one-day enforcement cycle.", "registry_ids": { "oecd_aim": "2025-04-29-1f62" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents on Jieshou Road had written that the car's late-night passes seriously affected their quality of life and sleep. They saw it identified and referred for a modification review and fine within a day. The reports do not document whether the noise ceased afterwards." }, { "group": "organizational", "depth": 1, "reason": "The bureau closed one complaint in 24 hours and used the case to publicize its sound-camera enforcement. Its claim that the cameras “greatly raise the success rate” of tracking noise vehicles comes without figures." } ], "ai_role": { "status": "assessed", "statement": "The bureau's own description is the only account of the system's contribution. The task force placed AI sound camera equipment at the relevant locations on the night of the complaint. Analysis of the noise data with the complaint reports gave the car's haunts and times. Environmental and police officers then found it near Daxing Road on 23 April. The sources do not say how the cameras tie noise to a vehicle. They report no decibel reading. They give no counterfactual for how long a conventional patrol would have taken. The stop, the referral and the pending penalty were the officers' work." }, "evidence": [ { "kind": "news report", "citation": "Cheng Shu-ting (鄭淑婷) / Liberty Times 自由時報 (2025). 桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰 [Taoyuan Bade's 'Jieshou Road public enemy' noise car caught; environmental and police enforcement and penalty]. Liberty Times Net, 29 April 2025.", "title": "桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰", "url": "https://news.ltn.com.tw/news/society/breakingnews/5026699", "date": "2025-04-29", "written_by": "independent", "quote": "靜桃專案小組立即於相關地點位佈設AI聲音照相設備" }, { "kind": "news report", "citation": "Yeh Chih-cheng (葉志成) / Taiwan Hot News 台灣好新聞, via Yahoo News Taiwan (2025). 八德介壽路噪音車引民怨 環警24小時追查到案裁罰 [Bade Jieshou Road noise car draws complaints; environmental and police track it down and penalise within 24 hours]. Yahoo News Taiwan, 29 April 2025.", "title": "八德介壽路噪音車引民怨　環警24小時追查到案裁罰", "url": "https://tw.news.yahoo.com/%E5%85%AB%E5%BE%B7%E4%BB%8B%E5%A3%BD%E8%B7%AF%E5%99%AA%E9%9F%B3%E8%BB%8A%E5%BC%95%E6%B0%91%E6%80%A8-%E7%92%B0%E8%AD%A624%E5%B0%8F%E6%99%82%E8%BF%BD%E6%9F%A5%E5%88%B0%E6%A1%88%E8%A3%81%E7%BD%B0-161300728.html", "date": "2025-04-29", "written_by": "independent", "quote": "本案在接獲陳情後24小時內即查到違規的民眾" }, { "kind": "news report", "citation": "Taoyuan E-News 桃園電子報, via yam News 蕃新聞 (2025). 八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人 [Bade BMW hooligan blasts the street at night; Taoyuan environmental and police nab him within 24 hours]. yam News, 29 April 2025.", "title": "八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人", "url": "https://n.yam.com/Article/20250429591252", "date": "2025-04-29", "written_by": "independent", "quote": "有民眾發文抱怨1台白色BMW噪音車常於八德區介壽路呼嘯而過" }, { "kind": "news report", "citation": "Tseng Ping (曾平) / 觀傳媒, via yam News 蕃新聞 (2025). 深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰 [Late-night exhaust bangs disturb residents; Taoyuan EPB and police track down the owner for penalty]. yam News, 29 April 2025.", "title": "深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰", "url": "https://n.yam.com/Article/20250429841522", "date": "2025-04-29", "written_by": "independent", "quote": "桃園市大量布設「改良式移動聲音照相」於主要道路與住宅區" }, { "kind": "news report", "citation": "工商時報 CTEE (2026). 噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統. 工商時報, 8 January 2026.", "title": "噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統", "url": "https://www.ctee.com.tw/news/20260108701305-431401", "date": "2026-01-08", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A complaint that named the road and the time window (late night on Jieshou Road). This told the task force where to place the cameras the same night.", "Mobile AI sound cameras already in the bureau's inventory under the Quiet Taoyuan project. Deployment did not wait on procurement.", "A standing environmental-police joint enforcement arrangement that could put officers at the predicted place and time on 23 April." ], "must_hold": [ "The target must be a repeat offender with a pattern. The method matches noise data against complaint times and places, and cannot catch a one-off pass.", "Officers must be free to intercept on the night the analysis points to. The cameras locate; they do not stop.", "A legal basis for the follow-up: here the Noise Control Act (fine up to NT$3,600 and an improvement order). Also the Road Traffic Management and Penalty Act (up to NT$24,000 for a removed muffler)." ], "echoes": [] }, "paired_incident": null, "headline": "Taoyuan's AI sound cameras pinpoint a noisy BMW within a day", "metric": null, "comparator": { "against": "tracking noise vehicles without the mobile sound cameras (the bureau's own assertion; no baseline figure given)", "before": null, "after": "該案在接獲陳情後24小時內即查到違規的民眾", "basis": "assumed", "quote": "桃園市大量布設「改良式移動聲音照相」於主要道路與住宅區，不僅可依陳情熱點作機動性部署，更可長期監控特定熱點區域，自動蒐集噪音數據，結合陳情數據掌握噪音發生的時間與地點，大幅提高追蹤噪音車輛的成功率。", "url": "https://n.yam.com/Article/20250429841522" }, "later": { "status": "scaled", "date": "2026-01-08", "note": "By January 2026 Taoyuan reported 50 sound-camera sets installed citywide, noise complaints down 38% since the Quiet Taoyuan project began, and an AI voiceprint system being integrated into the sound cameras.", "quote": "截至114年，全市已設置50套聲音照相設備，每套採購金額約介於90萬至130萬元之間，屬於中長期噪音治理的重要基礎建設。", "url": "https://www.ctee.com.tw/news/20260108701305-431401" }, "value": { "amount": "3,600元 (New Taiwan dollars)", "what": "maximum fine under the Noise Control Act to be imposed for the noise violation", "who": "the BMW's owner pays", "estimate": true, "quote": "後續將依噪音管制法進行裁罰，最高可處3,600元罰鍰，並通知限期改善，屆期未改善者將按次處罰直至符合機動車輛噪音管制標準為止。", "url": "https://n.yam.com/Article/20250429841522" }, "developer_country": null, "deployer_country": "Taiwan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as benefit created or restored as the classifier proposed: the AI located a car that had already been causing the nuisance, and the dated benefit is a one-day enforcement cycle rather than the prevention of a specific future harm. Every report reproduces the Department of Environmental Protection's 29 April statement, with photographs credited to the bureau; the Liberty Times piece carries its own reporter's byline and quotes the director, 顏己喨. The bureau's own press release could not be retrieved (dep.tycg.gov.tw did not respond and tycg.gov.tw sits behind anti-DDoS protection), and the UDN link on the Monitor returns 404. Corrections to the Monitor summary: the fine had not been imposed at the time of reporting; the reports say the car will subsequently be fined under the Noise Control Act (up to NT$3,600) after referral to the motor vehicle office for a modification review. “BMW” appears in the Taoyuan E-News and Guan Media versions; the Liberty Times and Taiwan Hot News say only “white noise car”. The Monitor's “Economic/Property” harm to the general public is the nuisance the car caused, not anything the AI did. Dated to the stop on 23 April 2025; the complaint arrived on the night of 22 April; the Monitor's ID date (29 April) is the press coverage.", "source": "https://oecd.ai/en/incidents/2025-04-29-1f62", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://n.yam.com/Article/20250429841522; https://www.ctee.com.tw/news/20260108701305-431401; https://n.yam.com/Article/20250429841522", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Taoyuan_City_Government", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0129", "catalog_version": "1.0.0", "source_fingerprint": "106c762dbbd43015b21d4b7a1ccbd451fcaffa175762bbd5ee479a6c176b9ed8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0129-source-0001", "AIBD-0129-source-0002", "AIBD-0129-source-0003", "AIBD-0129-source-0004", "AIBD-0129-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0129-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Taoyuan City Department of Environmental Protection (桃園市政府環境保護局), with the Taoyuan City Police Department for the stop" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The bureau's own description is the only account of the system's contribution. The task force placed AI sound camera equipment at the relevant locations on the night of the complaint. Analysis of the noise data with the complaint reports gave the car's haunts and times. Environmental and police officers then found it near Daxing Road on 23 April. The sources do not say how the cameras tie noise to a vehicle. They report no decibel reading. They give no counterfactual for how long a conventional patrol would have taken. The stop, the referral and the pending penalty were the officers' work." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The bureau's own description is the only account of the system's contribution. The task force placed AI sound camera equipment at the relevant locations on the night of the complaint. Analysis of the noise data with the complaint reports gave the car's haunts and times. Environmental and police officers then found it near Daxing Road on 23 April. The sources do not say how the cameras tie noise to a vehicle. They report no decibel reading. They give no counterfactual for how long a conventional patrol would have taken. The stop, the referral and the pending penalty were the officers' work." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents on Jieshou Road had written that the car's late-night passes seriously affected their quality of life and sleep. They saw it identified and referred for a modification review and fine within a day. The reports do not document whether the noise ceased afterwards." }, { "group": "organizational", "depth": 1, "reason": "The bureau closed one complaint in 24 hours and used the case to publicize its sound-camera enforcement. Its claim that the cameras “greatly raise the success rate” of tracking noise vehicles comes without figures." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents on Jieshou Road had written that the car's late-night passes seriously affected their quality of life and sleep. They saw it identified and referred for a modification review and fine within a day. The reports do not document whether the noise ceased afterwards." }, { "group": "organizational", "depth": 1, "reason": "The bureau closed one complaint in 24 hours and used the case to publicize its sound-camera enforcement. Its claim that the cameras “greatly raise the success rate” of tracking noise vehicles comes without figures." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents on Jieshou Road had written that the car's late-night passes seriously affected their quality of life and sleep. They saw it identified and referred for a modification review and fine within a day. The reports do not document whether the noise ceased afterwards." }, { "group": "organizational", "depth": 1, "reason": "The bureau closed one complaint in 24 hours and used the case to publicize its sound-camera enforcement. Its claim that the cameras “greatly raise the success rate” of tracking noise vehicles comes without figures." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "3,600元 (New Taiwan dollars)", "what": "maximum fine under the Noise Control Act to be imposed for the noise violation", "who": "the BMW's owner pays", "estimate": true, "quote": "後續將依噪音管制法進行裁罰，最高可處3,600元罰鍰，並通知限期改善，屆期未改善者將按次處罰直至符合機動車輛噪音管制標準為止。", "url": "https://n.yam.com/Article/20250429841522" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "3,600元 (New Taiwan dollars)", "what": "maximum fine under the Noise Control Act to be imposed for the noise violation", "who": "the BMW's owner pays", "estimate": true, "quote": "後續將依噪音管制法進行裁罰，最高可處3,600元罰鍰，並通知限期改善，屆期未改善者將按次處罰直至符合機動車輛噪音管制標準為止。", "url": "https://n.yam.com/Article/20250429841522" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "3,600元 (New Taiwan dollars)", "what": "maximum fine under the Noise Control Act to be imposed for the noise violation", "who": "the BMW's owner pays", "estimate": true, "quote": "後續將依噪音管制法進行裁罰，最高可處3,600元罰鍰，並通知限期改善，屆期未改善者將按次處罰直至符合機動車輛噪音管制標準為止。", "url": "https://n.yam.com/Article/20250429841522" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-04-29-1f62" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-01-08", "note": "By January 2026 Taoyuan reported 50 sound-camera sets installed citywide, noise complaints down 38% since the Quiet Taoyuan project began, and an AI voiceprint system being integrated into the sound cameras.", "quote": "截至114年，全市已設置50套聲音照相設備，每套採購金額約介於90萬至130萬元之間，屬於中長期噪音治理的重要基礎建設。", "url": "https://www.ctee.com.tw/news/20260108701305-431401" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0129" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": "The residents' good, noise abatement, has no type; the documented gain is the bureau's one-day enforcement cycle." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Residents of Bade District, Taoyuan, complained in the Facebook group 記憶八德. A white BMW with a loud exhaust kept roaring down Jieshou Road late at night. The Taoyuan City Department of Environmental Protection received the complaint on the night of 22 April 2025. Its Quiet Taoyuan (靜桃) task force immediately set up AI sound camera equipment at the relevant spots. By analyzing the noise data against the complaints, it worked out where and when the car appeared. On 23 April environmental and police officers stopped it near Daxing Road. The car was referred to the motor vehicle office for a modification review. A Noise Control Act fine of up to NT$3,600 and an improvement order are to follow.", "benefit_type": "Improved Organizational Efficiency" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Residents on Jieshou Road had written that the car's late-night passes seriously affected their quality of life and sleep. They saw it identified and referred for a modification review and fine within a day. The reports do not document whether the noise ceased afterwards." }, { "group": "organizational", "depth": 1, "reason": "The bureau closed one complaint in 24 hours and used the case to publicize its sound-camera enforcement. Its claim that the cameras “greatly raise the success rate” of tracking noise vehicles comes without figures." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The bureau places mobile AI sound cameras on main roads and residential streets to collect noise data. Combined with complaint data, this gives the time and place of illegally modified noisy vehicles. Locating this car was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Taoyuan, Taiwan", "deployer_country": "Taiwan" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The bureau places mobile AI sound cameras on main roads and residential streets to collect noise data. Combined with complaint data, this gives the time and place of illegally modified noisy vehicles. Locating this car was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-04-23", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as benefit created or restored as the classifier proposed: the AI located a car that had already been causing the nuisance, and the dated benefit is a one-day enforcement cycle rather than the prevention of a specific future harm. Every report reproduces the Department of Environmental Protection's 29 April statement, with photographs credited to the bureau; the Liberty Times piece carries its own reporter's byline and quotes the director, 顏己喨. The bureau's own press release could not be retrieved (dep.tycg.gov.tw did not respond and tycg.gov.tw sits behind anti-DDoS protection), and the UDN link on the Monitor returns 404. Corrections to the Monitor summary: the fine had not been imposed at the time of reporting; the reports say the car will subsequently be fined under the Noise Control Act (up to NT$3,600) after referral to the motor vehicle office for a modification review. “BMW” appears in the Taoyuan E-News and Guan Media versions; the Liberty Times and Taiwan Hot News say only “white noise car”. The Monitor's “Economic/Property” harm to the general public is the nuisance the car caused, not anything the AI did. Dated to the stop on 23 April 2025; the complaint arrived on the night of 22 April; the Monitor's ID date (29 April) is the press coverage.", "source": "https://oecd.ai/en/incidents/2025-04-29-1f62", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://n.yam.com/Article/20250429841522; https://www.ctee.com.tw/news/20260108701305-431401; https://n.yam.com/Article/20250429841522", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Taoyuan_City_Government", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A complaint that named the road and the time window (late night on Jieshou Road). This told the task force where to place the cameras the same night.", "Mobile AI sound cameras already in the bureau's inventory under the Quiet Taoyuan project. Deployment did not wait on procurement.", "A standing environmental-police joint enforcement arrangement that could put officers at the predicted place and time on 23 April." ], "must_hold": [ "The target must be a repeat offender with a pattern. The method matches noise data against complaint times and places, and cannot catch a one-off pass.", "Officers must be free to intercept on the night the analysis points to. The cameras locate; they do not stop.", "A legal basis for the follow-up: here the Noise Control Act (fine up to NT$3,600 and an improvement order). Also the Road Traffic Management and Penalty Act (up to NT$24,000 for a removed muffler)." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as benefit created or restored as the classifier proposed: the AI located a car that had already been causing the nuisance, and the dated benefit is a one-day enforcement cycle rather than the prevention of a specific future harm. Every report reproduces the Department of Environmental Protection's 29 April statement, with photographs credited to the bureau; the Liberty Times piece carries its own reporter's byline and quotes the director, 顏己喨. The bureau's own press release could not be retrieved (dep.tycg.gov.tw did not respond and tycg.gov.tw sits behind anti-DDoS protection), and the UDN link on the Monitor returns 404. Corrections to the Monitor summary: the fine had not been imposed at the time of reporting; the reports say the car will subsequently be fined under the Noise Control Act (up to NT$3,600) after referral to the motor vehicle office for a modification review. “BMW” appears in the Taoyuan E-News and Guan Media versions; the Liberty Times and Taiwan Hot News say only “white noise car”. The Monitor's “Economic/Property” harm to the general public is the nuisance the car caused, not anything the AI did. Dated to the stop on 23 April 2025; the complaint arrived on the night of 22 April; the Monitor's ID date (29 April) is the press coverage.", "source": "https://oecd.ai/en/incidents/2025-04-29-1f62", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://n.yam.com/Article/20250429841522; https://www.ctee.com.tw/news/20260108701305-431401; https://n.yam.com/Article/20250429841522", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Taoyuan_City_Government", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Environmental enforcement (vehicle noise control)", "segment": "Local government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Cheng Shu-ting (鄭淑婷) / Liberty Times 自由時報 (2025). 桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰 [Taoyuan Bade's 'Jieshou Road public enemy' noise car caught; environmental and police enforcement and penalty]. Liberty Times Net, 29 April 2025.", "title": "桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰", "url": "https://news.ltn.com.tw/news/society/breakingnews/5026699", "date": "2025-04-29", "written_by": "independent", "quote": "靜桃專案小組立即於相關地點位佈設AI聲音照相設備" }, { "kind": "news report", "citation": "Yeh Chih-cheng (葉志成) / Taiwan Hot News 台灣好新聞, via Yahoo News Taiwan (2025). 八德介壽路噪音車引民怨 環警24小時追查到案裁罰 [Bade Jieshou Road noise car draws complaints; environmental and police track it down and penalise within 24 hours]. Yahoo News Taiwan, 29 April 2025.", "title": "八德介壽路噪音車引民怨　環警24小時追查到案裁罰", "url": "https://tw.news.yahoo.com/%E5%85%AB%E5%BE%B7%E4%BB%8B%E5%A3%BD%E8%B7%AF%E5%99%AA%E9%9F%B3%E8%BB%8A%E5%BC%95%E6%B0%91%E6%80%A8-%E7%92%B0%E8%AD%A624%E5%B0%8F%E6%99%82%E8%BF%BD%E6%9F%A5%E5%88%B0%E6%A1%88%E8%A3%81%E7%BD%B0-161300728.html", "date": "2025-04-29", "written_by": "independent", "quote": "本案在接獲陳情後24小時內即查到違規的民眾" }, { "kind": "news report", "citation": "Taoyuan E-News 桃園電子報, via yam News 蕃新聞 (2025). 八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人 [Bade BMW hooligan blasts the street at night; Taoyuan environmental and police nab him within 24 hours]. yam News, 29 April 2025.", "title": "八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人", "url": "https://n.yam.com/Article/20250429591252", "date": "2025-04-29", "written_by": "independent", "quote": "有民眾發文抱怨1台白色BMW噪音車常於八德區介壽路呼嘯而過" }, { "kind": "news report", "citation": "Tseng Ping (曾平) / 觀傳媒, via yam News 蕃新聞 (2025). 深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰 [Late-night exhaust bangs disturb residents; Taoyuan EPB and police track down the owner for penalty]. yam News, 29 April 2025.", "title": "深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰", "url": "https://n.yam.com/Article/20250429841522", "date": "2025-04-29", "written_by": "independent", "quote": "桃園市大量布設「改良式移動聲音照相」於主要道路與住宅區" }, { "kind": "news report", "citation": "工商時報 CTEE (2026). 噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統. 工商時報, 8 January 2026.", "title": "噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統", "url": "https://www.ctee.com.tw/news/20260108701305-431401", "date": "2026-01-08", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0129-source-0001", "AIBD-0129-source-0002", "AIBD-0129-source-0003", "AIBD-0129-source-0004", "AIBD-0129-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Cheng Shu-ting (鄭淑婷) / Liberty Times 自由時報 (2025). 桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰 [Taoyuan Bade's 'Jieshou Road public enemy' noise car caught; environmental and police enforcement and penalty]. Liberty Times Net, 29 April 2025.", "title": "桃園八德「介壽路公敵」噪音車抓到了 環、警查緝裁罰", "url": "https://news.ltn.com.tw/news/society/breakingnews/5026699", "date": "2025-04-29", "written_by": "independent", "quote": "靜桃專案小組立即於相關地點位佈設AI聲音照相設備" }, { "kind": "news report", "citation": "Yeh Chih-cheng (葉志成) / Taiwan Hot News 台灣好新聞, via Yahoo News Taiwan (2025). 八德介壽路噪音車引民怨 環警24小時追查到案裁罰 [Bade Jieshou Road noise car draws complaints; environmental and police track it down and penalise within 24 hours]. Yahoo News Taiwan, 29 April 2025.", "title": "八德介壽路噪音車引民怨　環警24小時追查到案裁罰", "url": "https://tw.news.yahoo.com/%E5%85%AB%E5%BE%B7%E4%BB%8B%E5%A3%BD%E8%B7%AF%E5%99%AA%E9%9F%B3%E8%BB%8A%E5%BC%95%E6%B0%91%E6%80%A8-%E7%92%B0%E8%AD%A624%E5%B0%8F%E6%99%82%E8%BF%BD%E6%9F%A5%E5%88%B0%E6%A1%88%E8%A3%81%E7%BD%B0-161300728.html", "date": "2025-04-29", "written_by": "independent", "quote": "本案在接獲陳情後24小時內即查到違規的民眾" }, { "kind": "news report", "citation": "Taoyuan E-News 桃園電子報, via yam News 蕃新聞 (2025). 八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人 [Bade BMW hooligan blasts the street at night; Taoyuan environmental and police nab him within 24 hours]. yam News, 29 April 2025.", "title": "八德BMW猴深夜炸街擾民 桃園環警24小時內火速逮人", "url": "https://n.yam.com/Article/20250429591252", "date": "2025-04-29", "written_by": "independent", "quote": "有民眾發文抱怨1台白色BMW噪音車常於八德區介壽路呼嘯而過" }, { "kind": "news report", "citation": "Tseng Ping (曾平) / 觀傳媒, via yam News 蕃新聞 (2025). 深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰 [Late-night exhaust bangs disturb residents; Taoyuan EPB and police track down the owner for penalty]. yam News, 29 April 2025.", "title": "深夜「屁砲聲」擾民 桃園環保局及警方追查車主到案裁罰", "url": "https://n.yam.com/Article/20250429841522", "date": "2025-04-29", "written_by": "independent", "quote": "桃園市大量布設「改良式移動聲音照相」於主要道路與住宅區" }, { "kind": "news report", "citation": "工商時報 CTEE (2026). 噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統. 工商時報, 8 January 2026.", "title": "噪音車有感下降！靜桃專案陳情減38％ 再引入AI科技聲紋系統", "url": "https://www.ctee.com.tw/news/20260108701305-431401", "date": "2026-01-08", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Taoyuan's AI sound cameras pinpointed a white BMW noise car the day after residents complained; officers caught it on 23 April 2025.", "account": "Residents of Bade District, Taoyuan, complained in the Facebook group 記憶八德. A white BMW with a loud exhaust kept roaring down Jieshou Road late at night. The Taoyuan City Department of Environmental Protection received the complaint on the night of 22 April 2025. Its Quiet Taoyuan (靜桃) task force immediately set up AI sound camera equipment at the relevant spots. By analyzing the noise data against the complaints, it worked out where and when the car appeared. On 23 April environmental and police officers stopped it near Daxing Road. The car was referred to the motor vehicle office for a modification review. A Noise Control Act fine of up to NT$3,600 and an improvement order are to follow." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI sound camera equipment (AI聲音照相) of the Quiet Taoyuan (靜桃) project, part of the bureau's network of 'improved mobile sound cameras' (改良式移動聲音照相)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Taoyuan's AI sound cameras locate a white BMW that had been roaring down Jieshou Road at night, within a day of residents' complaint", "headline": "Taoyuan's AI sound cameras pinpoint a noisy BMW within a day" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "106c762dbbd43015b21d4b7a1ccbd451fcaffa175762bbd5ee479a6c176b9ed8" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Residents of Bade District, Taoyuan, complained in the Facebook group 記憶八德. A white BMW with a loud exhaust kept roaring down Jieshou Road late at night. The Taoyuan City Department of Environmental Protection received the complaint on the night of 22 April 2025. Its Quiet Taoyuan (靜桃) task force immediately set up AI sound camera equipment at the relevant spots. By analyzing the noise data against the complaints, it worked out where and when the car appeared. On 23 April environmental and police officers stopped it near Daxing Road. The car was referred to the motor vehicle office for a modification review. A Noise Control Act fine of up to NT$3,600 and an improvement order are to follow.", "replication": { "produced_by": [ "A complaint that named the road and the time window (late night on Jieshou Road). This told the task force where to place the cameras the same night.", "Mobile AI sound cameras already in the bureau's inventory under the Quiet Taoyuan project. Deployment did not wait on procurement.", "A standing environmental-police joint enforcement arrangement that could put officers at the predicted place and time on 23 April." ], "must_hold": [ "The target must be a repeat offender with a pattern. The method matches noise data against complaint times and places, and cannot catch a one-off pass.", "Officers must be free to intercept on the night the analysis points to. The cameras locate; they do not stop.", "A legal basis for the follow-up: here the Noise Control Act (fine up to NT$3,600 and an improvement order). Also the Road Traffic Management and Penalty Act (up to NT$24,000 for a removed muffler)." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0349", "case_url": "records/AIBD-0349.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use." }, { "label": "AI contribution", "value": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Turkey", "Greece", "Romania", "Bulgaria" ] }, "record": { "id": "AIBD-0349", "title": "Google's Android Earthquake Alerts detects the Mw 6.2 Marmara Ereğlisi earthquake 5.31 s after origin and alerts over 16 million users on 23 April 2025; evaluation is Google-authored", "summary": "AEA detected the 23 April 2025 Mw 6.2 Marmara Ereğlisi quake in 5.31 s and alerted 16 million+ users (median 56 s warning for weak shaking); Google-authored evaluation; inland Take Action shortfall.", "date_observed": "2025-04-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "category": "protective response", "unexpected": false, "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use.", "system": "Android Earthquake Alerts (AEA) system", "model_version": null, "developer": "Google", "deployer": "Google (alerts delivered directly to Android users; launched in Türkiye in June 2021)", "location": "Sea of Marmara / Istanbul region, Türkiye (alerts also in Greece, Romania and Bulgaria)", "domain": "Earthquake early warning / public safety", "segment": "Consumer operating-system safety feature", "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "depth": null, "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ], "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "model_version": null, "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "date": null, "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "model_version": null, "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] }, "paired_incident": null, "headline": "Google's AEA alerts 16 million+ users to Marmara Ereğlisi quake", "metric": "alerted 16 million+ users (median 56 s warning for weak shaking)", "comparator": { "against": "local seismic networks (AFAD stations)", "before": null, "after": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users.", "basis": "measured", "quote": "A key finding was the earlier detection of seismic signals by smartphone accelerometers compared to the wavefield’s arrival at local seismic networks in this specific geography.", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" }, "later": { "status": "persisted", "date": "2025-09-16", "note": "Google reports the system still operating in Türkiye after the event, with more than 116 million alerts issued for 203 detected events since June 2021 as of 16 September 2025.", "quote": "Since AEA’s launch in Türkiye in June 2021, more than 116 million alerts have been issued for 203 detected events in the country (as of Sep 16, 2025)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and four peer-reviewed studies from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0349", "catalog_version": "1.0.0", "source_fingerprint": "5d5b66e01eda69fa53abd5912285d70540631339d1048ba27dde67b5a1cf8ba7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0349-source-0001", "AIBD-0349-source-0002", "AIBD-0349-source-0003", "AIBD-0349-source-0004", "AIBD-0349-source-0005", "AIBD-0349-source-0006", "AIBD-0349-source-0007", "AIBD-0349-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Google (alerts delivered directly to Android users; launched in Türkiye in June 2021)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Android Earthquake Alerts detected the earthquake from phone accelerometers and issued warnings. The authors report detection 5.31 seconds after the earthquake began and an alert within a further 0.43 seconds.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The system itself generated the detection and the alerts. A few hundred phones triggered on the P wave. The system detected the event 5.31 s after origin and issued alerts within 0.43 s. Magnitude estimates were updated over the next 12 s. All performance figures and user-response data come from Google-authored papers (Mousavi et al. 2026; Allen et al. 2025). That is said plainly here. The only independent cited source, the BBC, concerns the 2023 underestimation. The read papers describe the detection as an on-phone algorithm that analyzes acceleration time series for sudden changes. They do not use the terms machine learning or artificial intelligence. Whether the system uses AI is not established by the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and four peer-reviewed studies from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "alerted 16 million+ users (median 56 s warning for weak shaking)", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Google-authored papers supply the performance figures and user responses. The sources describe an algorithm that detects changes in acceleration but do not establish that it uses machine learning. The independent BBC report concerns a different earthquake.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-09-16", "note": "Google reports the system still operating in Türkiye after the event, with more than 116 million alerts issued for 203 detected events since June 2021 as of 16 September 2025.", "quote": "Since AEA’s launch in Türkiye in June 2021, more than 116 million alerts have been issued for 203 detected events in the country (as of Sep 16, 2025)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0349" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Google's feedback data say recipients were likely to drop, cover and hold on or warn others. No individual injury avoided is documented. The data are vendor-collected." }, { "group": "organizational", "reason": "No organization's decision or operation is documented as changed by the alerts." }, { "group": "societal", "depth": 2, "reason": "More than 16 million users were alerted with a median 56 s warning for weak shaking. “Take Action” alerts did not cover inland MMI V areas in time. No reduction in injuries is demonstrated. Evidence is vendor-authored." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Sea of Marmara / Istanbul region, Türkiye (alerts also in Greece, Romania and Bulgaria)", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Crowd-sourced earthquake detection and early warning delivered to Android phones in countries without dense seismic networks. The alerts were the intended use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-04-23", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Earthquake early warning / public safety", "segment": "Consumer operating-system safety feature" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0349-source-0001", "AIBD-0349-source-0002", "AIBD-0349-source-0003", "AIBD-0349-source-0004", "AIBD-0349-source-0005", "AIBD-0349-source-0006", "AIBD-0349-source-0007", "AIBD-0349-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., Barski, A., Bosch, R., Thiruverahan, N., Cho, Y., Gadh, T., Malkos, S., Spooner, B., Wimpey, G., Shi, E., Stogaitis, M. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Authors affiliated with Google LLC and Google Germany GmbH.", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake", "url": "https://doi.org/10.1038/s41467-026-76413-7", "date": "2026-08-11", "written_by": "makers", "quote": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, abstract, warning times.", "title": "Performance and user response of android's smartphone-based alerts (warning times)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Methods, detection and alerting.", "title": "Performance and user response of android's smartphone-based alerts (detection method)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "An on-phone detection algorithm analyzes acceleration time series for sudden changes indicative of P- or S-wave arrivals." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M. et al. (2026). Same article, Results, alert performance.", "title": "Performance and user response of android's smartphone-based alerts (Take Action shortfall)", "url": "https://europepmc.org/article/PMC/PMC13558587", "date": "2026-08-11", "written_by": "makers", "quote": "Consequently, inland populations who experienced moderate ground motions (MMI V) did not receive a “Take Action” alert before the arrival of the stronger shaking." }, { "kind": "peer-reviewed study", "citation": "Allen, R. M., Barski, A., Berman, M., Bosch, R., Cho, Y., Jiang, X. S., Lee, Y. L., Malkos, S., Mousavi, S. M., Robertson, P., Spooner, B., Stogaitis, M., Thiruverahan, N., Wimpey, G. (2025). Global earthquake detection and warning using Android phones. Science, 17 July 2025. doi:10.1126/science.ads4779 (Google-authored).", "title": "Global earthquake detection and warning using Android phones", "url": "https://europepmc.org/abstract/MED/40674485", "date": "2025-07-17", "written_by": "makers", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Google failed to warn 10 million of Turkey earthquake severity. BBC News, 27 July 2025.", "title": "Google failed to warn 10 million of Turkey earthquake severity", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "Instead, only 469 \"Take Action\" warnings were sent out for the first 7.8 magnitude quake." }, { "kind": "news report", "citation": "Clayton, J., Foster, A., Derico, B. (2025). Same article, magnitude estimate.", "title": "Google failed to warn 10 million of Turkey earthquake severity (magnitude estimate)", "url": "https://www.bbc.com/news/articles/c77v2kx304go", "date": "2025-07-27", "written_by": "independent", "quote": "For the first earthquake, the system estimated the shaking at between 4.5 and 4.9 on the moment magnitude scale (MMS) when it was actually a 7.8." }, { "kind": "peer-reviewed study", "citation": "Mousavi, S. M., Robertson, P., Allen, R. M., et al. (2026). Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake. Nature Communications, 11 August 2026. doi:10.1038/s41467-026-76413-7. Full text as served by the Europe PMC REST API (PMC13558587).", "title": "Performance and user response of android's smartphone-based alerts in the 2025 Marmara Ereğlisi earthquake (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML", "date": "2026-08-11", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AEA detected the 23 April 2025 Mw 6.2 Marmara Ereğlisi quake in 5.31 s and alerted 16 million+ users (median 56 s warning for weak shaking); Google-authored evaluation; inland Take Action shortfall.", "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts (AEA) system" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts detects the Mw 6.2 Marmara Ereğlisi earthquake 5.31 s after origin and alerts over 16 million users on 23 April 2025; evaluation is Google-authored", "headline": "Google's AEA alerts 16 million+ users to Marmara Ereğlisi quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5d5b66e01eda69fa53abd5912285d70540631339d1048ba27dde67b5a1cf8ba7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 23 April 2025 the Mw 6.2 Marmara Ereğlisi earthquake struck offshore in the Sea of Marmara, Türkiye. Initial reports cited by the evaluation put shaking in Istanbul at MMI V. At least 359 people were injured, mostly from panic. Google's Android Earthquake Alerts (AEA) system uses the accelerometers of online Android phones. An on-phone detection algorithm flags sudden changes in acceleration. The system detected the event 5.31 seconds after its origin. It issued a first “Be Aware” alert 0.43 seconds later on an initial magnitude estimate of Mw 4.83. “Take Action” alerts followed as the estimate rose. Alerts reached more than 16 million Android users in Türkiye, Greece, Romania and Bulgaria. Warning times for weak shaking (MMI III) reached up to 150 seconds with a median of 56 seconds. The authors report 90% true positives and 99% precision. The final magnitude estimate saturated at Mw 6.04, so the “Take Action” polygon was too small. Inland populations that felt MMI V shaking received only the “Be Aware” warning before stronger shaking arrived. The evaluation (Nature Communications, 11 August 2026) and the system description (Science, 17 July 2025) are written by Google employees. So is the user-feedback data. The same system underestimated the 6 February 2023 Türkiye earthquakes. The BBC reported on 27 July 2025 that only 469 “Take Action” alerts were sent for the M7.8 event. 10 million people could have received one.", "replication": { "produced_by": [ "Dense network of online Android phones (about 66,000 used for characterization) around an offshore epicentre", "On-phone trigger on P-wave acceleration and server-side magnitude estimation updated over 12 s", "Two-tier alerting (Be Aware, Take Action) delivered through the operating system", "A population with high earthquake awareness after the 2023 Kahramanmaraş disaster, per the authors" ], "must_hold": [ "Enough stationary, charging phones near the epicentre to trigger early", "Magnitude estimates that do not saturate below the true magnitude. This failed here at Mw 6.04 and failed badly in 2023.", "Alerts that outrun S-wave arrival at the populated distance", "Users who recognize and act on the alert" ], "echoes": [ { "date": "2023-02-06", "setting": "Mw 7.8 and Mw 7.5 earthquakes, south-east Türkiye: magnitude estimated at 4.5–4.9; 469 Take Action alerts sent for the first quake when 10 million people could have received one (BBC)", "outcome": "did not recur", "source": "https://www.bbc.com/news/articles/c77v2kx304go" }, { "setting": "Global operation over three years: alerts in 98 countries, about 60 events and 18 million alerts per month; 85% of alert recipients felt shaking (Science 2025, Google-authored)", "outcome": "recurred", "source": "https://europepmc.org/abstract/MED/40674485" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0126", "case_url": "records/AIBD-0126.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "The software was deployed to identify people in CCTV footage by matching faces against police databases. Naming a robbery suspect from a frame is that function." }, { "label": "AI contribution", "value": "By DCP (North) Raja Banthia's account, teams took a screengrab of a man seen following the victim. The Israeli software matched it to a prior record from Kotwali police station with a similarity score of 32.09 percent. That gave them Ali's name and his Daryaganj address. A score that low is a lead, not an identification. Police confirmed it by laying a trap and interrogating Ali. The auto-rickshaw trace, the reconnaissance evidence and Samir's arrest came from conventional work. The reports name neither the software nor its vendor." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0126", "title": "Delhi Police matched a CCTV frame to a prior arrest record with Israeli facial recognition software, arrested two men within about a day of a Rs 80 lakh armed robbery in Chandni Chowk and recovered Rs 79.5 lakh", "summary": "Facial recognition on CCTV named a Chandni Chowk robber; two arrested within about a day and Rs 79.5 lakh of the Rs 80 lakh recovered.", "date_observed": "2025-03-19", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On the evening of 17 March 2025, cash courier Ajmal Bhai Ganesh, 49, of R K Enterprises was robbed at gunpoint of Rs 80 lakh. It was near his Fatehpuri home, Chandni Chowk, Delhi. Delhi Police's North district analyzed some 500 closed-circuit television (CCTV) cameras' footage. Israeli facial recognition software matched a frame of a man who had followed the victim to a record at Kotwali police station. That gave Mohammad Ali, 21, of Daryaganj. Ali was arrested early on 19 March (the Times of India says Tuesday) and named Samir, 19, arrested in Daryaganj. A raid on Samir's home found Rs 79.5 lakh and a semi-automatic pistol, which police called the entire amount.", "category": "benefit created", "unexpected": false, "purpose": "The software was deployed to identify people in CCTV footage by matching faces against police databases. Naming a robbery suspect from a frame is that function.", "system": "Israeli facial recognition software used by Delhi Police (not named in the reports)", "model_version": null, "developer": null, "deployer": "Delhi Police, North district", "location": "Delhi, India", "domain": "Policing / robbery investigation", "segment": "CCTV-based suspect identification in an armed robbery case", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-03-19-2b12" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Ajmal Bhai Ganesh and his employer R K Enterprises recovered Rs 79.5 lakh of the Rs 80 lakh taken at gunpoint. Police described that as the entire amount. A large material loss reversed within two days." }, { "group": "organizational", "depth": 2, "reason": "Delhi Police's North district cleared an armed robbery whose video had gone viral within about 24 hours. Both accused are in judicial custody under the BNS and Arms Act and the pistol recovered." }, { "group": "societal", "depth": 1, "reason": "Two men who had surveilled cash couriers and used a firearm were taken off the street. The reports give no evidence of further robberies prevented, so the public benefit is small." } ], "ai_role": { "status": "assessed", "statement": "By DCP (North) Raja Banthia's account, teams took a screengrab of a man seen following the victim. The Israeli software matched it to a prior record from Kotwali police station with a similarity score of 32.09 percent. That gave them Ali's name and his Daryaganj address. A score that low is a lead, not an identification. Police confirmed it by laying a trap and interrogating Ali. The auto-rickshaw trace, the reconnaissance evidence and Samir's arrest came from conventional work. The reports name neither the software nor its vendor." }, "evidence": [ { "kind": "news report", "citation": "The Times of India (2025). Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs. The Times of India, Delhi, 19 March 2025.", "title": "Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs", "url": "https://timesofindia.indiatimes.com/city/delhi/israeli-face-recognition-software-ends-robbers-run-within-24-hrs/articleshow/119222102.cms", "date": "2025-03-19", "written_by": "independent", "quote": "A breakthrough came when Mohammad Ali's face was captured on CCTV near the crime scene. Using Israeli facial recognition software, our teams matched it with a prior criminal record" }, { "kind": "news report", "citation": "The Indian Express (2025). CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader. The Indian Express, 20 March 2025.", "title": "CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader", "url": "https://indianexpress.com/article/cities/delhi/cctv-footage-face-recognition-tech-how-delhi-cops-nabbed-two-men-who-stole-rs-80-lakh-from-trader-9895565/", "date": "2025-03-20", "written_by": "independent", "quote": "As police analysed footage from around 500 CCTV cameras, they identified Ali in the clips — as he followed Ajmal — through a facial recognition system." }, { "kind": "news report", "citation": "Press Trust of India via Hindustan Times (2025). Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held. Hindustan Times, 19 March 2025.", "title": "Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held", "url": "https://www.hindustantimes.com/india-news/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-2-held-101742373825793.html", "date": "2025-03-19", "written_by": "independent", "quote": "An Israeli facial recognition system has helped Delhi Police solve a case of robbery at gunpoint, arrest two accused persons, and recover the entire stolen amount of ₹80 lakh" }, { "kind": "news report", "citation": "Press Trust of India via Rediff.com (2025). Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held. Rediff.com, 19 March 2025.", "title": "Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held", "url": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "date": "2025-03-19", "written_by": "independent", "quote": "The similarity score was 32.09 percent. We got to know that the accused lived in Daryaganj, where teams were already working to trace the Scooty used in the crime" }, { "kind": "news report", "citation": "The New Indian Express (2025). Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held. The New Indian Express, 20 March 2025.", "title": "Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held", "url": "https://www.newindianexpress.com/cities/delhi/2025/Mar/20/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-two-held", "date": "2025-03-20", "written_by": "independent", "quote": "Facial recognition technology identified one suspect as Mohammad Ali, who had been arrested previously in a similar case, Deputy Commissioner of Police (North) Raja Banthia said." } ], "replication": { "produced_by": [ "Dense CCTV coverage (about 500 cameras across five areas) from which a usable frame of the suspect's face could be taken", "A police facial recognition database that already held the suspect's photograph from a prior arrest at Kotwali police station" ], "must_hold": [ "A low-similarity match (32.09 percent) is only a lead and must be confirmed by conventional investigation before arrest", "The suspect must already be in the database; a first-time offender would not have been matched" ], "echoes": [] }, "paired_incident": null, "headline": "Delhi Police facial recognition leads to Rs 79.5 lakh recovery", "metric": "two arrested within about a day and Rs 79.5 lakh of the Rs 80 lakh recovered", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No chargesheet, trial or other development after the 19-20 March 2025 arrest reports was found; the reports say only that both accused were booked.", "quote": null, "url": null }, "value": { "amount": "Rs 79.5 lakh", "what": "stolen cash recovered", "who": "the robbed cash courier's firm, R K Enterprises (recovered by Delhi Police from the accused's residence)", "estimate": false, "quote": "A raid at Samir's residence led to the recovery of Rs 79.5 lakh in cash, a semi-automatic pistol with three bullets, and other incriminating materials.", "url": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm" }, "developer_country": null, "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-19-2b12", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Delhi_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0126", "catalog_version": "1.0.0", "source_fingerprint": "583679466697009ff9b744c37371ceccd1d79ea0ed2ace31dcb6a8f928a344e7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0126-source-0001", "AIBD-0126-source-0002", "AIBD-0126-source-0003", "AIBD-0126-source-0004", "AIBD-0126-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0126-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Delhi Police, North district" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "By DCP (North) Raja Banthia's account, teams took a screengrab of a man seen following the victim. The Israeli software matched it to a prior record from Kotwali police station with a similarity score of 32.09 percent. That gave them Ali's name and his Daryaganj address. A score that low is a lead, not an identification. Police confirmed it by laying a trap and interrogating Ali. The auto-rickshaw trace, the reconnaissance evidence and Samir's arrest came from conventional work. The reports name neither the software nor its vendor." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "By DCP (North) Raja Banthia's account, teams took a screengrab of a man seen following the victim. The Israeli software matched it to a prior record from Kotwali police station with a similarity score of 32.09 percent. That gave them Ali's name and his Daryaganj address. A score that low is a lead, not an identification. Police confirmed it by laying a trap and interrogating Ali. The auto-rickshaw trace, the reconnaissance evidence and Samir's arrest came from conventional work. The reports name neither the software nor its vendor." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Ajmal Bhai Ganesh and his employer R K Enterprises recovered Rs 79.5 lakh of the Rs 80 lakh taken at gunpoint. Police described that as the entire amount. A large material loss reversed within two days." }, { "group": "organizational", "depth": 2, "reason": "Delhi Police's North district cleared an armed robbery whose video had gone viral within about 24 hours. Both accused are in judicial custody under the BNS and Arms Act and the pistol recovered." }, { "group": "societal", "depth": 1, "reason": "Two men who had surveilled cash couriers and used a firearm were taken off the street. The reports give no evidence of further robberies prevented, so the public benefit is small." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Ajmal Bhai Ganesh and his employer R K Enterprises recovered Rs 79.5 lakh of the Rs 80 lakh taken at gunpoint. Police described that as the entire amount. A large material loss reversed within two days." }, { "group": "organizational", "depth": 2, "reason": "Delhi Police's North district cleared an armed robbery whose video had gone viral within about 24 hours. Both accused are in judicial custody under the BNS and Arms Act and the pistol recovered." }, { "group": "societal", "depth": 1, "reason": "Two men who had surveilled cash couriers and used a firearm were taken off the street. The reports give no evidence of further robberies prevented, so the public benefit is small." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Ajmal Bhai Ganesh and his employer R K Enterprises recovered Rs 79.5 lakh of the Rs 80 lakh taken at gunpoint. Police described that as the entire amount. A large material loss reversed within two days." }, { "group": "organizational", "depth": 2, "reason": "Delhi Police's North district cleared an armed robbery whose video had gone viral within about 24 hours. Both accused are in judicial custody under the BNS and Arms Act and the pistol recovered." }, { "group": "societal", "depth": 1, "reason": "Two men who had surveilled cash couriers and used a firearm were taken off the street. 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external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "Rs 79.5 lakh", "what": "stolen cash recovered", "who": "the robbed cash courier's firm, R K Enterprises (recovered by Delhi Police from the accused's residence)", "estimate": false, "quote": "A raid at Samir's residence led to the recovery of Rs 79.5 lakh in cash, a semi-automatic pistol with three bullets, and other incriminating materials.", "url": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On the evening of 17 March 2025, cash courier Ajmal Bhai Ganesh, 49, of R K Enterprises was robbed at gunpoint of Rs 80 lakh. It was near his Fatehpuri home, Chandni Chowk, Delhi. Delhi Police's North district analyzed some 500 closed-circuit television (CCTV) cameras' footage. Israeli facial recognition software matched a frame of a man who had followed the victim to a record at Kotwali police station. That gave Mohammad Ali, 21, of Daryaganj. Ali was arrested early on 19 March (the Times of India says Tuesday) and named Samir, 19, arrested in Daryaganj. A raid on Samir's home found Rs 79.5 lakh and a semi-automatic pistol, which police called the entire amount.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Ajmal Bhai Ganesh and his employer R K Enterprises recovered Rs 79.5 lakh of the Rs 80 lakh taken at gunpoint. Police described that as the entire amount. A large material loss reversed within two days." }, { "group": "organizational", "depth": 2, "reason": "Delhi Police's North district cleared an armed robbery whose video had gone viral within about 24 hours. Both accused are in judicial custody under the BNS and Arms Act and the pistol recovered." }, { "group": "societal", "depth": 1, "reason": "Two men who had surveilled cash couriers and used a firearm were taken off the street. The reports give no evidence of further robberies prevented, so the public benefit is small." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The software was deployed to identify people in CCTV footage by matching faces against police databases. Naming a robbery suspect from a frame is that function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Delhi, India", "deployer_country": "India" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The software was deployed to identify people in CCTV footage by matching faces against police databases. Naming a robbery suspect from a frame is that function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-03-19", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-19-2b12", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Delhi_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense CCTV coverage (about 500 cameras across five areas) from which a usable frame of the suspect's face could be taken", "A police facial recognition database that already held the suspect's photograph from a prior arrest at Kotwali police station" ], "must_hold": [ "A low-similarity match (32.09 percent) is only a lead and must be confirmed by conventional investigation before arrest", "The suspect must already be in the database; a first-time offender would not have been matched" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-19-2b12", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Delhi_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / robbery investigation", "segment": "CCTV-based suspect identification in an armed robbery case" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The Times of India (2025). Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs. The Times of India, Delhi, 19 March 2025.", "title": "Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs", "url": "https://timesofindia.indiatimes.com/city/delhi/israeli-face-recognition-software-ends-robbers-run-within-24-hrs/articleshow/119222102.cms", "date": "2025-03-19", "written_by": "independent", "quote": "A breakthrough came when Mohammad Ali's face was captured on CCTV near the crime scene. Using Israeli facial recognition software, our teams matched it with a prior criminal record" }, { "kind": "news report", "citation": "The Indian Express (2025). CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader. The Indian Express, 20 March 2025.", "title": "CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader", "url": "https://indianexpress.com/article/cities/delhi/cctv-footage-face-recognition-tech-how-delhi-cops-nabbed-two-men-who-stole-rs-80-lakh-from-trader-9895565/", "date": "2025-03-20", "written_by": "independent", "quote": "As police analysed footage from around 500 CCTV cameras, they identified Ali in the clips — as he followed Ajmal — through a facial recognition system." }, { "kind": "news report", "citation": "Press Trust of India via Hindustan Times (2025). Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held. Hindustan Times, 19 March 2025.", "title": "Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held", "url": "https://www.hindustantimes.com/india-news/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-2-held-101742373825793.html", "date": "2025-03-19", "written_by": "independent", "quote": "An Israeli facial recognition system has helped Delhi Police solve a case of robbery at gunpoint, arrest two accused persons, and recover the entire stolen amount of ₹80 lakh" }, { "kind": "news report", "citation": "Press Trust of India via Rediff.com (2025). Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held. Rediff.com, 19 March 2025.", "title": "Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held", "url": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "date": "2025-03-19", "written_by": "independent", "quote": "The similarity score was 32.09 percent. We got to know that the accused lived in Daryaganj, where teams were already working to trace the Scooty used in the crime" }, { "kind": "news report", "citation": "The New Indian Express (2025). Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held. The New Indian Express, 20 March 2025.", "title": "Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held", "url": "https://www.newindianexpress.com/cities/delhi/2025/Mar/20/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-two-held", "date": "2025-03-20", "written_by": "independent", "quote": "Facial recognition technology identified one suspect as Mohammad Ali, who had been arrested previously in a similar case, Deputy Commissioner of Police (North) Raja Banthia said." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0126-source-0001", "AIBD-0126-source-0002", "AIBD-0126-source-0003", "AIBD-0126-source-0004", "AIBD-0126-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "The Times of India (2025). Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs. The Times of India, Delhi, 19 March 2025.", "title": "Chandni Chowk heist: Israeli face recognition software ends robbers' run within 24 hrs", "url": "https://timesofindia.indiatimes.com/city/delhi/israeli-face-recognition-software-ends-robbers-run-within-24-hrs/articleshow/119222102.cms", "date": "2025-03-19", "written_by": "independent", "quote": "A breakthrough came when Mohammad Ali's face was captured on CCTV near the crime scene. Using Israeli facial recognition software, our teams matched it with a prior criminal record" }, { "kind": "news report", "citation": "The Indian Express (2025). CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader. The Indian Express, 20 March 2025.", "title": "CCTV footage, face recognition tech: How Delhi cops nabbed two men who stole Rs 80 lakh from trader", "url": "https://indianexpress.com/article/cities/delhi/cctv-footage-face-recognition-tech-how-delhi-cops-nabbed-two-men-who-stole-rs-80-lakh-from-trader-9895565/", "date": "2025-03-20", "written_by": "independent", "quote": "As police analysed footage from around 500 CCTV cameras, they identified Ali in the clips — as he followed Ajmal — through a facial recognition system." }, { "kind": "news report", "citation": "Press Trust of India via Hindustan Times (2025). Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held. Hindustan Times, 19 March 2025.", "title": "Israeli facial recognition tech helps Delhi Police crack ₹80 lakh robbery case; 2 held", "url": "https://www.hindustantimes.com/india-news/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-2-held-101742373825793.html", "date": "2025-03-19", "written_by": "independent", "quote": "An Israeli facial recognition system has helped Delhi Police solve a case of robbery at gunpoint, arrest two accused persons, and recover the entire stolen amount of ₹80 lakh" }, { "kind": "news report", "citation": "Press Trust of India via Rediff.com (2025). Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held. Rediff.com, 19 March 2025.", "title": "Israeli tech helps Delhi police crack Rs 80 lakh robbery; 2 held", "url": "https://www.rediff.com/news/report/israeli-tech-helps-delhi-police-crack-rs-80-lakh-robbery-2-held/20250319.htm", "date": "2025-03-19", "written_by": "independent", "quote": "The similarity score was 32.09 percent. We got to know that the accused lived in Daryaganj, where teams were already working to trace the Scooty used in the crime" }, { "kind": "news report", "citation": "The New Indian Express (2025). Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held. The New Indian Express, 20 March 2025.", "title": "Israeli facial recognition tech helps Delhi police crack Rs 80 lakh robbery case, two held", "url": "https://www.newindianexpress.com/cities/delhi/2025/Mar/20/israeli-facial-recognition-tech-helps-delhi-police-crack-rs-80-lakh-robbery-case-two-held", "date": "2025-03-20", "written_by": "independent", "quote": "Facial recognition technology identified one suspect as Mohammad Ali, who had been arrested previously in a similar case, Deputy Commissioner of Police (North) Raja Banthia said." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Facial recognition on CCTV named a Chandni Chowk robber; two arrested within about a day and Rs 79.5 lakh of the Rs 80 lakh recovered.", "account": "On the evening of 17 March 2025, cash courier Ajmal Bhai Ganesh, 49, of R K Enterprises was robbed at gunpoint of Rs 80 lakh. It was near his Fatehpuri home, Chandni Chowk, Delhi. Delhi Police's North district analyzed some 500 closed-circuit television (CCTV) cameras' footage. Israeli facial recognition software matched a frame of a man who had followed the victim to a record at Kotwali police station. That gave Mohammad Ali, 21, of Daryaganj. Ali was arrested early on 19 March (the Times of India says Tuesday) and named Samir, 19, arrested in Daryaganj. A raid on Samir's home found Rs 79.5 lakh and a semi-automatic pistol, which police called the entire amount." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Israeli facial recognition software used by Delhi Police (not named in the reports)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Delhi Police matched a CCTV frame to a prior arrest record with Israeli facial recognition software, arrested two men within about a day of a Rs 80 lakh armed robbery in Chandni Chowk and recovered Rs 79.5 lakh", "headline": "Delhi Police facial recognition leads to Rs 79.5 lakh recovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "583679466697009ff9b744c37371ceccd1d79ea0ed2ace31dcb6a8f928a344e7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On the evening of 17 March 2025, cash courier Ajmal Bhai Ganesh, 49, of R K Enterprises was robbed at gunpoint of Rs 80 lakh. It was near his Fatehpuri home, Chandni Chowk, Delhi. Delhi Police's North district analyzed some 500 closed-circuit television (CCTV) cameras' footage. Israeli facial recognition software matched a frame of a man who had followed the victim to a record at Kotwali police station. That gave Mohammad Ali, 21, of Daryaganj. Ali was arrested early on 19 March (the Times of India says Tuesday) and named Samir, 19, arrested in Daryaganj. A raid on Samir's home found Rs 79.5 lakh and a semi-automatic pistol, which police called the entire amount.", "replication": { "produced_by": [ "Dense CCTV coverage (about 500 cameras across five areas) from which a usable frame of the suspect's face could be taken", "A police facial recognition database that already held the suspect's photograph from a prior arrest at Kotwali police station" ], "must_hold": [ "A low-similarity match (32.09 percent) is only a lead and must be confirmed by conventional investigation before arrest", "The suspect must already be in the database; a first-time offender would not have been matched" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0382", "case_url": "records/AIBD-0382.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Police analysis software built to pick out vehicles and people likely to be carrying narcotics for checkpoint stops. This seizure was that use." }, { "label": "AI contribution", "value": "ASENA supplied the selection and nothing more. DHA's wording is that the vehicle was identified and stopped “thanks to the ASENA software”. The dog Hector then found the packages and officers made the arrests. No report describes ASENA's inputs, its ranking method, or whether an analyst reviewed the flag before the stop." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0382", "title": "ASENA narcotics analysis software flags a car at the Köy Hizmetleri checkpoint in Şırnak; the dog Hector finds 86.6 kg of skunk in 119 packages and three lawyers are remanded", "summary": "Şırnak police stopped a car selected by the ASENA narcotics analysis software; the dog Hector found 86.6 kg of skunk in 119 packages in the trunk, and three lawyers were remanded (March 2025).", "date_observed": "2025-03-17", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Şırnak Provincial Police stopped a vehicle at the Köy Hizmetleri (Village Services) police checkpoint after ASENA selected it for inspection. DHA calls ASENA police software “in artificial intelligence format”. The narcotics dog Hector then found 86 kilograms 600 grams of skunk in 119 packages hidden in the trunk. Three occupants, M.E., H.K. and Ş.A., were detained (Sözcü/DHA and Habertürk/DHA, 17 March 2025). Diken, citing İHA, gives ASENA's full name as Analiz Sistemleri Narkotik Ağı (Narcotics Analysis Systems Network). It says the three were lawyers registered with the Mardin Bar Association. They were taken to court after police processing and remanded in custody (Diken, 18 March 2025). None of the reports gives the day of the stop, what data ASENA analyzed or how it chose the car, or any official's name.", "category": "benefit created", "unexpected": false, "purpose": "Police analysis software built to pick out vehicles and people likely to be carrying narcotics for checkpoint stops. This seizure was that use.", "system": "ASENA (Analiz Sistemleri Narkotik Ağı), Turkish police narcotics analysis software", "model_version": null, "developer": null, "deployer": "Şırnak Provincial Police Directorate (Turkish National Police)", "location": "Şırnak, Turkey", "domain": "Law enforcement / narcotics interdiction", "segment": "Checkpoint vehicle screening", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-03-17-8c32" }, "ratings": [ { "group": "societal", "depth": 2, "reason": "86.6 kg of skunk was taken out of circulation and three suspects remanded. A measurable interdiction result, but the reports specify no downstream harm averted." }, { "group": "organizational", "depth": 2, "reason": "The Şırnak police turned a software flag into a large seizure and three remands, per the DHA and İHA reports. A measurable result for the unit." } ], "ai_role": { "status": "assessed", "statement": "ASENA supplied the selection and nothing more. DHA's wording is that the vehicle was identified and stopped “thanks to the ASENA software”. The dog Hector then found the packages and officers made the arrests. No report describes ASENA's inputs, its ranking method, or whether an analyst reviewed the flag before the stop." }, "evidence": [ { "kind": "news report", "citation": "Sözcü / DHA (2025). 86 kilo uyuşturucuyu narkotik köpeği buldu. Sözcü, March 17, 2025.", "title": "86 kilo uyuşturucuyu narkotik köpeği buldu", "url": "https://www.sozcu.com.tr/86-kilo-uyusturucuyu-narkotik-kopegi-buldu-p151719", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım" }, { "kind": "news report", "citation": "Habertürk / DHA (2025). Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu. Habertürk, March 17, 2025.", "title": "Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu", "url": "https://yerel-haberler.haberturk.com/sirnak-haberleri/durdurulan-aractaki-86-kilo-uyusturucuyu-hektor-buldu-112096987", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım olan ASENA yazılımı sayesinde" }, { "kind": "news report", "citation": "Diken / İHA (2025). Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı. Diken, March 18, 2025.", "title": "Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı", "url": "https://www.diken.com.tr/sirnakta-86-kilo-uyusturucuyla-yakalanan-uc-avukat-tutuklandi", "date": "2025-03-18", "written_by": "independent", "quote": "Analiz Sistemleri Narkotik Ağı" }, { "kind": "registry label", "citation": "OECD.AI (2025). AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested. OECD AI Incidents and Hazards Monitor, 2025-03-17.", "title": "AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested", "url": "https://oecd.ai/en/incidents/2025-03-17-8c32", "date": "2025-03-17", "written_by": "independent", "quote": "used the ASENA AI narcotics analysis system and detection dog Hector to intercept a vehicle, uncovering 86.6 kg of skunk cannabis" } ], "replication": { "produced_by": [ "A police analysis program that ranks vehicles for interdiction", "A fixed checkpoint at which a flagged car can be stopped and searched", "A narcotics dog and officers prepared to search the trunk" ], "must_hold": [ "The flag must be followed by a lawful stop and a physical search. The software only selects", "Seized material and arrests must be carried through prosecution" ], "echoes": [] }, "paired_incident": null, "headline": "ASENA flags a car at a Şırnak checkpoint; 86.6 kg of skunk found", "metric": "86.6 kg of skunk in 119 packages", "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "Turkey", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-17-8c32", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0382", "catalog_version": "1.0.0", "source_fingerprint": "dfb098d8d930258fe8fdd8c4942e4947cefe5cd57f667980f8d2a644a86efc2f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0382-source-0001", "AIBD-0382-source-0002", "AIBD-0382-source-0003", "AIBD-0382-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Şırnak Provincial Police Directorate (Turkish National Police)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "ASENA supplied the selection and nothing more. DHA's wording is that the vehicle was identified and stopped “thanks to the ASENA software”. The dog Hector then found the packages and officers made the arrests. No report describes ASENA's inputs, its ranking method, or whether an analyst reviewed the flag before the stop." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "ASENA supplied the selection and nothing more. DHA's wording is that the vehicle was identified and stopped “thanks to the ASENA software”. The dog Hector then found the packages and officers made the arrests. No report describes ASENA's inputs, its ranking method, or whether an analyst reviewed the flag before the stop." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "86.6 kg of skunk was taken out of circulation and three suspects remanded. A measurable interdiction result, but the reports specify no downstream harm averted." }, { "group": "organizational", "depth": 2, "reason": "The Şırnak police turned a software flag into a large seizure and three remands, per the DHA and İHA reports. A measurable result for the unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "86.6 kg of skunk was taken out of circulation and three suspects remanded. A measurable interdiction result, but the reports specify no downstream harm averted." }, { "group": "organizational", "depth": 2, "reason": "The Şırnak police turned a software flag into a large seizure and three remands, per the DHA and İHA reports. A measurable result for the unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "86.6 kg of skunk was taken out of circulation and three suspects remanded. A measurable interdiction result, but the reports specify no downstream harm averted." }, { "group": "organizational", "depth": 2, "reason": "The Şırnak police turned a software flag into a large seizure and three remands, per the DHA and İHA reports. A measurable result for the unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "86.6 kg of skunk in 119 packages", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-03-17-8c32" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0382" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Şırnak Provincial Police stopped a vehicle at the Köy Hizmetleri (Village Services) police checkpoint after ASENA selected it for inspection. DHA calls ASENA police software “in artificial intelligence format”. The narcotics dog Hector then found 86 kilograms 600 grams of skunk in 119 packages hidden in the trunk. Three occupants, M.E., H.K. and Ş.A., were detained (Sözcü/DHA and Habertürk/DHA, 17 March 2025). Diken, citing İHA, gives ASENA's full name as Analiz Sistemleri Narkotik Ağı (Narcotics Analysis Systems Network). It says the three were lawyers registered with the Mardin Bar Association. They were taken to court after police processing and remanded in custody (Diken, 18 March 2025). None of the reports gives the day of the stop, what data ASENA analyzed or how it chose the car, or any official's name.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "86.6 kg of skunk was taken out of circulation and three suspects remanded. A measurable interdiction result, but the reports specify no downstream harm averted." }, { "group": "organizational", "depth": 2, "reason": "The Şırnak police turned a software flag into a large seizure and three remands, per the DHA and İHA reports. A measurable result for the unit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Police analysis software built to pick out vehicles and people likely to be carrying narcotics for checkpoint stops. This seizure was that use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Şırnak, Turkey", "deployer_country": "Turkey" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Police analysis software built to pick out vehicles and people likely to be carrying narcotics for checkpoint stops. This seizure was that use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-03-17", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-17-8c32", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A police analysis program that ranks vehicles for interdiction", "A fixed checkpoint at which a flagged car can be stopped and searched", "A narcotics dog and officers prepared to search the trunk" ], "must_hold": [ "The flag must be followed by a lawful stop and a physical search. The software only selects", "Seized material and arrests must be carried through prosecution" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-17-8c32", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Law enforcement / narcotics interdiction", "segment": "Checkpoint vehicle screening" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Sözcü / DHA (2025). 86 kilo uyuşturucuyu narkotik köpeği buldu. Sözcü, March 17, 2025.", "title": "86 kilo uyuşturucuyu narkotik köpeği buldu", "url": "https://www.sozcu.com.tr/86-kilo-uyusturucuyu-narkotik-kopegi-buldu-p151719", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım" }, { "kind": "news report", "citation": "Habertürk / DHA (2025). Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu. Habertürk, March 17, 2025.", "title": "Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu", "url": "https://yerel-haberler.haberturk.com/sirnak-haberleri/durdurulan-aractaki-86-kilo-uyusturucuyu-hektor-buldu-112096987", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım olan ASENA yazılımı sayesinde" }, { "kind": "news report", "citation": "Diken / İHA (2025). Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı. Diken, March 18, 2025.", "title": "Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı", "url": "https://www.diken.com.tr/sirnakta-86-kilo-uyusturucuyla-yakalanan-uc-avukat-tutuklandi", "date": "2025-03-18", "written_by": "independent", "quote": "Analiz Sistemleri Narkotik Ağı" }, { "kind": "registry label", "citation": "OECD.AI (2025). AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested. OECD AI Incidents and Hazards Monitor, 2025-03-17.", "title": "AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested", "url": "https://oecd.ai/en/incidents/2025-03-17-8c32", "date": "2025-03-17", "written_by": "independent", "quote": "used the ASENA AI narcotics analysis system and detection dog Hector to intercept a vehicle, uncovering 86.6 kg of skunk cannabis" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0382-source-0001", "AIBD-0382-source-0002", "AIBD-0382-source-0003", "AIBD-0382-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Sözcü / DHA (2025). 86 kilo uyuşturucuyu narkotik köpeği buldu. Sözcü, March 17, 2025.", "title": "86 kilo uyuşturucuyu narkotik köpeği buldu", "url": "https://www.sozcu.com.tr/86-kilo-uyusturucuyu-narkotik-kopegi-buldu-p151719", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım" }, { "kind": "news report", "citation": "Habertürk / DHA (2025). Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu. Habertürk, March 17, 2025.", "title": "Durdurulan araçtaki 86 kilo uyuşturucuyu Hektor buldu", "url": "https://yerel-haberler.haberturk.com/sirnak-haberleri/durdurulan-aractaki-86-kilo-uyusturucuyu-hektor-buldu-112096987", "date": "2025-03-17", "written_by": "independent", "quote": "yapay zeka formatında bir yazılım olan ASENA yazılımı sayesinde" }, { "kind": "news report", "citation": "Diken / İHA (2025). Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı. Diken, March 18, 2025.", "title": "Şırnak'ta 86 kilo uyuşturucuyla yakalanan üç avukat tutuklandı", "url": "https://www.diken.com.tr/sirnakta-86-kilo-uyusturucuyla-yakalanan-uc-avukat-tutuklandi", "date": "2025-03-18", "written_by": "independent", "quote": "Analiz Sistemleri Narkotik Ağı" }, { "kind": "registry label", "citation": "OECD.AI (2025). AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested. OECD AI Incidents and Hazards Monitor, 2025-03-17.", "title": "AI-Driven Raid Seizes 86.6 kg of Drugs in Şırnak, Three Lawyers Arrested", "url": "https://oecd.ai/en/incidents/2025-03-17-8c32", "date": "2025-03-17", "written_by": "independent", "quote": "used the ASENA AI narcotics analysis system and detection dog Hector to intercept a vehicle, uncovering 86.6 kg of skunk cannabis" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Şırnak police stopped a car selected by the ASENA narcotics analysis software; the dog Hector found 86.6 kg of skunk in 119 packages in the trunk, and three lawyers were remanded (March 2025).", "account": "Şırnak Provincial Police stopped a vehicle at the Köy Hizmetleri (Village Services) police checkpoint after ASENA selected it for inspection. DHA calls ASENA police software “in artificial intelligence format”. The narcotics dog Hector then found 86 kilograms 600 grams of skunk in 119 packages hidden in the trunk. Three occupants, M.E., H.K. and Ş.A., were detained (Sözcü/DHA and Habertürk/DHA, 17 March 2025). Diken, citing İHA, gives ASENA's full name as Analiz Sistemleri Narkotik Ağı (Narcotics Analysis Systems Network). It says the three were lawyers registered with the Mardin Bar Association. They were taken to court after police processing and remanded in custody (Diken, 18 March 2025). None of the reports gives the day of the stop, what data ASENA analyzed or how it chose the car, or any official's name." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ASENA (Analiz Sistemleri Narkotik Ağı), Turkish police narcotics analysis software" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ASENA narcotics analysis software flags a car at the Köy Hizmetleri checkpoint in Şırnak; the dog Hector finds 86.6 kg of skunk in 119 packages and three lawyers are remanded", "headline": "ASENA flags a car at a Şırnak checkpoint; 86.6 kg of skunk found" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "dfb098d8d930258fe8fdd8c4942e4947cefe5cd57f667980f8d2a644a86efc2f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Şırnak Provincial Police stopped a vehicle at the Köy Hizmetleri (Village Services) police checkpoint after ASENA selected it for inspection. DHA calls ASENA police software “in artificial intelligence format”. The narcotics dog Hector then found 86 kilograms 600 grams of skunk in 119 packages hidden in the trunk. Three occupants, M.E., H.K. and Ş.A., were detained (Sözcü/DHA and Habertürk/DHA, 17 March 2025). Diken, citing İHA, gives ASENA's full name as Analiz Sistemleri Narkotik Ağı (Narcotics Analysis Systems Network). It says the three were lawyers registered with the Mardin Bar Association. They were taken to court after police processing and remanded in custody (Diken, 18 March 2025). None of the reports gives the day of the stop, what data ASENA analyzed or how it chose the car, or any official's name.", "replication": { "produced_by": [ "A police analysis program that ranks vehicles for interdiction", "A fixed checkpoint at which a flagged car can be stopped and searched", "A narcotics dog and officers prepared to search the trunk" ], "must_hold": [ "The flag must be followed by a lawful stop and a physical search. The software only selects", "Seized material and arrests must be carried through prosecution" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0125", "case_url": "records/AIBD-0125.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Police facial recognition systems exist to identify people against identity and wanted-person records. Matching an unidentified detainee to a fugitive convict is that function, not a side effect." }, { "label": "AI contribution", "value": "The İHA and DHA reports credit the facial recognition system with the identification. The man produced no ID, and the match against police records returned his name and outstanding sentence. DHA's version says he was taken in for both fingerprint and facial recognition checks, so fingerprints might have identified him regardless. What the system is attested to have contributed is the identification itself, not time saved. The reports name neither the system nor its developer and give no match score. No official is quoted by name." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0125", "title": "A facial recognition check at an Adana police stop identified a man who said he had no ID as Süleyman Abül, a robbery convict wanted to serve a sentence of 10 years, 1 month and 10 days", "summary": "Adana police stopped a man with no ID; facial recognition identified him as a robbery convict wanted to serve a 10-year sentence.", "date_observed": "2025-03-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Officers of the Seyhan District Police Directorate in Adana, Turkey, stopped a suspicious man during a street check in the Sucuzade neighborhood. He said he had no identity document and, by DHA's account, gave contradictory statements. So the district's Crime Prevention and Investigation Bureau took him to the station for fingerprint and facial recognition checks. The facial recognition system identified him as Süleyman Abül, 38, a fugitive convict wanted to serve 10 years, 1 month and 10 days for robbery. İHA and DHA published this in the early hours of 4 March 2025 without stating the day of the stop. The OECD Monitor dates it 3 March. DHA said he would go to prison once processing was done.", "category": "benefit created", "unexpected": false, "purpose": "Police facial recognition systems exist to identify people against identity and wanted-person records. Matching an unidentified detainee to a fugitive convict is that function, not a side effect.", "system": "Facial recognition system used by Turkish police at the Seyhan district station (not named in the reports)", "model_version": null, "developer": null, "deployer": "Seyhan District Police Directorate, Adana (Turkish National Police)", "location": "Adana, Turkey", "domain": "Policing / fugitive identification", "segment": "Street identity checks by district police", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-03-03-13e9" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The Seyhan unit established at the station that a man who said he had no ID and gave contradictory statements was a fugitive convict. He had a robbery sentence of 10 years, 1 month and 10 days outstanding, and could go to prison. A material but single-case gain for one police unit." }, { "group": "societal", "depth": 1, "reason": "One convicted robber who had evaded a long sentence was returned to custody. The reports give no evidence of any further harm prevented, so the public benefit is small and diffuse." } ], "ai_role": { "status": "assessed", "statement": "The İHA and DHA reports credit the facial recognition system with the identification. The man produced no ID, and the match against police records returned his name and outstanding sentence. DHA's version says he was taken in for both fingerprint and facial recognition checks, so fingerprints might have identified him regardless. What the system is attested to have contributed is the identification itself, not time saved. The reports name neither the system nor its developer and give no match score. No official is quoted by name." }, "evidence": [ { "kind": "news report", "citation": "Demirören Haber Ajansı via NTV (2025). Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi. NTV, 4 March 2025.", "title": "Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi", "url": "https://www.ntv.com.tr/turkiye/firari-hukumlu-yuz-tanima-sistemiyle-yakayi-ele-verdi,U5c9NcXxdUWolAOeyn13sA", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yapılan incelemede yüz tanıma sisteminde şüphelinin, yağma suçundan 10 yıl 1 ay 10 gün hapis cezası ile aranan firari hükümlü Süleyman Abül olduğu belirlendi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Habertürk (2025). Yüz tanıma sistemiyle yakayı eleverdi. Habertürk, 4 March 2025.", "title": "Yüz tanıma sistemiyle yakayı eleverdi", "url": "https://www.haberturk.com/yuz-tanima-sistemiyle-yakayi-eleverdi-3770669", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yüz tanıma sistemi kullanılarak şüphelinin yağma suçundan 10 yıl 1 ay 10 gün aranan Süleyman Abül (38) olduğu tespit edildi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Haberler.com (2025). Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi. Haberler.com, published 00:18 local time, 4 March 2025.", "title": "Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi", "url": "https://www.haberler.com/3-sayfa/yuz-tanima-sistemiyle-yakalanan-hukumlu-kimligim-yok-dedi-18423978-haberi", "date": "2025-03-04", "written_by": "independent", "quote": "Adana'da yağma suçundan 10 yıl hapis cezasıyla aranan hükümlü polislere \"Kimliğim yok\" dedi ancak yüz tanıma sistemi sayesinde yakayı ele verdi." } ], "replication": { "produced_by": [ "A street identity check in which the detainee could not or would not produce identification and gave contradictory answers", "A facial recognition system at the station linked to records that hold wanted fugitives' photographs and outstanding sentences" ], "must_hold": [ "The face match must be confirmed against identity records before anyone is handed to prison. The reports describe processing continuing after the match", "Fingerprint checks were available in parallel, so the system's contribution is the identification, not a capability police otherwise lacked" ], "echoes": [] }, "paired_incident": null, "headline": "Facial recognition in Adana identifies a wanted robbery convict", "metric": "a fugitive convict wanted to serve 10 years, 1 month and 10 days for robbery", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No report later than the 4 March 2025 agency stories was found; they say only that he would be handed to prison after processing.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "Turkey", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-03-13e9", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0125", "catalog_version": "1.0.0", "source_fingerprint": "9c28da9b1e2e3c230acc71037f4ac3f46c199651a3abdd954683fef9ebebcbf0", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0125-source-0001", "AIBD-0125-source-0002", "AIBD-0125-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Seyhan District Police Directorate, Adana (Turkish National Police)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The İHA and DHA reports credit the facial recognition system with the identification. The man produced no ID, and the match against police records returned his name and outstanding sentence. DHA's version says he was taken in for both fingerprint and facial recognition checks, so fingerprints might have identified him regardless. What the system is attested to have contributed is the identification itself, not time saved. The reports name neither the system nor its developer and give no match score. No official is quoted by name." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The İHA and DHA reports credit the facial recognition system with the identification. 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report later than the 4 March 2025 agency stories was found; they say only that he would be handed to prison after processing." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0125" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Officers of the Seyhan District Police Directorate in Adana, Turkey, stopped a suspicious man during a street check in the Sucuzade neighborhood. He said he had no identity document and, by DHA's account, gave contradictory statements. So the district's Crime Prevention and Investigation Bureau took him to the station for fingerprint and facial recognition checks. The facial recognition system identified him as Süleyman Abül, 38, a fugitive convict wanted to serve 10 years, 1 month and 10 days for robbery. İHA and DHA published this in the early hours of 4 March 2025 without stating the day of the stop. The OECD Monitor dates it 3 March. DHA said he would go to prison once processing was done.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The Seyhan unit established at the station that a man who said he had no ID and gave contradictory statements was a fugitive convict. He had a robbery sentence of 10 years, 1 month and 10 days outstanding, and could go to prison. A material but single-case gain for one police unit." }, { "group": "societal", "depth": 1, "reason": "One convicted robber who had evaded a long sentence was returned to custody. The reports give no evidence of any further harm prevented, so the public benefit is small and diffuse." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Police facial recognition systems exist to identify people against identity and wanted-person records. Matching an unidentified detainee to a fugitive convict is that function, not a side effect." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Adana, Turkey", "deployer_country": "Turkey" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Police facial recognition systems exist to identify people against identity and wanted-person records. Matching an unidentified detainee to a fugitive convict is that function, not a side effect." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-03-03", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-03-13e9", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A street identity check in which the detainee could not or would not produce identification and gave contradictory answers", "A facial recognition system at the station linked to records that hold wanted fugitives' photographs and outstanding sentences" ], "must_hold": [ "The face match must be confirmed against identity records before anyone is handed to prison. The reports describe processing continuing after the match", "Fingerprint checks were available in parallel, so the system's contribution is the identification, not a capability police otherwise lacked" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-03-03-13e9", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/General_Directorate_of_Security_(Turkey)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / fugitive identification", "segment": "Street identity checks by district police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Demirören Haber Ajansı via NTV (2025). Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi. NTV, 4 March 2025.", "title": "Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi", "url": "https://www.ntv.com.tr/turkiye/firari-hukumlu-yuz-tanima-sistemiyle-yakayi-ele-verdi,U5c9NcXxdUWolAOeyn13sA", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yapılan incelemede yüz tanıma sisteminde şüphelinin, yağma suçundan 10 yıl 1 ay 10 gün hapis cezası ile aranan firari hükümlü Süleyman Abül olduğu belirlendi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Habertürk (2025). Yüz tanıma sistemiyle yakayı eleverdi. Habertürk, 4 March 2025.", "title": "Yüz tanıma sistemiyle yakayı eleverdi", "url": "https://www.haberturk.com/yuz-tanima-sistemiyle-yakayi-eleverdi-3770669", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yüz tanıma sistemi kullanılarak şüphelinin yağma suçundan 10 yıl 1 ay 10 gün aranan Süleyman Abül (38) olduğu tespit edildi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Haberler.com (2025). Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi. Haberler.com, published 00:18 local time, 4 March 2025.", "title": "Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi", "url": "https://www.haberler.com/3-sayfa/yuz-tanima-sistemiyle-yakalanan-hukumlu-kimligim-yok-dedi-18423978-haberi", "date": "2025-03-04", "written_by": "independent", "quote": "Adana'da yağma suçundan 10 yıl hapis cezasıyla aranan hükümlü polislere \"Kimliğim yok\" dedi ancak yüz tanıma sistemi sayesinde yakayı ele verdi." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0125-source-0001", "AIBD-0125-source-0002", "AIBD-0125-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Demirören Haber Ajansı via NTV (2025). Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi. NTV, 4 March 2025.", "title": "Firari hükümlü, yüz tanıma sistemiyle yakayı ele verdi", "url": "https://www.ntv.com.tr/turkiye/firari-hukumlu-yuz-tanima-sistemiyle-yakayi-ele-verdi,U5c9NcXxdUWolAOeyn13sA", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yapılan incelemede yüz tanıma sisteminde şüphelinin, yağma suçundan 10 yıl 1 ay 10 gün hapis cezası ile aranan firari hükümlü Süleyman Abül olduğu belirlendi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Habertürk (2025). Yüz tanıma sistemiyle yakayı eleverdi. Habertürk, 4 March 2025.", "title": "Yüz tanıma sistemiyle yakayı eleverdi", "url": "https://www.haberturk.com/yuz-tanima-sistemiyle-yakayi-eleverdi-3770669", "date": "2025-03-04", "written_by": "independent", "quote": "Burada yüz tanıma sistemi kullanılarak şüphelinin yağma suçundan 10 yıl 1 ay 10 gün aranan Süleyman Abül (38) olduğu tespit edildi." }, { "kind": "news report", "citation": "İhlas Haber Ajansı via Haberler.com (2025). Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi. Haberler.com, published 00:18 local time, 4 March 2025.", "title": "Yüz Tanıma Sistemiyle Yakalanan Hükümlü: 'Kimliğim Yok' Dedi", "url": "https://www.haberler.com/3-sayfa/yuz-tanima-sistemiyle-yakalanan-hukumlu-kimligim-yok-dedi-18423978-haberi", "date": "2025-03-04", "written_by": "independent", "quote": "Adana'da yağma suçundan 10 yıl hapis cezasıyla aranan hükümlü polislere \"Kimliğim yok\" dedi ancak yüz tanıma sistemi sayesinde yakayı ele verdi." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Adana police stopped a man with no ID; facial recognition identified him as a robbery convict wanted to serve a 10-year sentence.", "account": "Officers of the Seyhan District Police Directorate in Adana, Turkey, stopped a suspicious man during a street check in the Sucuzade neighborhood. He said he had no identity document and, by DHA's account, gave contradictory statements. So the district's Crime Prevention and Investigation Bureau took him to the station for fingerprint and facial recognition checks. The facial recognition system identified him as Süleyman Abül, 38, a fugitive convict wanted to serve 10 years, 1 month and 10 days for robbery. İHA and DHA published this in the early hours of 4 March 2025 without stating the day of the stop. The OECD Monitor dates it 3 March. DHA said he would go to prison once processing was done." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Facial recognition system used by Turkish police at the Seyhan district station (not named in the reports)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "A facial recognition check at an Adana police stop identified a man who said he had no ID as Süleyman Abül, a robbery convict wanted to serve a sentence of 10 years, 1 month and 10 days", "headline": "Facial recognition in Adana identifies a wanted robbery convict" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "9c28da9b1e2e3c230acc71037f4ac3f46c199651a3abdd954683fef9ebebcbf0" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Officers of the Seyhan District Police Directorate in Adana, Turkey, stopped a suspicious man during a street check in the Sucuzade neighborhood. He said he had no identity document and, by DHA's account, gave contradictory statements. So the district's Crime Prevention and Investigation Bureau took him to the station for fingerprint and facial recognition checks. The facial recognition system identified him as Süleyman Abül, 38, a fugitive convict wanted to serve 10 years, 1 month and 10 days for robbery. İHA and DHA published this in the early hours of 4 March 2025 without stating the day of the stop. The OECD Monitor dates it 3 March. DHA said he would go to prison once processing was done.", "replication": { "produced_by": [ "A street identity check in which the detainee could not or would not produce identification and gave contradictory answers", "A facial recognition system at the station linked to records that hold wanted fugitives' photographs and outstanding sentences" ], "must_hold": [ "The face match must be confirmed against identity records before anyone is handed to prison. The reports describe processing continuing after the match", "Fingerprint checks were available in parallel, so the system's contribution is the identification, not a capability police otherwise lacked" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0350", "case_url": "records/AIBD-0350.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Real-time detection of suspicious vessel behavior and fishing activity to support maritime enforcement against illegal, unreported and unregulated fishing. The interception was the intended use." }, { "label": "AI contribution", "value": "Skylight's own account describes a two-stage AI contribution. A rule-based Speed Range Event flagged the vessel at under 6 knots inside an area of interest. 40 minutes later a Fishing Event from its machine-learning model, which detects longlining, trawling and squid jigging, reinforced the suspicion. PNA analysts then redirected a coastguard vessel. A retired PNA captain is quoted saying Skylight contributed directly to the decision. This is the maker's account, with the deployer's officers quoted inside it. The independent report (Agenda Malvinas, citing Pescare and official information) attributes the detection to the PNA's Sistema Guardacostas and does not name Skylight or AI. So the attribution to the AI output is not independently confirmed. The PNA release Skylight links to is not among the sources." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Argentina" ] }, "record": { "id": "AIBD-0350", "title": "Skylight speed-range and machine-learning fishing alerts may have led Argentina's Prefectura Naval to intercept the Vanuatu-flagged XINRUN 579 on 5 January 2025, and a fine may have been paid", "summary": "PNA intercepted XINRUN 579 fishing illegally in Argentina's EEZ on 5 January 2025; Skylight says its speed-range and ML fishing alerts prompted the response and a first electronic fine was paid.", "date_observed": "2025-01-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Sunday 5 January 2025 the Prefectura Naval Argentina (PNA) detected the Vanuatu-flagged trawler XinRun 579. It was in Argentina's exclusive economic zone off Caleta Olivia, Santa Cruz, sailing at under 6 knots. Local rules treat that as indicating trawling. Coastguard vessel GC-25 Azopardo intercepted it. The captain was formally notified of an infraction under Law 24.922, opening administrative proceedings (Agenda Malvinas, 8 January 2025, citing official information). Skylight, the free AI maritime-monitoring platform of the Allen Institute for AI, published its own account on 31 March 2025. A rule-based Speed Range Event first alerted the authorities. A Fishing Event alert from its machine-learning model came 40 minutes later. PNA analysts redirected a nearby coastguard vessel. It led to Argentina's first international electronic fine using AI-powered evidence, with the fine and operational costs paid. It quotes the EICEMAR coordinator and the PNA GIS division lead crediting Skylight. The independent news report attributes the detection to the PNA's Sistema Guardacostas and does not mention Skylight or AI. The PNA release Skylight links to is not found live or in the Internet Archive. An NVIDIA Developer blog (3 March 2025) also reports the interdiction and fine.", "category": "protective response", "unexpected": false, "purpose": "Real-time detection of suspicious vessel behavior and fishing activity to support maritime enforcement against illegal, unreported and unregulated fishing. The interception was the intended use.", "system": "Skylight maritime-monitoring platform (Speed Range Event rule and machine-learning Fishing Event detection)", "model_version": "Atlantes", "developer": "Allen Institute for AI (Ai2), Skylight", "deployer": "Prefectura Naval Argentina (Dirección de Tráfico Marítimo, Fluvial y Lacustre; EICEMAR)", "location": "Argentine exclusive economic zone off Caleta Olivia, Santa Cruz, Argentina", "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "National coastguard using a free non-profit AI platform", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "An interception and fine for illegal fishing protect a fishery; no ecological gain is measured and no enforcement type exists.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified." }, { "group": "organizational", "depth": 2, "reason": "The PNA intercepted the vessel within hours and, per Skylight, issued its first international electronic fine with the fine and costs paid. The independent report confirms the interception and administrative proceedings." }, { "group": "societal", "depth": 1, "reason": "One vessel among hundreds operating at the edge of Argentina's EEZ each season; the fishery-level effect is not quantified." } ], "ai_role": { "status": "assessed", "statement": "Skylight's own account describes a two-stage AI contribution. A rule-based Speed Range Event flagged the vessel at under 6 knots inside an area of interest. 40 minutes later a Fishing Event from its machine-learning model, which detects longlining, trawling and squid jigging, reinforced the suspicion. PNA analysts then redirected a coastguard vessel. A retired PNA captain is quoted saying Skylight contributed directly to the decision. This is the maker's account, with the deployer's officers quoted inside it. The independent report (Agenda Malvinas, citing Pescare and official information) attributes the detection to the PNA's Sistema Guardacostas and does not name Skylight or AI. So the attribution to the AI output is not independently confirmed. The PNA release Skylight links to is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Agenda Malvinas (2025). Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas. 8 January 2025 (source line: Pescare).", "title": "Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "la Prefectura Naval Argentina detectó este domingo 5 de enero, al buque pesquero XinRun 579, de bandera de Vanuatu, realizando pesca ilegal en la Zona Económica Exclusiva Argentina" }, { "kind": "news report", "citation": "Agenda Malvinas (2025). Same article, detection and interception sentences.", "title": "Prefectura detectó un buque asiático pescando ilegalmente (interception)", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "Tras la intercepción del Guardacostas GC-25 Azopardo, se notificó formalmente al capitán del XinRun 579 sobre la infracción" }, { "kind": "press release", "citation": "Skylight (2025). Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing. skylight.global, 31 March 2025 (maker's account).", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Just 40 minutes later, a Fishing Event alert provided additional intelligence, reinforcing suspicions that the vessel could be engaged in illegal fishing activity." }, { "kind": "press release", "citation": "Skylight (2025). Same article, description of the detection stages.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (machine-learning model)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "then by a machine learning model that detects several types of fishing behavior like long lining, trawling, and squid jigging" }, { "kind": "press release", "citation": "Skylight (2025). Same article, quotation attributed to Retired Captain Almada, Coordinator of EICEMAR, Prefectura Naval Argentina.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (PNA quotation)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Skylight contributed directly to the Coast Guard's decision-making by delivering immediate and reliable insights." }, { "kind": "press release", "citation": "Skylight (2025). Same article, outcome sentence.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (fine)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "The case led to Argentina’s first international electronic fine using AI-powered evidence." }, { "kind": "press release", "citation": "Wolfberg, E. (2025). AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale. NVIDIA Developer blog, 3 March 2025 (technology-partner blog, not the system's maker).", "title": "AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale", "url": "https://developer.nvidia.com/blog/ai-model-offers-conservationists-new-tools-to-protect-fisheries-wildlife-at-scale/", "date": "2025-03-03", "written_by": "independent", "quote": "Argentina's Coast Guard interdicted—and later fined—a vessel illegally fishing in its exclusive economic zone after Skylight alerted it to the vessel's suspicious behavior" }, { "kind": "press release", "citation": "Skylight (2026). Year in Review: Skylight Opens to the Public, Introduces Shippy, and Helps Partners Act at Sea (Impact Report June 2025-May 2026).", "title": "Skylight Impact Report 2025-2026", "url": "https://www.skylight.global/news/impact-report-2025-2026", "date": null, "written_by": "makers", "quote": "Skylight surfaces signals. The inspections, fines, and decisions belong to the agencies that made them." } ], "replication": { "produced_by": [ "A vessel transmitting AIS inside a monitored area of interest", "A speed-range rule tuned to the local legal definition of trawling speed", "A machine-learning fishing-behavior model producing a second alert within 40 minutes", "PNA analysts integrating Skylight with the Sistema Guardacostas and a coastguard vessel within reach" ], "must_hold": [ "AIS transmissions not switched off or spoofed", "A patrol asset close enough to intercept before the vessel leaves the EEZ", "A legal route to sanction (Law 24.922 and the electronic fine procedure)", "Analyst confirmation of each alert before dispatch" ], "echoes": [ { "date": null, "setting": "Gabon, ANPN marine parks: Skylight alert on GUO JI 838 entering a restricted MPA led to a patrol being sent; no outcome stated (Skylight's account, undated)", "model_version": null, "outcome": "unknown", "source": "https://www.skylight.global/news/stories-from-the-field-protecting-the-biggest-marine-national-park-system-in-africa" }, { "date": null, "setting": "Skylight impact report June 2025-May 2026: aggregated partner results of 21+ vessels interdicted and fines issued, US$915,000+ in fines, without case-level attribution (maker's figures)", "model_version": null, "outcome": "recurred", "source": "https://www.skylight.global/news/impact-report-2025-2026" } ] }, "paired_incident": null, "headline": "Skylight alerts may have led PNA to intercept XINRUN 579", "metric": null, "comparator": { "against": "PNA's capability before Skylight's real-time fishing detection", "before": null, "after": "a Speed Range Event alert, then a Fishing Event alert 40 minutes later", "basis": "assumed", "quote": "This kind of swift and decisive assistance wouldn't have been possible just a few months ago, before Skylight’s latest advancements in real-time fishing detection.", "url": "https://www.skylight.global/news/argentina-success-story" }, "later": { "status": "litigated", "date": "2025-03-31", "note": "The administrative proceeding ended with the vessel paying the fine and the authorities' operational costs, as Skylight reported on 31 March 2025 (the payment date itself is not given; NVIDIA's 3 March 2025 blog already said the vessel was later fined).", "quote": "The vessel paid both the fine and the operational costs incurred by authorities–underscoring how smarter, faster detection is helping enforcement teams respond quickly and build stronger, more actionable cases against those who exploit the ocean.", "url": "https://www.skylight.global/news/argentina-success-story" }, "value": null, "developer_country": "United States", "deployer_country": "Argentina", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.skylight.global/news/argentina-success-story; https://www.skylight.global/news/argentina-success-story; https://developer.nvidia.com/blog/ai-model-offers-conservationists-new-tools-to-protect-fisheries-wildlife-at-scale/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://allenai.org/about; https://en.wikipedia.org/wiki/Argentine_Naval_Prefecture", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, one independent press release and two press releases from the makers. 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It was in Argentina's exclusive economic zone off Caleta Olivia, Santa Cruz, sailing at under 6 knots. Local rules treat that as indicating trawling. Coastguard vessel GC-25 Azopardo intercepted it. The captain was formally notified of an infraction under Law 24.922, opening administrative proceedings (Agenda Malvinas, 8 January 2025, citing official information). Skylight, the free AI maritime-monitoring platform of the Allen Institute for AI, published its own account on 31 March 2025. A rule-based Speed Range Event first alerted the authorities. A Fishing Event alert from its machine-learning model came 40 minutes later. PNA analysts redirected a nearby coastguard vessel. It led to Argentina's first international electronic fine using AI-powered evidence, with the fine and operational costs paid. It quotes the EICEMAR coordinator and the PNA GIS division lead crediting Skylight. 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Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas. 8 January 2025 (source line: Pescare).", "title": "Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "la Prefectura Naval Argentina detectó este domingo 5 de enero, al buque pesquero XinRun 579, de bandera de Vanuatu, realizando pesca ilegal en la Zona Económica Exclusiva Argentina" }, { "kind": "news report", "citation": "Agenda Malvinas (2025). 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Same article, outcome sentence.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (fine)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "The case led to Argentina’s first international electronic fine using AI-powered evidence." }, { "kind": "press release", "citation": "Wolfberg, E. (2025). AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale. 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The inspections, fines, and decisions belong to the agencies that made them." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0350-source-0001", "AIBD-0350-source-0002", "AIBD-0350-source-0003", "AIBD-0350-source-0004", "AIBD-0350-source-0005", "AIBD-0350-source-0006", "AIBD-0350-source-0007", "AIBD-0350-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Agenda Malvinas (2025). Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas. 8 January 2025 (source line: Pescare).", "title": "Prefectura detectó un buque asiático pescando ilegalmente en aguas argentinas", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "la Prefectura Naval Argentina detectó este domingo 5 de enero, al buque pesquero XinRun 579, de bandera de Vanuatu, realizando pesca ilegal en la Zona Económica Exclusiva Argentina" }, { "kind": "news report", "citation": "Agenda Malvinas (2025). Same article, detection and interception sentences.", "title": "Prefectura detectó un buque asiático pescando ilegalmente (interception)", "url": "https://agendamalvinas.com.ar/noticia/prefectura-detecto-un-buque-asiatico-pescando-ilegalmente-en-aguas-argentinas", "date": "2025-01-08", "written_by": "independent", "quote": "Tras la intercepción del Guardacostas GC-25 Azopardo, se notificó formalmente al capitán del XinRun 579 sobre la infracción" }, { "kind": "press release", "citation": "Skylight (2025). Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing. skylight.global, 31 March 2025 (maker's account).", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Just 40 minutes later, a Fishing Event alert provided additional intelligence, reinforcing suspicions that the vessel could be engaged in illegal fishing activity." }, { "kind": "press release", "citation": "Skylight (2025). Same article, description of the detection stages.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (machine-learning model)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "then by a machine learning model that detects several types of fishing behavior like long lining, trawling, and squid jigging" }, { "kind": "press release", "citation": "Skylight (2025). Same article, quotation attributed to Retired Captain Almada, Coordinator of EICEMAR, Prefectura Naval Argentina.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (PNA quotation)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "Skylight contributed directly to the Coast Guard's decision-making by delivering immediate and reliable insights." }, { "kind": "press release", "citation": "Skylight (2025). Same article, outcome sentence.", "title": "Real-Time Intelligence Powers Argentina's Fight Against Illegal Fishing (fine)", "url": "https://www.skylight.global/news/argentina-success-story", "date": "2025-03-31", "written_by": "makers", "quote": "The case led to Argentina’s first international electronic fine using AI-powered evidence." }, { "kind": "press release", "citation": "Wolfberg, E. (2025). AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale. NVIDIA Developer blog, 3 March 2025 (technology-partner blog, not the system's maker).", "title": "AI Model Offers Conservationists New Tools to Protect Fisheries, Wildlife at Scale", "url": "https://developer.nvidia.com/blog/ai-model-offers-conservationists-new-tools-to-protect-fisheries-wildlife-at-scale/", "date": "2025-03-03", "written_by": "independent", "quote": "Argentina's Coast Guard interdicted—and later fined—a vessel illegally fishing in its exclusive economic zone after Skylight alerted it to the vessel's suspicious behavior" }, { "kind": "press release", "citation": "Skylight (2026). Year in Review: Skylight Opens to the Public, Introduces Shippy, and Helps Partners Act at Sea (Impact Report June 2025-May 2026).", "title": "Skylight Impact Report 2025-2026", "url": "https://www.skylight.global/news/impact-report-2025-2026", "written_by": "makers", "quote": "Skylight surfaces signals. The inspections, fines, and decisions belong to the agencies that made them." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "PNA intercepted XINRUN 579 fishing illegally in Argentina's EEZ on 5 January 2025; Skylight says its speed-range and ML fishing alerts prompted the response and a first electronic fine was paid.", "account": "On Sunday 5 January 2025 the Prefectura Naval Argentina (PNA) detected the Vanuatu-flagged trawler XinRun 579. It was in Argentina's exclusive economic zone off Caleta Olivia, Santa Cruz, sailing at under 6 knots. Local rules treat that as indicating trawling. Coastguard vessel GC-25 Azopardo intercepted it. The captain was formally notified of an infraction under Law 24.922, opening administrative proceedings (Agenda Malvinas, 8 January 2025, citing official information). Skylight, the free AI maritime-monitoring platform of the Allen Institute for AI, published its own account on 31 March 2025. A rule-based Speed Range Event first alerted the authorities. A Fishing Event alert from its machine-learning model came 40 minutes later. PNA analysts redirected a nearby coastguard vessel. It led to Argentina's first international electronic fine using AI-powered evidence, with the fine and operational costs paid. It quotes the EICEMAR coordinator and the PNA GIS division lead crediting Skylight. The independent news report attributes the detection to the PNA's Sistema Guardacostas and does not mention Skylight or AI. The PNA release Skylight links to is not found live or in the Internet Archive. An NVIDIA Developer blog (3 March 2025) also reports the interdiction and fine." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Atlantes" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Skylight maritime-monitoring platform (Speed Range Event rule and machine-learning Fishing Event detection)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Allen Institute for AI (Ai2), Skylight" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Skylight speed-range and machine-learning fishing alerts may have led Argentina's Prefectura Naval to intercept the Vanuatu-flagged XINRUN 579 on 5 January 2025, and a fine may have been paid", "headline": "Skylight alerts may have led PNA to intercept XINRUN 579" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "30e9f88a9087fef78c19cfc9c9322d9ab4b3a849e0f3dd14333df59dc2954154" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Sunday 5 January 2025 the Prefectura Naval Argentina (PNA) detected the Vanuatu-flagged trawler XinRun 579. It was in Argentina's exclusive economic zone off Caleta Olivia, Santa Cruz, sailing at under 6 knots. Local rules treat that as indicating trawling. Coastguard vessel GC-25 Azopardo intercepted it. The captain was formally notified of an infraction under Law 24.922, opening administrative proceedings (Agenda Malvinas, 8 January 2025, citing official information). Skylight, the free AI maritime-monitoring platform of the Allen Institute for AI, published its own account on 31 March 2025. A rule-based Speed Range Event first alerted the authorities. A Fishing Event alert from its machine-learning model came 40 minutes later. PNA analysts redirected a nearby coastguard vessel. It led to Argentina's first international electronic fine using AI-powered evidence, with the fine and operational costs paid. It quotes the EICEMAR coordinator and the PNA GIS division lead crediting Skylight. The independent news report attributes the detection to the PNA's Sistema Guardacostas and does not mention Skylight or AI. The PNA release Skylight links to is not found live or in the Internet Archive. An NVIDIA Developer blog (3 March 2025) also reports the interdiction and fine.", "replication": { "produced_by": [ "A vessel transmitting AIS inside a monitored area of interest", "A speed-range rule tuned to the local legal definition of trawling speed", "A machine-learning fishing-behavior model producing a second alert within 40 minutes", "PNA analysts integrating Skylight with the Sistema Guardacostas and a coastguard vessel within reach" ], "must_hold": [ "AIS transmissions not switched off or spoofed", "A patrol asset close enough to intercept before the vessel leaves the EEZ", "A legal route to sanction (Law 24.922 and the electronic fine procedure)", "Analyst confirmation of each alert before dispatch" ], "echoes": [ { "setting": "Gabon, ANPN marine parks: Skylight alert on GUO JI 838 entering a restricted MPA led to a patrol being sent; no outcome stated (Skylight's account, undated)", "outcome": "unknown", "source": "https://www.skylight.global/news/stories-from-the-field-protecting-the-biggest-marine-national-park-system-in-africa" }, { "setting": "Skylight impact report June 2025-May 2026: aggregated partner results of 21+ vessels interdicted and fines issued, US$915,000+ in fines, without case-level attribution (maker's figures)", "outcome": "recurred", "source": "https://www.skylight.global/news/impact-report-2025-2026" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0124", "case_url": "records/AIBD-0124.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "Spark was built to find bugs and vulnerabilities autonomously by selecting functions to fuzz and generating and running fuzz tests. Finding this flaw was exactly that function, exercised in the vendor's own beta test." }, { "label": "AI contribution", "value": "On the vendor's account, Spark selected the target, generated the fuzz test and ran it after a single “cifuzz spark” command. One manual step confirmed that the finding was real and exploitable under narrow conditions. wolfSSL's pull request and release notes confirm the bug, its location in pem_read_bio_key(), the timing and the reporter's first name. They mention neither AI nor Code Intelligence. The autonomous discovery therefore rests on the vendor's statements, repeated by QA Financial and SC Media, not on an independent test." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "Germany" ] }, "record": { "id": "AIBD-0124", "title": "Code Intelligence's Spark agent found a heap use-after-free in wolfSSL's pem_read_bio_key() during its beta test; wolfSSL merged a fix within days and shipped it in 5.7.6", "summary": "Spark, an AI fuzz-test agent, found a heap use-after-free in wolfSSL in late Oct 2024; fixed in PR 8139 and shipped in 5.7.6 on 31 December 2024.", "date_observed": "2024-11-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Spark is the AI test agent in Code Intelligence GmbH's CI Fuzz product. Its final beta test in late October 2024 ran it on wolfSSL, an open-source cryptography library for embedded and IoT devices. By the vendor's account, after one command the agent chose the target, generated and ran a fuzz test and exposed a heap use-after-free in pem_read_bio_key(). There a BIO write-back could reallocate a buffer still referenced by a pointer. The vendor reported it to wolfSSL, which merged a fix in PR 8139 on 1 November 2024. It shipped in release 5.7.6 on 31 December 2024, crediting “Peter” in the release notes. The vendor's blog post on the find is by engineer Peter Samarin.", "category": "benefit created", "unexpected": false, "purpose": "Spark was built to find bugs and vulnerabilities autonomously by selecting functions to fuzz and generating and running fuzz tests. Finding this flaw was exactly that function, exercised in the vendor's own beta test.", "system": "Spark, AI test agent in Code Intelligence's CI Fuzz (invoked as 'cifuzz spark')", "model_version": null, "developer": "Code Intelligence GmbH", "deployer": "Code Intelligence GmbH (beta test run by the vendor)", "location": "Bonn, Germany", "domain": "Software security / open-source cryptography library testing", "segment": "Open-source TLS and cryptography library for embedded and IoT systems", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-01-09-764d" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "wolfSSL received a reproducible report of a heap use-after-free in a private-key parsing path. It merged the fix within days (PR 8139, 1 November 2024) and shipped it in 5.7.6 on 31 December 2024. The maintainers listed it among bug fixes rather than in the release's Vulnerabilities section. No CVE for it appears on wolfSSL's vulnerabilities page. The vendor itself says the exploit conditions “don't seem easy to meet”, so the benefit is material but bounded." } ], "ai_role": { "status": "assessed", "statement": "On the vendor's account, Spark selected the target, generated the fuzz test and ran it after a single “cifuzz spark” command. One manual step confirmed that the finding was real and exploitable under narrow conditions. wolfSSL's pull request and release notes confirm the bug, its location in pem_read_bio_key(), the timing and the reporter's first name. They mention neither AI nor Code Intelligence. The autonomous discovery therefore rests on the vendor's statements, repeated by QA Financial and SC Media, not on an independent test." }, "evidence": [ { "kind": "other", "citation": "wolfSSL (2024). wolfSSL Release 5.7.6 (Dec 31, 2024), release notes, Bug Fixes. GitHub, wolfSSL/wolfssl, tag v5.7.6-stable, published 31 December 2024.", "title": "wolfSSL Release 5.7.6 (Dec 31, 2024)", "url": "https://github.com/wolfSSL/wolfssl/releases/tag/v5.7.6-stable", "date": "2024-12-31", "written_by": "beneficiary", "quote": "Fix for potential heap use after free with wolfSSL_PEM_read_bio_PrivateKey. Thanks to Peter for the issue reported. (PR 8139)" }, { "kind": "other", "citation": "douzzer (2024). 20241031-fixes. wolfSSL/wolfssl pull request 8139, GitHub, opened and merged 1 November 2024; commit b41ce042 changes src/pk.c.", "title": "20241031-fixes (wolfSSL pull request 8139)", "url": "https://github.com/wolfSSL/wolfssl/pull/8139", "date": "2024-11-01", "written_by": "beneficiary", "quote": "src/pk.c: in pem_read_bio_key(), fix invalid read (ZD#18875)." }, { "kind": "press release", "citation": "Samarin, P. (2025). How AI-generated fuzz test uncovered a vulnerability in wolfSSL. Code Intelligence blog, 3 January 2025; Wayback Machine capture of 9 January 2025 (the live domain did not resolve on 20 October 2026).", "title": "How AI-generated fuzz test uncovered a vulnerability in wolfSSL", "url": "https://web.archive.org/web/20250109141038/https://www.code-intelligence.com/blog/ai-generated-fuzz-test-wolfssl-vulnerability", "date": "2025-01-03", "written_by": "makers", "quote": "The vulnerability was identified in wolfSSL, an open-source cryptography library widely used in developing embedded devices and IoT systems, during the final week of October 2024." }, { "kind": "press release", "citation": "Code Intelligence (2025). Code Intelligence Launches Spark, Its AI Test Agent That Autonomously Uncovers Vulnerabilities Without Human Interaction. PR Newswire, Bonn, 9 January 2025.", "title": "Code Intelligence Launches Spark, Its AI Test Agent That Autonomously Uncovers Vulnerabilities Without Human Interaction", "url": "https://www.prnewswire.com/news-releases/code-intelligence-launches-spark-its-ai-test-agent-that-autonomously-uncovers-vulnerabilities-without-human-interaction-302345548.html", "date": "2025-01-09", "written_by": "makers", "quote": "The only human involvement was launching a single command to run the AI Test Agent; analyzing the code, generating a relevant test case, and running it was done autonomously." }, { "kind": "news report", "citation": "Willems, M. (2025). German startup to launch world's first AI test agent. QA Financial, 13 January 2025.", "title": "German startup to launch world's first AI test agent", "url": "https://qa-financial.com/german-startup-to-launch-worlds-first-ai-test-agent/", "date": "2025-01-13", "written_by": "independent", "quote": "Brueggemann claimed his firm shared the bug with WolfSSL’s team, which went on to issue a warning and update in December of last year." }, { "kind": "news report", "citation": "French, L. (2025). How AI can revolutionize vulnerability research. SC Media, 3 February 2025.", "title": "How AI can revolutionize vulnerability research", "url": "https://www.scworld.com/feature/how-ai-can-revolutionize-vulnerability-research", "date": "2025-02-03", "written_by": "independent", "quote": "Code Intelligence’s Spark autonomously discovering a heap-based user-after-free vulnerability in popular open-source wolfSSL library during its beta testing in October 2024." } ], "replication": { "produced_by": [ "wolfSSL's source tree (before 5.7.6) set up as a CI Fuzz project by the vendor", "A single “cifuzz spark” command, after which the agent chose pem_read_bio_key() as a target and generated and ran a fuzz test", "A sanitizer-detectable heap use-after-free in the BIO write-back path of pem_read_bio_key() that a generated input could trigger", "A maintainer team that tracked the report as ZD#18875 and merged a fix within three days of the discovery" ], "must_hold": [ "A human must confirm the finding. The vendor says the one manual step was assessing that the bug exists and is exploitable under specific conditions", "The beneficiary must be an identifiable project with a public fix record. Without wolfSSL's PR and release notes the event would rest on vendor claims alone", "The AI's share of the work is described only by the vendor. wolfSSL's records name the reporter, not the tool, so the autonomy claim cannot be replicated without the vendor's product" ], "echoes": [] }, "paired_incident": null, "headline": "Code Intelligence's Spark finds a heap use-after-free in wolfSSL", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2024-12-31", "note": "wolfSSL shipped the fix in release 5.7.6 on 31 December 2024, crediting the reporter in its release notes, and Code Intelligence launched Spark publicly in January 2025.", "quote": "Fix for potential heap use after free with wolfSSL_PEM_read_bio_PrivateKey. Thanks to Peter for the issue reported. (PR 8139)", "url": "https://github.com/wolfSSL/wolfssl/releases/tag/v5.7.6-stable" }, "value": null, "developer_country": "Germany", "deployer_country": "Germany", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-01-09-764d", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://github.com/wolfSSL/wolfssl/releases/tag/v5.7.6-stable", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.northdata.de/Code+Intelligence+GmbH,+Bonn; https://www.northdata.de/Code+Intelligence+GmbH,+Bonn", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, two press releases from the makers and two other sources from the beneficiary. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0124", "catalog_version": "1.0.0", "source_fingerprint": "7ce94ae129baf748ac97ed8b650550a439de68537780b073ab8c984a81c60a8a", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0124-source-0001", "AIBD-0124-source-0002", "AIBD-0124-source-0003", "AIBD-0124-source-0004", "AIBD-0124-source-0005", "AIBD-0124-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Code Intelligence GmbH", "deployer": "Code Intelligence GmbH (beta test run by the vendor)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "On the vendor's account, Spark selected the target, generated the fuzz test and ran it after a single “cifuzz spark” command. One manual step confirmed that the finding was real and exploitable under narrow conditions. wolfSSL's pull request and release notes confirm the bug, its location in pem_read_bio_key(), the timing and the reporter's first name. They mention neither AI nor Code Intelligence. The autonomous discovery therefore rests on the vendor's statements, repeated by QA Financial and SC Media, not on an independent test." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "On the vendor's account, Spark selected the target, generated the fuzz test and ran it after a single “cifuzz spark” command. 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"coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-01-09-764d" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2024-12-31", "note": "wolfSSL shipped the fix in release 5.7.6 on 31 December 2024, crediting the reporter in its release notes, and Code Intelligence launched Spark publicly in January 2025.", "quote": "Fix for potential heap use after free with wolfSSL_PEM_read_bio_PrivateKey. Thanks to Peter for the issue reported. 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Its final beta test in late October 2024 ran it on wolfSSL, an open-source cryptography library for embedded and IoT devices. By the vendor's account, after one command the agent chose the target, generated and ran a fuzz test and exposed a heap use-after-free in pem_read_bio_key(). There a BIO write-back could reallocate a buffer still referenced by a pointer. The vendor reported it to wolfSSL, which merged a fix in PR 8139 on 1 November 2024. It shipped in release 5.7.6 on 31 December 2024, crediting “Peter” in the release notes. The vendor's blog post on the find is by engineer Peter Samarin." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Spark, AI test agent in Code Intelligence's CI Fuzz (invoked as 'cifuzz spark')" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Code Intelligence GmbH" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Code Intelligence's Spark agent found a heap use-after-free in wolfSSL's pem_read_bio_key() during its beta test; wolfSSL merged a fix within days and shipped it in 5.7.6", "headline": "Code Intelligence's Spark finds a heap use-after-free in wolfSSL" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7ce94ae129baf748ac97ed8b650550a439de68537780b073ab8c984a81c60a8a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Spark is the AI test agent in Code Intelligence GmbH's CI Fuzz product. Its final beta test in late October 2024 ran it on wolfSSL, an open-source cryptography library for embedded and IoT devices. By the vendor's account, after one command the agent chose the target, generated and ran a fuzz test and exposed a heap use-after-free in pem_read_bio_key(). There a BIO write-back could reallocate a buffer still referenced by a pointer. The vendor reported it to wolfSSL, which merged a fix in PR 8139 on 1 November 2024. It shipped in release 5.7.6 on 31 December 2024, crediting “Peter” in the release notes. The vendor's blog post on the find is by engineer Peter Samarin.", "replication": { "produced_by": [ "wolfSSL's source tree (before 5.7.6) set up as a CI Fuzz project by the vendor", "A single “cifuzz spark” command, after which the agent chose pem_read_bio_key() as a target and generated and ran a fuzz test", "A sanitizer-detectable heap use-after-free in the BIO write-back path of pem_read_bio_key() that a generated input could trigger", "A maintainer team that tracked the report as ZD#18875 and merged a fix within three days of the discovery" ], "must_hold": [ "A human must confirm the finding. The vendor says the one manual step was assessing that the bug exists and is exploitable under specific conditions", "The beneficiary must be an identifiable project with a public fix record. Without wolfSSL's PR and release notes the event would rest on vendor claims alone", "The AI's share of the work is described only by the vendor. wolfSSL's records name the reporter, not the tool, so the autonomy claim cannot be replicated without the vendor's product" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0380", "case_url": "records/AIBD-0380.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Models built by the Kunshan police to detect crime patterns and trace funds for investigators. The bust and the freeze were that purpose." }, { "label": "AI contribution", "value": "Both accounts are the police's. They credit the models with flagging the outdoor casino from behavioral features in bulk data and mapping the ring's structure “with one click”. They also traced card-tier fund flows in parallel, so a freeze came within 10 minutes. Officers made the arrests and freeze requests. Deputy criminal-investigation chief He Yongliang describes the gain as 24-hour, parallel rather than serial casework. Neither source separates the models' share from ordinary investigation." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "China" ] }, "record": { "id": "AIBD-0380", "title": "Kunshan police “AI police” models flag a mobile gambling ring at Zhouzhuang, 20-plus arrested on 10 September 2024, and trace a telecom fraud's card transfers to freeze 500,000 yuan within 10 minutes", "summary": "Kunshan police say their “AI police” models led a 10 September 2024 bust of a mobile gambling ring (20-plus arrests) and froze 500,000 yuan of a 980,000-yuan telecom fraud within 10 minutes.", "date_observed": "2024-09-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 10 September 2024, the Kunshan Public Security Bureau in Jiangsu broke up a gang running a mobile open-air casino in Zhouzhuang town. It arrested over 20 suspects. Modern Express reported that the “AI police” had found the ring (NetEase reprint, 6 October 2024). These are models that detect outdoor gambling from behavioral patterns in bulk data. In a telecom fraud handled by the Lujia police station, a victim lost 980,000 yuan. The models traced three tiers of card transfers and froze 500,000 yuan within 10 minutes. They identified 87 suspect accounts within 8 hours. Nine suspects were arrested on the third day with 200,000 yuan recovered (Suzhou Daily via suzhou.gov.cn, 7 October 2024). The models were built by the “Kunpeng Squad”, 35 officers assembled in May 2024. They report over 220 models and, over six months, 609 fraud cases solved and 32.47 million yuan recovered (both sources). All figures are the police's own.", "category": "benefit created", "unexpected": false, "purpose": "Models built by the Kunshan police to detect crime patterns and trace funds for investigators. The bust and the freeze were that purpose.", "system": "Kunshan Public Security Bureau 'AI police' (AI警察): more than 220 'main-attack, main-defense' intelligent models built by the Kunpeng Squad", "model_version": null, "developer": "Kunshan Public Security Bureau, Kunpeng Squad (鲲鹏战队), with Suzhou Public Security Bureau support", "deployer": "Kunshan Public Security Bureau", "location": "Kunshan, China", "domain": "Law enforcement / criminal investigation", "segment": "Gambling rings and telecom fraud", "benefit_type": "Reduced Risk", "taxonomy_note": "The quantified outcome is a fraud loss partly averted (500,000 yuan frozen), which is Reduced Risk; the dated gambling bust is a crime stopped, Trustible's Reduced Violence, but the offense was nonviolent.", "registry_ids": { "oecd_aim": "2024-10-03-edf0" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "The fraud victim who lost 980,000 yuan had 500,000 yuan frozen within 10 minutes and 200,000 yuan recovered after the arrests, per the police. Whether the frozen sum was returned is not reported." }, { "group": "organizational", "depth": 2, "reason": "The Kunshan bureau reports a 20-plus-suspect bust, nine fraud arrests in three days and 609 fraud cases solved in six months, recovery rates up 62%. A measurable gain for the unit, on its own figures." }, { "group": "societal", "depth": null, "reason": "The six-month aggregate of 32.47 million yuan recovered is the deployer's own claim with no method or independent check, so no population effect is established." } ], "ai_role": { "status": "assessed", "statement": "Both accounts are the police's. They credit the models with flagging the outdoor casino from behavioral features in bulk data and mapping the ring's structure “with one click”. They also traced card-tier fund flows in parallel, so a freeze came within 10 minutes. Officers made the arrests and freeze requests. Deputy criminal-investigation chief He Yongliang describes the gain as 24-hour, parallel rather than serial casework. Neither source separates the models' share from ordinary investigation." }, "evidence": [ { "kind": "news report", "citation": "Ji, Y. (2024). 江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”. Modern Express (现代快报) via NetEase, October 6, 2024.", "title": "江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”", "url": "https://m.163.com/dy/article/JDQORBU7053469KC.html", "date": "2024-10-06", "written_by": "independent", "quote": "仅用10分钟，'AI警察'就追踪到三级卡资金流向，并成功止付50万元" }, { "kind": "news report", "citation": "Suzhou Daily (2024). 昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力. Suzhou Municipal People's Government website, October 7, 2024.", "title": "昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力", "url": "https://www.suzhou.gov.cn/2024ndzt/mtjjxzc/202410/41e0e0bc1a8843ebb55e2a0d54f44bf5.shtml", "date": "2024-10-07", "written_by": "deployer", "quote": "AI警察的应用为改进治理提供了一条新的路径" }, { "kind": "registry label", "citation": "OECD.AI (2024). AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention. OECD AI Incidents and Hazards Monitor, 2024-10-03.", "title": "AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention", "url": "https://oecd.ai/en/incidents/2024-10-03-edf0", "date": "2024-10-03", "written_by": "independent", "quote": "deployed AI-driven policing systems ('AI police') to identify and dismantle a mobile gambling ring, arresting over 20 suspects" } ], "replication": { "produced_by": [ "Bulk police data on gambling and payment behavior that models can score for anomalies", "A dedicated squad of officers who build and run models inside the bureau", "Bank cooperation that lets a traced account be frozen within minutes" ], "must_hold": [ "Officers verify each model flag and make the arrests and freeze requests themselves", "Figures are the deployer's own; an outside audit would be needed to confirm them" ], "echoes": [] }, "paired_incident": null, "headline": "Kunshan police AI flags gambling ring and freezes fraud transfers", "metric": "traced three tiers of card transfers and froze 500,000 yuan within 10 minutes", "comparator": null, "later": null, "value": null, "developer_country": "China", "deployer_country": "China", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-10-03-edf0", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Kunshan; https://en.wikipedia.org/wiki/Kunshan", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, a registry entry and the deployer's news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0380", "catalog_version": "1.0.0", "source_fingerprint": "8c7fdbc00abd5b816b15e4d1333369ff72f148a42c84cfe1d89d009021d6bde6", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0380-source-0001", "AIBD-0380-source-0002", "AIBD-0380-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Kunshan Public Security Bureau, Kunpeng Squad (鲲鹏战队), with Suzhou Public Security Bureau support", "deployer": "Kunshan Public Security Bureau" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Both accounts are the police's. They credit the models with flagging the outdoor casino from behavioral features in bulk data and mapping the ring's structure “with one click”. They also traced card-tier fund flows in parallel, so a freeze came within 10 minutes. Officers made the arrests and freeze requests. Deputy criminal-investigation chief He Yongliang describes the gain as 24-hour, parallel rather than serial casework. Neither source separates the models' share from ordinary investigation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Both accounts are the police's. They credit the models with flagging the outdoor casino from behavioral features in bulk data and mapping the ring's structure “with one click”. They also traced card-tier fund flows in parallel, so a freeze came within 10 minutes. Officers made the arrests and freeze requests. Deputy criminal-investigation chief He Yongliang describes the gain as 24-hour, parallel rather than serial casework. Neither source separates the models' share from ordinary investigation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "The fraud victim who lost 980,000 yuan had 500,000 yuan frozen within 10 minutes and 200,000 yuan recovered after the arrests, per the police. Whether the frozen sum was returned is not reported." }, { "group": "organizational", "depth": 2, "reason": "The Kunshan bureau reports a 20-plus-suspect bust, nine fraud arrests in three days and 609 fraud cases solved in six months, recovery rates up 62%. A measurable gain for the unit, on its own figures." }, { "group": "societal", "reason": "The six-month aggregate of 32.47 million yuan recovered is the deployer's own claim with no method or independent check, so no population effect is established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report, a registry entry and the deployer's news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "The fraud victim who lost 980,000 yuan had 500,000 yuan frozen within 10 minutes and 200,000 yuan recovered after the arrests, per the police. Whether the frozen sum was returned is not reported." }, { "group": "organizational", "depth": 2, "reason": "The Kunshan bureau reports a 20-plus-suspect bust, nine fraud arrests in three days and 609 fraud cases solved in six months, recovery rates up 62%. A measurable gain for the unit, on its own figures." }, { "group": "societal", "reason": "The six-month aggregate of 32.47 million yuan recovered is the deployer's own claim with no method or independent check, so no population effect is established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "The fraud victim who lost 980,000 yuan had 500,000 yuan frozen within 10 minutes and 200,000 yuan recovered after the arrests, per the police. Whether the frozen sum was returned is not reported." }, { "group": "organizational", "depth": 2, "reason": "The Kunshan bureau reports a 20-plus-suspect bust, nine fraud arrests in three days and 609 fraud cases solved in six months, recovery rates up 62%. A measurable gain for the unit, on its own figures." }, { "group": "societal", "reason": "The six-month aggregate of 32.47 million yuan recovered is the deployer's own claim with no method or independent check, so no population effect is established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "traced three tiers of card transfers and froze 500,000 yuan within 10 minutes", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2024-10-03-edf0" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0380" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "The quantified outcome is a fraud loss partly averted (500,000 yuan frozen), which is Reduced Risk; the dated gambling bust is a crime stopped, Trustible's Reduced Violence, but the offense was nonviolent." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 10 September 2024, the Kunshan Public Security Bureau in Jiangsu broke up a gang running a mobile open-air casino in Zhouzhuang town. It arrested over 20 suspects. Modern Express reported that the “AI police” had found the ring (NetEase reprint, 6 October 2024). These are models that detect outdoor gambling from behavioral patterns in bulk data. In a telecom fraud handled by the Lujia police station, a victim lost 980,000 yuan. The models traced three tiers of card transfers and froze 500,000 yuan within 10 minutes. They identified 87 suspect accounts within 8 hours. Nine suspects were arrested on the third day with 200,000 yuan recovered (Suzhou Daily via suzhou.gov.cn, 7 October 2024). The models were built by the “Kunpeng Squad”, 35 officers assembled in May 2024. They report over 220 models and, over six months, 609 fraud cases solved and 32.47 million yuan recovered (both sources). All figures are the police's own.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "The fraud victim who lost 980,000 yuan had 500,000 yuan frozen within 10 minutes and 200,000 yuan recovered after the arrests, per the police. Whether the frozen sum was returned is not reported." }, { "group": "organizational", "depth": 2, "reason": "The Kunshan bureau reports a 20-plus-suspect bust, nine fraud arrests in three days and 609 fraud cases solved in six months, recovery rates up 62%. A measurable gain for the unit, on its own figures." }, { "group": "societal", "reason": "The six-month aggregate of 32.47 million yuan recovered is the deployer's own claim with no method or independent check, so no population effect is established." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Models built by the Kunshan police to detect crime patterns and trace funds for investigators. The bust and the freeze were that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kunshan, China", "developer_country": "China", "deployer_country": "China" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Models built by the Kunshan police to detect crime patterns and trace funds for investigators. The bust and the freeze were that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-09-10", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-10-03-edf0", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Kunshan; https://en.wikipedia.org/wiki/Kunshan", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Bulk police data on gambling and payment behavior that models can score for anomalies", "A dedicated squad of officers who build and run models inside the bureau", "Bank cooperation that lets a traced account be frozen within minutes" ], "must_hold": [ "Officers verify each model flag and make the arrests and freeze requests themselves", "Figures are the deployer's own; an outside audit would be needed to confirm them" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-10-03-edf0", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Kunshan; https://en.wikipedia.org/wiki/Kunshan", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Law enforcement / criminal investigation", "segment": "Gambling rings and telecom fraud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Ji, Y. (2024). 江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”. Modern Express (现代快报) via NetEase, October 6, 2024.", "title": "江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”", "url": "https://m.163.com/dy/article/JDQORBU7053469KC.html", "date": "2024-10-06", "written_by": "independent", "quote": "仅用10分钟，'AI警察'就追踪到三级卡资金流向，并成功止付50万元" }, { "kind": "news report", "citation": "Suzhou Daily (2024). 昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力. Suzhou Municipal People's Government website, October 7, 2024.", "title": "昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力", "url": "https://www.suzhou.gov.cn/2024ndzt/mtjjxzc/202410/41e0e0bc1a8843ebb55e2a0d54f44bf5.shtml", "date": "2024-10-07", "written_by": "deployer", "quote": "AI警察的应用为改进治理提供了一条新的路径" }, { "kind": "registry label", "citation": "OECD.AI (2024). AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention. OECD AI Incidents and Hazards Monitor, 2024-10-03.", "title": "AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention", "url": "https://oecd.ai/en/incidents/2024-10-03-edf0", "date": "2024-10-03", "written_by": "independent", "quote": "deployed AI-driven policing systems ('AI police') to identify and dismantle a mobile gambling ring, arresting over 20 suspects" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0380-source-0001", "AIBD-0380-source-0002", "AIBD-0380-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Ji, Y. (2024). 江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”. Modern Express (现代快报) via NetEase, October 6, 2024.", "title": "江苏昆山公安：“AI警察”上岗 “算力”引爆“战力”", "url": "https://m.163.com/dy/article/JDQORBU7053469KC.html", "date": "2024-10-06", "written_by": "independent", "quote": "仅用10分钟，'AI警察'就追踪到三级卡资金流向，并成功止付50万元" }, { "kind": "news report", "citation": "Suzhou Daily (2024). 昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力. Suzhou Municipal People's Government website, October 7, 2024.", "title": "昆山警方释放出“专业+机制+大数据”新型警务运行模式强大新质战力", "url": "https://www.suzhou.gov.cn/2024ndzt/mtjjxzc/202410/41e0e0bc1a8843ebb55e2a0d54f44bf5.shtml", "date": "2024-10-07", "written_by": "deployer", "quote": "AI警察的应用为改进治理提供了一条新的路径" }, { "kind": "registry label", "citation": "OECD.AI (2024). AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention. OECD AI Incidents and Hazards Monitor, 2024-10-03.", "title": "AI Police Deployed in Kunshan, China, Lead to Major Crime Busts and Fraud Prevention", "url": "https://oecd.ai/en/incidents/2024-10-03-edf0", "date": "2024-10-03", "written_by": "independent", "quote": "deployed AI-driven policing systems ('AI police') to identify and dismantle a mobile gambling ring, arresting over 20 suspects" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kunshan police say their “AI police” models led a 10 September 2024 bust of a mobile gambling ring (20-plus arrests) and froze 500,000 yuan of a 980,000-yuan telecom fraud within 10 minutes.", "account": "On 10 September 2024, the Kunshan Public Security Bureau in Jiangsu broke up a gang running a mobile open-air casino in Zhouzhuang town. It arrested over 20 suspects. Modern Express reported that the “AI police” had found the ring (NetEase reprint, 6 October 2024). These are models that detect outdoor gambling from behavioral patterns in bulk data. In a telecom fraud handled by the Lujia police station, a victim lost 980,000 yuan. The models traced three tiers of card transfers and froze 500,000 yuan within 10 minutes. They identified 87 suspect accounts within 8 hours. Nine suspects were arrested on the third day with 200,000 yuan recovered (Suzhou Daily via suzhou.gov.cn, 7 October 2024). The models were built by the “Kunpeng Squad”, 35 officers assembled in May 2024. They report over 220 models and, over six months, 609 fraud cases solved and 32.47 million yuan recovered (both sources). All figures are the police's own." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Kunshan Public Security Bureau 'AI police' (AI警察): more than 220 'main-attack, main-defense' intelligent models built by the Kunpeng Squad" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Kunshan Public Security Bureau, Kunpeng Squad (鲲鹏战队), with Suzhou Public Security Bureau support" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Kunshan police “AI police” models flag a mobile gambling ring at Zhouzhuang, 20-plus arrested on 10 September 2024, and trace a telecom fraud's card transfers to freeze 500,000 yuan within 10 minutes", "headline": "Kunshan police AI flags gambling ring and freezes fraud transfers" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "8c7fdbc00abd5b816b15e4d1333369ff72f148a42c84cfe1d89d009021d6bde6" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 10 September 2024, the Kunshan Public Security Bureau in Jiangsu broke up a gang running a mobile open-air casino in Zhouzhuang town. It arrested over 20 suspects. Modern Express reported that the “AI police” had found the ring (NetEase reprint, 6 October 2024). These are models that detect outdoor gambling from behavioral patterns in bulk data. In a telecom fraud handled by the Lujia police station, a victim lost 980,000 yuan. The models traced three tiers of card transfers and froze 500,000 yuan within 10 minutes. They identified 87 suspect accounts within 8 hours. Nine suspects were arrested on the third day with 200,000 yuan recovered (Suzhou Daily via suzhou.gov.cn, 7 October 2024). The models were built by the “Kunpeng Squad”, 35 officers assembled in May 2024. They report over 220 models and, over six months, 609 fraud cases solved and 32.47 million yuan recovered (both sources). All figures are the police's own.", "replication": { "produced_by": [ "Bulk police data on gambling and payment behavior that models can score for anomalies", "A dedicated squad of officers who build and run models inside the bureau", "Bank cooperation that lets a traced account be frozen within minutes" ], "must_hold": [ "Officers verify each model flag and make the arrests and freeze requests themselves", "Figures are the deployer's own; an outside audit would be needed to confirm them" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0095", "case_url": "records/AIBD-0095.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding." }, { "label": "AI contribution", "value": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Nigeria" ] }, "record": { "id": "AIBD-0095", "title": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "summary": "Google Flood Hub forecast triggers paid $105 to 4,600 people in Ibaji, Kogi State, mostly before floods peaked on 21 October 2024", "date_observed": "2024-08-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "category": "protective response", "unexpected": false, "purpose": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding.", "system": "Google Flood Hub AI river-flood forecasts accessed via API, with JBA Global Resilience hydrology analysis and a 20%-of-community-area trigger rule", "model_version": null, "developer": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience", "deployer": "GiveDirectly", "location": "Ibaji, Kogi State, Nigeria", "domain": "Humanitarian anticipatory action and cash transfers", "segment": "NGO cash assistance", "benefit_type": "Reduced Risk", "taxonomy_note": "Cash paid before a forecast flood is read as reducing potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ], "ai_role": { "status": "assessed", "statement": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." }, "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ], "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "model_version": null, "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "date": null, "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "model_version": null, "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash." }, "headline": "Google Flood Hub triggers pay $105 to 4,600 people in Kogi", "metric": "weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%", "comparator": { "against": "recipients' own situation before the transfers (GiveDirectly follow-up survey; no control group)", "before": "weekly income ₦4,500; 97% facing food shortages", "after": "weekly income ₦10,000; 7% facing food shortages", "basis": "measured", "quote": "Weekly income more than doubled, from ₦4,500 to ₦10,000 🍲 Food security improved, with 97% facing food shortages before and just 7% after", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "later": { "status": "scaled", "date": "2025-06-27", "note": "GiveDirectly's implementer report (published 27 June 2025, revised July 2026 to add the tradeoffs of remote phone-based targeting) says it is extending AI-triggered anticipatory cash with Google Research to new countries and other hazards.", "quote": "GiveDirectly continues to work with Google Research to scale the use of AI for anticipatory action to new countries and explore anticipatory cash transfers for other hazards, such as droughts.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "value": { "amount": "$105", "what": "anticipatory cash transfer per recipient before the flood peak (a second $210 transfer followed)", "who": "over 4,600 flood-exposed residents of Ibaji, Kogi State", "estimate": false, "quote": "Once forecasted flood thresholds were met, we sent $105 to over 4,600 individuals before the flood peak.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Re-aligned the account, ratings, AI-role statement, what produced it and the 2022 echo with the GiveDirectly and New Humanitarian texts (JBA pulled forecasts by API and set thresholds while GiveDirectly monitored daily, the $210 recipient count is not stated, the page was updated in July 2026 before the 31 August 2026 investigation, payments used bank details rather than mobile money and the Google.org grant is not tied to Kogi, satellite imagery informed crop and village density rather than poverty maps, late payments are the page's identity checks), confirmed the Ayeni byline against the Wayback snapshot of 22 September 2026, and dropped the word “State” from the headline to bring it under the 64-character limit.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; 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Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and the deployer's press release. 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It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Recipients received cash, and GiveDirectly’s survey reports higher income and fewer food shortages. GiveDirectly also gained experience in sending payments after a forecast threshold was reached. It acknowledged that its recipient units were inconsistent. 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Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. 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Reported equivalent; later naira payments may differ.", "monetary_statements": [ { "id": "DIV-0017", "title": "Flood-triggered cash transfers in Nigeria", "monetary_role": "recipient_transfer", "financial_type": "recipient_transfer", "amount_basis": "transfer", "net_benefit_established": false, "source_review_status": "primary_source_checked", "curation_status": "curated", "observations": [ { "id": "primary", "relationship": "primary", "amount": { "source_text": "US$105 per recipient", "value": "105", "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. 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Each statement retains its source review status. The mapping performs no new source review or currency conversion and does not establish additional value from AI. The Fructus audit matches the original monetary value. The original field and its provenance are retained with this answer.", "record_fields": [], "source_refs": [ "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "https://www.givedirectly.org/flood-forecast-ai" ] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "The amount is not a comparison with aid delivered without AI.", "monetary_statements": [ { "id": "DIV-0017", "title": "Flood-triggered cash transfers in Nigeria", "monetary_role": "recipient_transfer", "financial_type": "recipient_transfer", "amount_basis": "transfer", "net_benefit_established": false, "source_review_status": "primary_source_checked", "curation_status": "curated", "observations": [ { "id": "primary", "relationship": "primary", "amount": { "source_text": "US$105 per recipient", "value": "105", "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. 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About 1,400 recipients were paid on 24 October 2024, after floods peaked on 21 October. A later US$210 transfer has no stated recipient count." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2025-06-27", "note": "GiveDirectly's implementer report (published 27 June 2025, revised July 2026 to add the tradeoffs of remote phone-based targeting) says it is extending AI-triggered anticipatory cash with Google Research to new countries and other hazards.", "quote": "GiveDirectly continues to work with Google Research to scale the use of AI for anticipatory action to new countries and explore anticipatory cash transfers for other hazards, such as droughts.", "url": "https://www.givedirectly.org/flood-forecast-ai/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0095" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Residents of 53 communities in Ibaji, Kogi State, Nigeria." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Residents of 53 communities in Ibaji, Kogi State, Nigeria." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "GiveDirectly reports more than 4,600 recipients. It acknowledged that it had used “household” and “individual” interchangeably." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GiveDirectly reports more than 4,600 recipients. It acknowledged that it had used “household” and “individual” interchangeably." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Cash paid before a forecast flood is read as reducing potential for harm; Trustible's description stresses legal harm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GiveDirectly paid US$105 to more than 4,600 people in flood-prone communities, with 3,250 paid before the flood peak." } }, "outcomes[].distribution": { "state": "value_present", "value": { "kind": "text", "text": "Some residents were excluded because they lacked phone access or their details did not match government or bank records." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Some residents were excluded because they lacked phone access or their details did not match government or bank records." } }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "value_present", "value": { "kind": "text", "text": "The New Humanitarian found that verification and phone-access barriers excluded some of the most vulnerable residents." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The New Humanitarian found that verification and phone-access barriers excluded some of the most vulnerable residents." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Ibaji, Kogi State, Nigeria", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-08-29", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Re-aligned the account, ratings, AI-role statement, what produced it and the 2022 echo with the GiveDirectly and New Humanitarian texts (JBA pulled forecasts by API and set thresholds while GiveDirectly monitored daily, the $210 recipient count is not stated, the page was updated in July 2026 before the 31 August 2026 investigation, payments used bank details rather than mobile money and the Google.org grant is not tied to Kogi, satellite imagery informed crop and village density rather than poverty maps, late payments are the page's identity checks), confirmed the Ayeni byline against the Wayback snapshot of 22 September 2026, and dropped the word “State” from the headline to bring it under the 64-character limit.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://projects.propublica.org/nonprofits/organizations/271661997", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Re-aligned the account, ratings, AI-role statement, what produced it and the 2022 echo with the GiveDirectly and New Humanitarian texts (JBA pulled forecasts by API and set thresholds while GiveDirectly monitored daily, the $210 recipient count is not stated, the page was updated in July 2026 before the 31 August 2026 investigation, payments used bank details rather than mobile money and the Google.org grant is not tied to Kogi, satellite imagery informed crop and village density rather than poverty maps, late payments are the page's identity checks), confirmed the Ayeni byline against the Wayback snapshot of 22 September 2026, and dropped the word “State” from the headline to bring it under the 64-character limit.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://projects.propublica.org/nonprofits/organizations/271661997", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Humanitarian anticipatory action and cash transfers", "segment": "NGO cash assistance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0095-source-0001", "AIBD-0095-source-0002", "AIBD-0095-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Google Flood Hub forecast triggers paid $105 to 4,600 people in Ibaji, Kogi State, mostly before floods peaked on 21 October 2024", "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Google Flood Hub AI river-flood forecasts accessed via API, with JBA Global Resilience hydrology analysis and a 20%-of-community-area trigger rule" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "headline": "Google Flood Hub triggers pay $105 to 4,600 people in Kogi" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "98c5275b8297241b39c99ee3714e3d5dfdb3a9236527256b7a4fc10d1caf9aed" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GiveDirectly and JBA used Flood Hub forecasts to identify communities where at least 20% of the area was expected to flood. GiveDirectly verified residents’ identities and bank details before sending payments." } } }, "summary": { "total": 91, "addressed": 91, "known": 43, "reviewed": 0, "record_mapped": 43, "not_assessed": 48, "complete": false, "by_state": { "value_present": 43, "not_reported": 0, "unresolved": 0, "not_assessed": 48, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0162", "case_url": "records/AIBD-0162.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Pano AI cameras scan for smoke and alert emergency crews with a location. Catching a fire near homes early was the intended use." }, { "label": "AI contribution", "value": "The camera's AI smoke alert was the trigger for dispatch, according to the fire division quoted by CBS Colorado. The only source is a single local news report with named sources. The fire's date, cause of containment and any official report are not documented." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0162", "title": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge; crews hold it to one-eighth of an acre", "summary": "Xcel-funded Pano AI camera alerted crews to a campsite fire east of a Breckenridge neighborhood; firefighters said the early notice kept it to one-eighth of an acre (CBS Colorado, 9 July 2024).", "date_observed": "2024-07-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "CBS Colorado reported on 9 July 2024 on the Wellington fire, which broke out at an unattended campsite just east of a Breckenridge neighborhood. It was spotted by a Pano AI camera financially supported by Xcel Energy. The camera sits on a cell tower with a 360-degree view and submits an alert to emergency crews if it notices smoke. Firefighters said this was the first instance in Summit County where a wildfire was caught early thanks to the camera's early notice. It was kept to one-eighth of an acre. This followed earlier false alarms from legal campfires. Mathew Benedict of Red White and Blue Fire's Wildland Division said a location with an actual lat/long let crews drive straight to the fire. Otherwise they could spend up to an hour finding it. Xcel's Michelle Aguayo said the company had 28 Pano AI cameras installed. The fire's date is not given.", "category": "harm prevented", "unexpected": false, "purpose": "Pano AI cameras scan for smoke and alert emergency crews with a location. Catching a fire near homes early was the intended use.", "system": "Pano AI wildfire detection camera", "model_version": null, "developer": "Pano AI", "deployer": "Xcel Energy (camera funding); alerts received by Red White and Blue Fire", "location": "Breckenridge, United States", "domain": "Energy and infrastructure", "segment": "Electric utility wildfire mitigation", "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "A fire next to a residential neighborhood was kept to one-eighth of an acre. Firefighters credit the camera's early notice, though the counterfactual is their judgment." }, { "group": "organizational", "depth": 2, "reason": "Red White and Blue Fire received an exact location and drove straight to the fire. Benedict said locating a wildfire can otherwise take an hour." }, { "group": "personal", "depth": null, "reason": "No individual beneficiary is documented." } ], "ai_role": { "status": "assessed", "statement": "The camera's AI smoke alert was the trigger for dispatch, according to the fire division quoted by CBS Colorado. The only source is a single local news report with named sources. 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(2026). Xcel Energy deploys AI cameras to detect wildfires early in Colorado. KJCT8, 5 May 2026.", "title": "Xcel Energy deploys AI cameras to detect wildfires early in Colorado", "url": "https://www.kjct8.com/2026/05/05/xcel-energy-deploys-ai-cameras-detect-wildfires-early-colorado/", "date": "2026-05-05", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A camera station with a 360-degree view covering a campsite near housing", "Alerts routed to the local fire district with a lat/long", "A fire district willing to respond to AI alerts despite earlier false alarms" ], "must_hold": [ "Camera coverage of the ignition", "Crew availability to reach the location quickly", "Fire conditions that allow a small crew to contain a sub-acre fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Bear Creek Fire, Douglas County, Colorado; Xcel and CORE Electric Pano AI cameras", "model_version": null, "outcome": "recurred", "source": "https://www.renewableenergyworld.com/power-grid/outage-management/ai-cameras-prevented-large-wildfire-utilities-say/" } ] }, "paired_incident": null, "headline": "Pano AI alert keeps Wellington fire to one-eighth of an acre", "metric": "the early notice kept it to one-eighth of an acre", "comparator": { "against": "locating a reported wildfire without the camera's coordinates", "before": "the process of finding, which often time with wildfires can take an hour", "after": "get a location, an actual lat/long and drive straight through it", "basis": "assumed", "quote": "It's learning Summit County and that's part of the progress, but when we can get a location, an actual lat/long and drive straight through it instead of going through the process of finding, which often time with wildfires can take an hour, just trying to figure out a location and access and figure out all the stuff and then get the troops there...it means a lot.", "url": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/" }, "later": { "status": "scaled", "date": "2026-05-05", "note": "By May 2026 Xcel Energy had more than 130 Pano AI cameras across Colorado, up from 28 at the time of the Wellington fire.", "quote": "Across the state, Xcel Energy has more than 130 Pano AI cameras that give them a look at fires as they’re happening in real time.", "url": "https://www.kjct8.com/2026/05/05/xcel-energy-deploys-ai-cameras-detect-wildfires-early-colorado/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/; https://www.kjct8.com/2026/05/05/xcel-energy-deploys-ai-cameras-detect-wildfires-early-colorado/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.pbs.org/newshour/show/how-satellites-and-ai-cameras-are-detecting-wildfires-before-they-get-out-of-control", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Xcel_Energy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports. 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It was spotted by a Pano AI camera financially supported by Xcel Energy. The camera sits on a cell tower with a 360-degree view and submits an alert to emergency crews if it notices smoke. Firefighters said this was the first instance in Summit County where a wildfire was caught early thanks to the camera's early notice. It was kept to one-eighth of an acre. This followed earlier false alarms from legal campfires. Mathew Benedict of Red White and Blue Fire's Wildland Division said a location with an actual lat/long let crews drive straight to the fire. Otherwise they could spend up to an hour finding it. Xcel's Michelle Aguayo said the company had 28 Pano AI cameras installed. 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Xcel and CORE Electric Pano AI cameras", "outcome": "recurred", "source": "https://www.renewableenergyworld.com/power-grid/outage-management/ai-cameras-prevented-large-wildfire-utilities-say/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A camera station with a 360-degree view covering a campsite near housing", "Alerts routed to the local fire district with a lat/long", "A fire district willing to respond to AI alerts despite earlier false alarms" ], "must_hold": [ "Camera coverage of the ignition", "Crew availability to reach the location quickly", "Fire conditions that allow a small crew to contain a sub-acre fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Bear Creek Fire, Douglas County, Colorado; 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(2026). Xcel Energy deploys AI cameras to detect wildfires early in Colorado. 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(2026). Xcel Energy deploys AI cameras to detect wildfires early in Colorado. 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It was spotted by a Pano AI camera financially supported by Xcel Energy. The camera sits on a cell tower with a 360-degree view and submits an alert to emergency crews if it notices smoke. Firefighters said this was the first instance in Summit County where a wildfire was caught early thanks to the camera's early notice. It was kept to one-eighth of an acre. This followed earlier false alarms from legal campfires. Mathew Benedict of Red White and Blue Fire's Wildland Division said a location with an actual lat/long let crews drive straight to the fire. Otherwise they could spend up to an hour finding it. Xcel's Michelle Aguayo said the company had 28 Pano AI cameras installed. The fire's date is not given." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Pano AI wildfire detection camera" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Pano AI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Xcel-funded Pano AI camera catches the Wellington fire near Breckenridge; crews hold it to one-eighth of an acre", "headline": "Pano AI alert keeps Wellington fire to one-eighth of an acre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "29ac81165bbfb30d7b1712cc3c8a235d2761d00c6b092c2bf203bb66c15f9e75" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "CBS Colorado reported on 9 July 2024 on the Wellington fire, which broke out at an unattended campsite just east of a Breckenridge neighborhood. It was spotted by a Pano AI camera financially supported by Xcel Energy. The camera sits on a cell tower with a 360-degree view and submits an alert to emergency crews if it notices smoke. Firefighters said this was the first instance in Summit County where a wildfire was caught early thanks to the camera's early notice. It was kept to one-eighth of an acre. This followed earlier false alarms from legal campfires. Mathew Benedict of Red White and Blue Fire's Wildland Division said a location with an actual lat/long let crews drive straight to the fire. Otherwise they could spend up to an hour finding it. Xcel's Michelle Aguayo said the company had 28 Pano AI cameras installed. The fire's date is not given.", "replication": { "produced_by": [ "A camera station with a 360-degree view covering a campsite near housing", "Alerts routed to the local fire district with a lat/long", "A fire district willing to respond to AI alerts despite earlier false alarms" ], "must_hold": [ "Camera coverage of the ignition", "Crew availability to reach the location quickly", "Fire conditions that allow a small crew to contain a sub-acre fire" ], "echoes": [ { "date": "2024-06-16", "setting": "Bear Creek Fire, Douglas County, Colorado; Xcel and CORE Electric Pano AI cameras", "outcome": "recurred", "source": "https://www.renewableenergyworld.com/power-grid/outage-management/ai-cameras-prevented-large-wildfire-utilities-say/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0007", "case_url": "records/AIBD-0007.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Built to detect smoke in camera feeds and alert fire dispatchers automatically. This benefit is that purpose." }, { "label": "AI contribution", "value": "The AI alert was the only report of the Auburn fire. Cal Fire's command center verified it and dispatched on it. The same source says the system still produces false positives from dust and clouds, so human verification sits between alert and dispatch." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0007", "title": "ALERTCalifornia AI camera alert sends Cal Fire engines to a night fire near Auburn before any 911 call", "summary": "AI camera alert at 2:33 a.m. on 6 July 2024 sent Cal Fire engines to a fire near Auburn before any 911 call; held to a 30 x 30 ft area", "date_observed": "2024-07-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The ALERTCalifornia camera network is operated by UC San Diego with AI smoke-detection software from DigitalPath of Chico, California, and used by Cal Fire. It posted an alert at 2:33 a.m. on July 6 of a fire near Auburn, in the foothills east of Sacramento. The report, from September 2025, gives the day but not the year. It places the case among the previous year's fires, so 2024 is used. Nobody had called 911. Cal Fire's Grass Valley Emergency Command Center verified the alert and sent engines. Crews put the fire out before it spread beyond a 30 x 30 foot area. Cal Fire Deputy Chief Brian York said in 2024 the cameras picked up 1,668 of the 7,553 fires in Cal Fire's jurisdiction. Of those, 636 came before any person called 911.", "category": "harm prevented", "unexpected": false, "purpose": "Built to detect smoke in camera feeds and alert fire dispatchers automatically. This benefit is that purpose.", "system": "ALERTCalifornia camera network with DigitalPath AI smoke detection", "model_version": null, "developer": "DigitalPath (AI smoke detection); ALERTCalifornia, University of California San Diego (camera network)", "deployer": "California Department of Forestry and Fire Protection (Cal Fire)", "location": "Auburn, United States", "domain": "Wildfire detection and response", "segment": "Public safety / fire services", "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Grass Valley command center dispatched engines on the alert. Crews extinguished the fire at a 30 x 30 ft area, avoiding a larger night-time incident." }, { "group": "societal", "depth": 2, "reason": "Residents near Auburn benefited from a fire being put out before it spread. The damage avoided is not quantified for this fire in the sources." } ], "ai_role": { "status": "assessed", "statement": "The AI alert was the only report of the Auburn fire. Cal Fire's command center verified it and dispatched on it. The same source says the system still produces false positives from dust and clouds, so human verification sits between alert and dispatch." }, "evidence": [ { "kind": "news report", "citation": "Rogers, P. (2025). ALERTCalifornia AI wildfire camera network replaces human lookout towers across California. Bay Area News Group / Tribune News Service, republished by FireRescue1.", "title": "Over 1,000 AI cameras help spot Calif. wildfires before the first 911 call", "url": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call", "date": "2025-09-19", "written_by": "independent", "quote": "an AI camera posted an alert at 2:33 am of a fire near Auburn, in the foothills east of Sacramento. Nobody had called 911." }, { "kind": "news report", "citation": "Rogers, P. (2025). AI cameras are spotting wildfires across California—often before humans call 911. MediaNews Group via Tribune Content Agency, republished by Phys.org.", "title": "AI cameras are spotting wildfires across California—often before humans call 911", "url": "https://phys.org/news/2025-09-ai-cameras-wildfires-california-humans.html", "date": "2025-09-22", "written_by": "independent", "quote": "Of those, 1,668 were picked up by the cameras, said Cal Fire's York. And 38%, or 636, were detected by the cameras before any person called 911" }, { "kind": "news report", "citation": "Reuters (2023). California turns to AI to help spot wildfires. Republished by Gulf News.", "title": "California turns to AI to help spot wildfires", "url": "https://gulfnews.com/world/americas/california-turns-to-ai-to-help-spot-wildfires-1.1691753082670", "date": "2023-08-11", "written_by": "independent", "quote": "Within 45 minutes the fire was out, Cal Fire said." }, { "kind": "press release", "citation": "Office of Governor Gavin Newsom (2023). TIME Recognizes CAL FIRE AI & AlertCalifornia as a Best Invention of 2023. gov.ca.gov.", "title": "TIME Recognizes CAL FIRE AI & AlertCalifornia as a Best Invention of 2023", "url": "https://www.gov.ca.gov/2023/10/24/time-recognizes-cal-fire-ai-alertcalifornia-as-a-best-invention-of-2023/", "date": "2023-10-24", "written_by": "deployer", "quote": "In the first two months, the system had correctly identified 77 fires before any 911 calls came in" }, { "kind": "news report", "citation": "Stevens, A. (2024). AI technology detects fire in rural San Luis Obispo County before 911 calls are made. KSBY.", "title": "AI technology detects fire in rural San Luis Obispo County before 911 calls are made", "url": "https://www.ksby.com/santa-margarita/ai-technology-detects-fire-in-san-luis-obispo-county-before-911-calls-are-made", "date": "2024-10-16", "written_by": "independent", "quote": "Without that early detection, that fire could have grown to a couple of hundred acres in size and potentially burned structures." }, { "kind": "news report", "citation": "Branch, J. (2026). How California is using AI and cameras to stop wildfires. The New York Times, republished by The Philadelphia Inquirer.", "title": "How California is using AI and cameras to stop wildfires", "url": "https://inquirer.com/news/nation-world/remote-cameras-ai-spot-wildfires-crew-satellite-wifi-california-20261015.html", "date": "2026-10-15", "written_by": "independent", "quote": "This system has changed the world of response. We have instant visibility and instant information that we need to make response decisions." } ], "replication": { "produced_by": [ "A fixed camera network with 24/7 AI smoke detection covering remote terrain", "A dispatch center that verifies AI alerts and sends crews without waiting for a 911 call" ], "must_hold": [ "A human verifies the alert before dispatch. The same source reports false positives from dust and clouds", "Crews can reach the fire while it is still small" ], "echoes": [ { "date": null, "setting": "Cleveland National Forest, about 50 miles east of San Diego, California; fire at 3 a.m. after the AI program began 10 July 2023; AI alerted a fire captain, about 60 firefighters responded, fire out within 45 minutes per Cal Fire (Reuters, 11 August 2023)", "model_version": null, "outcome": "recurred", "source": "https://gulfnews.com/world/americas/california-turns-to-ai-to-help-spot-wildfires-1.1691753082670" }, { "date": "2024-10-15", "setting": "La Panza Ranch, San Luis Obispo County, California; ALERTCalifornia AI notified Cal Fire of smoke with no public 911 call; fire held to about 16 acres (KSBY; Paso Robles Daily News, 29 October 2024)", "model_version": null, "outcome": "recurred", "source": "https://www.ksby.com/santa-margarita/ai-technology-detects-fire-in-san-luis-obispo-county-before-911-calls-are-made ; https://pasoroblesdailynews.com/local-fire-detected-by-ai-technology-before-anyone-had-noticed-it/204388/" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "796", "title": "Facebook's Content Moderation System Flagged and Removed Emergency Updates as Spam During Wildfires", "url": "https://incidentdatabase.ai/cite/796", "severity": null, "group": "societal", "basis": "Wildfire alerting in California: Facebook's AI moderation removed Cal Fire and Forest Service evacuation and fire-tracking updates as spam, harming the residents and evacuees whom the ALERTCalifornia camera alert near Auburn protected." }, "headline": "ALERTCalifornia alert sends Cal Fire engines before any 911 call", "metric": "crews put the fire out before it spread beyond a 30 x 30 foot area", "comparator": { "against": "waiting for a 911 call from the public", "before": "Nobody had called 911", "after": "put it out before it spread beyond a 30 x 30 foot area", "basis": "measured", "quote": "Nobody had called 911. Cal Fire's Grass Valley Emergency Command Center verified it and sent engines. Fire crews found the fire and put it out before it spread beyond a 30 x 30 foot area.", "url": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call" }, "later": { "status": "scaled", "date": "2026-10-15", "note": "By October 2026 the network had grown to nearly 1,300 cameras, with AI spotting most fires before a 911 call and the program director planning 1,800 cameras.", "quote": "Since 2023, artificial intelligence has instantly scoured Alert California camera footage, spotting wildfires before a 911 call is received in most cases.", "url": "https://inquirer.com/news/nation-world/remote-cameras-ai-spot-wildfires-crew-satellite-wifi-california-20261015.html" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Year corrected from 2025 to 2024: the source gives only the day and presents the case within its account of the previous year's fires.", "source": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 796 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/796", "editor": "AIBD" }, { "date": "2026-10-20", "change": "FireRescue1 evidence title changed from the page's HTML title element to the displayed headline, and the KSBY item dated 2024-10-16 from the page's publication timestamp (datePublished 2024–10–16T17:40:35–0700).", "source": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call ; https://www.ksby.com/santa-margarita/ai-technology-detects-fire-in-san-luis-obispo-county-before-911-calls-are-made", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call; https://inquirer.com/news/nation-world/remote-cameras-ai-spot-wildfires-crew-satellite-wifi-california-20261015.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.digitalpath.net/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/California_Department_of_Forestry_and_Fire_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0007", "catalog_version": "1.0.0", "source_fingerprint": "01c7e0ca7261efedfecb06f93ea4ec56552d85ff143fdeefa8b3497cd2830c84", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0007-source-0001", "AIBD-0007-source-0002", "AIBD-0007-source-0003", "AIBD-0007-source-0004", "AIBD-0007-source-0005", "AIBD-0007-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "DigitalPath (AI smoke detection); ALERTCalifornia, University of California San Diego (camera network)", "deployer": "California Department of Forestry and Fire Protection (Cal Fire)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI alert was the only report of the Auburn fire. Cal Fire's command center verified it and dispatched on it. The same source says the system still produces false positives from dust and clouds, so human verification sits between alert and dispatch." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The AI alert was the only report of the Auburn fire. Cal Fire's command center verified it and dispatched on it. The same source says the system still produces false positives from dust and clouds, so human verification sits between alert and dispatch." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Grass Valley command center dispatched engines on the alert. Crews extinguished the fire at a 30 x 30 ft area, avoiding a larger night-time incident." }, { "group": "societal", "depth": 2, "reason": "Residents near Auburn benefited from a fire being put out before it spread. The damage avoided is not quantified for this fire in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports and the deployer's press release. 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ALERTCalifornia camera alert near Auburn protected." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-10-15", "note": "By October 2026 the network had grown to nearly 1,300 cameras, with AI spotting most fires before a 911 call and the program director planning 1,800 cameras.", "quote": "Since 2023, artificial intelligence has instantly scoured Alert California camera footage, spotting wildfires before a 911 call is received in most cases.", "url": "https://inquirer.com/news/nation-world/remote-cameras-ai-spot-wildfires-crew-satellite-wifi-california-20261015.html" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the 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"value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The ALERTCalifornia camera network is operated by UC San Diego with AI smoke-detection software from DigitalPath of Chico, California, and used by Cal Fire. It posted an alert at 2:33 a.m. on July 6 of a fire near Auburn, in the foothills east of Sacramento. The report, from September 2025, gives the day but not the year. It places the case among the previous year's fires, so 2024 is used. Nobody had called 911. Cal Fire's Grass Valley Emergency Command Center verified the alert and sent engines. Crews put the fire out before it spread beyond a 30 x 30 foot area. Cal Fire Deputy Chief Brian York said in 2024 the cameras picked up 1,668 of the 7,553 fires in Cal Fire's jurisdiction. Of those, 636 came before any person called 911.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Grass Valley command center dispatched engines on the alert. Crews extinguished the fire at a 30 x 30 ft area, avoiding a larger night-time incident." }, { "group": "societal", "depth": 2, "reason": "Residents near Auburn benefited from a fire being put out before it spread. The damage avoided is not quantified for this fire in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect smoke in camera feeds and alert fire dispatchers automatically. 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It posted an alert at 2:33 a.m. on July 6 of a fire near Auburn, in the foothills east of Sacramento. The report, from September 2025, gives the day but not the year. It places the case among the previous year's fires, so 2024 is used. Nobody had called 911. Cal Fire's Grass Valley Emergency Command Center verified the alert and sent engines. Crews put the fire out before it spread beyond a 30 x 30 foot area. Cal Fire Deputy Chief Brian York said in 2024 the cameras picked up 1,668 of the 7,553 fires in Cal Fire's jurisdiction. Of those, 636 came before any person called 911.", "replication": { "produced_by": [ "A fixed camera network with 24/7 AI smoke detection covering remote terrain", "A dispatch center that verifies AI alerts and sends crews without waiting for a 911 call" ], "must_hold": [ "A human verifies the alert before dispatch. The same source reports false positives from dust and clouds", "Crews can reach the fire while it is still small" ], "echoes": [ { "setting": "Cleveland National Forest, about 50 miles east of San Diego, California; fire at 3 a.m. after the AI program began 10 July 2023; AI alerted a fire captain, about 60 firefighters responded, fire out within 45 minutes per Cal Fire (Reuters, 11 August 2023)", "outcome": "recurred", "source": "https://gulfnews.com/world/americas/california-turns-to-ai-to-help-spot-wildfires-1.1691753082670" }, { "date": "2024-10-15", "setting": "La Panza Ranch, San Luis Obispo County, California; ALERTCalifornia AI notified Cal Fire of smoke with no public 911 call; fire held to about 16 acres (KSBY; Paso Robles Daily News, 29 October 2024)", "outcome": "recurred", "source": "https://www.ksby.com/santa-margarita/ai-technology-detects-fire-in-san-luis-obispo-county-before-911-calls-are-made ; https://pasoroblesdailynews.com/local-fire-detected-by-ai-technology-before-anyone-had-noticed-it/204388/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0161", "case_url": "records/AIBD-0161.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Pano AI cameras rotate 360 degrees on mountaintop towers and use AI to detect smoke plumes. They alert utilities and fire agencies with a triangulated location. Detecting this fire early was the intended use." }, { "label": "AI contribution", "value": "The AI smoke detection produced the alert and location that the utilities and Denver7 describe. The sources are independent news reports quoting operator staff, not an official filing. The operators themselves say the Forest Service identified the fire simultaneously. So the counterfactual contribution of the AI to the three-acre outcome is unverified." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0161", "title": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire; helicopters hold it to three acres", "summary": "Utility-funded Pano AI cameras detected smoke from the 16 June 2024 Bear Creek Fire in Douglas County, Colorado; helicopters were dispatched within 30 minutes and the fire was held to three acres.", "date_observed": "2024-06-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 16 June 2024 Pano AI wildfire-detection cameras on Xcel Energy and CORE Electric Cooperative towers detected smoke. It came from a lightning-caused fire in Douglas County, Colorado. According to Renewable Energy World (18 July 2024), emergency services dispatched two helicopters within 30 minutes of the identification, when the fire was 0.25 acres. The helicopters dropped 17,765 gallons of water over six hours and the fire was limited to three acres. Denver7 (17 July 2024) reported that the fire was contained within a day and was first spotted by the AI-equipped cameras. CORE's wildfire mitigation manager Jordan Ambrogi said alerts typically arrive 10 to 12 minutes after smoke is detected. The utilities, however, said the cameras identified the fire “simultaneously” with the U.S. Forest Service. So the cameras' independent contribution to containment rests on the operators' own statements (CORE interim CEO Pam Feuerstein). The OECD AI Incidents Monitor carries a separate positively framed record of Xcel's November 2023 Pano AI deployment.", "category": "protective response", "unexpected": false, "purpose": "Pano AI cameras rotate 360 degrees on mountaintop towers and use AI to detect smoke plumes. They alert utilities and fire agencies with a triangulated location. Detecting this fire early was the intended use.", "system": "Pano AI wildfire detection cameras (Pano Rapid Detect)", "model_version": null, "developer": "Pano AI", "deployer": "Xcel Energy and CORE Electric Cooperative", "location": "Douglas County, Colorado, United States", "domain": "Energy and infrastructure", "segment": "Electric utility wildfire mitigation", "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "The fire was held to three acres after helicopters were dispatched within 30 minutes. CORE's CEO called the detection and location “crucial”. Simultaneous Forest Service identification limits what can be attributed to the cameras." }, { "group": "organizational", "depth": 2, "reason": "CORE Electric and Xcel Energy obtained a fast, located alert supporting their wildfire mitigation strategy. Denver7 quotes CORE's Jordan Ambrogi saying alerts typically beat 911 calls." }, { "group": "personal", "depth": null, "reason": "No individual beneficiary is documented in the sources." } ], "ai_role": { "status": "assessed", "statement": "The AI smoke detection produced the alert and location that the utilities and Denver7 describe. The sources are independent news reports quoting operator staff, not an official filing. The operators themselves say the Forest Service identified the fire simultaneously. So the counterfactual contribution of the AI to the three-acre outcome is unverified." }, "evidence": [ { "kind": "news report", "citation": "Colette Bordelon, 'Douglas County wildfire first detected by cameras equipped with AI', Denver7, 17 July 2024", "title": "Douglas County wildfire first detected by cameras equipped with AI", "url": "https://www.denver7.com/news/wildfire/douglas-county-wildfire-first-detected-by-cameras-equipped-with-ai", "date": "2024-07-17", "written_by": "independent", "quote": "The Bear Creek Fire in Douglas County, which was contained within a day, was first spotted by cameras equipped with artificial intelligence (AI)." }, { "kind": "news report", "citation": "John Engel, 'AI cameras prevented large wildfire, utilities say', Renewable Energy World / Factor This, 18 July 2024", "title": "AI cameras prevented large wildfire, utilities say", "url": "https://www.renewableenergyworld.com/power-grid/outage-management/ai-cameras-prevented-large-wildfire-utilities-say/", "date": "2024-07-18", "written_by": "independent", "quote": "Within 30 minutes of the identification, emergency services dispatched two helicopters to the fire when it was .25 acres." }, { "kind": "news report", "citation": "John Engel, Renewable Energy World, 18 July 2024 (operator attribution)", "title": "AI cameras prevented large wildfire, utilities say", "url": "https://www.renewableenergyworld.com/power-grid/outage-management/ai-cameras-prevented-large-wildfire-utilities-say/", "date": "2024-07-18", "written_by": "independent", "quote": "The utilities said their wildfire camera system, provided by Pano AI, identified the wildfire “simultaneously” with the U.S. Forest Service." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, record 2023-11-07-64f3, 'Xcel Energy Deploys AI-Powered Cameras for Early Wildfire Detection in Colorado'", "title": "Xcel Energy Deploys AI-Powered Cameras for Early Wildfire Detection in Colorado", "url": "https://oecd.ai/en/incidents/2023-11-07-64f3", "date": "2023-11-07", "written_by": "independent", "quote": "Xcel Energy, in partnership with Pano AI, is expanding the use of AI-driven wildfire detection cameras across Colorado." }, { "kind": "news report", "citation": "Wilson, S. (2024). AI-powered, fire-spotting cameras dispatch firefighters to Colorado wildfire. CBS Colorado, 9 July 2024.", "title": "AI-powered, fire-spotting cameras dispatch firefighters to Colorado wildfire", "url": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/", "date": "2024-07-09", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Camera stations with 360-degree views sited on cell towers within roughly 10 miles of the ignition", "Alert routing to both the utility and local fire agencies with a triangulated location", "Lightning-caused ignition in daylight in a remote area, with aerial resources available for dispatch" ], "must_hold": [ "Camera coverage of the ignition point and functioning AI smoke detection", "An agency able to act on the alert within minutes", "Independent confirmation of who detected the fire first, since the utilities report simultaneous Forest Service identification" ], "echoes": [ { "date": "2024-07-09", "setting": "Wellington fire near Breckenridge, Summit County, Colorado; Xcel-funded Pano AI camera", "model_version": null, "outcome": "recurred", "source": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/" }, { "date": "2024-08-29", "setting": "Austin Energy full deployment of 13 Pano AI cameras, Texas; no detection reported", "model_version": null, "outcome": "unknown", "source": "https://austinenergy.com/about/news/news-releases/2024/Austin-Energy-announces-full-deployment-of-AI-driven-Early-Wildfire-Detection-System" } ] }, "paired_incident": null, "headline": "Pano AI cameras spot the Bear Creek Fire; held to three acres", "metric": "helicopters were dispatched within 30 minutes and the fire was held to three acres", "comparator": { "against": "members of the public calling 911", "before": "anyone else calling 911", "after": "within 10 minutes of detecting smoke, 10 to 12 minutes", "basis": "assumed", "quote": "Typically, it's within 10 minutes of detecting smoke, 10 to 12 minutes. Depending on where it is, how remote an area, it'll beat anyone else calling 911.", "url": "https://www.denver7.com/news/wildfire/douglas-county-wildfire-first-detected-by-cameras-equipped-with-ai" }, "later": { "status": "persisted", "date": "2024-07-09", "note": "Three weeks later an Xcel-funded Pano AI camera caught the Wellington fire near Breckenridge, which firefighters held to one-eighth of an acre.", "quote": "While that has resulted in a few false alarms in Summit County, this was the first instance where a wildfire was caught early, and kept to only one-eighth of an acre, firefighters said, thanks to the camera's early notice.", "url": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.denver7.com/news/wildfire/douglas-county-wildfire-first-detected-by-cameras-equipped-with-ai; https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.pbs.org/newshour/show/how-satellites-and-ai-cameras-are-detecting-wildfires-before-they-get-out-of-control", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Xcel_Energy", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0161", "catalog_version": "1.0.0", "source_fingerprint": "2ea991607cecb1e9dc8085e2b93f1a0304a5267232553bd8ca55573b129d9ab6", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0161-source-0001", "AIBD-0161-source-0002", "AIBD-0161-source-0003", "AIBD-0161-source-0004", "AIBD-0161-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Pano AI", "deployer": "Xcel Energy and CORE Electric Cooperative" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI smoke detection produced the alert and location that the utilities and Denver7 describe. The sources are independent news reports quoting operator staff, not an official filing. The operators themselves say the Forest Service identified the fire simultaneously. So the counterfactual contribution of the AI to the three-acre outcome is unverified." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The AI smoke detection produced the alert and location that the utilities and Denver7 describe. The sources are independent news reports quoting operator staff, not an official filing. The operators themselves say the Forest Service identified the fire simultaneously. 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The OECD AI Incidents Monitor carries a separate positively framed record of Xcel's November 2023 Pano AI deployment." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Pano AI wildfire detection cameras (Pano Rapid Detect)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Pano AI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Pano AI cameras on Xcel and CORE Electric towers spot the Bear Creek Fire; helicopters hold it to three acres", "headline": "Pano AI cameras spot the Bear Creek Fire; held to three acres" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "2ea991607cecb1e9dc8085e2b93f1a0304a5267232553bd8ca55573b129d9ab6" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 16 June 2024 Pano AI wildfire-detection cameras on Xcel Energy and CORE Electric Cooperative towers detected smoke. It came from a lightning-caused fire in Douglas County, Colorado. According to Renewable Energy World (18 July 2024), emergency services dispatched two helicopters within 30 minutes of the identification, when the fire was 0.25 acres. The helicopters dropped 17,765 gallons of water over six hours and the fire was limited to three acres. Denver7 (17 July 2024) reported that the fire was contained within a day and was first spotted by the AI-equipped cameras. CORE's wildfire mitigation manager Jordan Ambrogi said alerts typically arrive 10 to 12 minutes after smoke is detected. The utilities, however, said the cameras identified the fire “simultaneously” with the U.S. Forest Service. So the cameras' independent contribution to containment rests on the operators' own statements (CORE interim CEO Pam Feuerstein). The OECD AI Incidents Monitor carries a separate positively framed record of Xcel's November 2023 Pano AI deployment.", "replication": { "produced_by": [ "Camera stations with 360-degree views sited on cell towers within roughly 10 miles of the ignition", "Alert routing to both the utility and local fire agencies with a triangulated location", "Lightning-caused ignition in daylight in a remote area, with aerial resources available for dispatch" ], "must_hold": [ "Camera coverage of the ignition point and functioning AI smoke detection", "An agency able to act on the alert within minutes", "Independent confirmation of who detected the fire first, since the utilities report simultaneous Forest Service identification" ], "echoes": [ { "date": "2024-07-09", "setting": "Wellington fire near Breckenridge, Summit County, Colorado; Xcel-funded Pano AI camera", "outcome": "recurred", "source": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/" }, { "date": "2024-08-29", "setting": "Austin Energy full deployment of 13 Pano AI cameras, Texas; no detection reported", "outcome": "unknown", "source": "https://austinenergy.com/about/news/news-releases/2024/Austin-Energy-announces-full-deployment-of-AI-driven-Early-Wildfire-Detection-System" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0232", "case_url": "records/AIBD-0232.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Identify unknown suspects by comparing CCTV images against police custody image databases." }, { "label": "AI contribution", "value": "The retrospective face match supplied the suspect's identity from CCTV. The arrest arose from a later patrol sighting, and the conviction rested on seized drugs and phone messages. Sources are the deploying force's release and a local newspaper quoting the named officer. The vendor, match score and any false-match safeguards are not stated." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0232", "title": "Cheshire Police facial recognition matches CCTV to Keenan Hughes; county lines dealers jailed for over five years", "summary": "Cheshire Constabulary's facial recognition matched CCTV of an unknown Northwich dealer to Keenan Hughes; he and Vincent Potter were stopped on 17 May 2024 with crack cocaine and jailed on 1 July 2024.", "date_observed": "2024-05-17", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Cheshire Constabulary's release states that in April 2024 officers in Northwich learned of a phone number used by the “Kyle” county lines gang. They recovered closed-circuit television (CCTV) of an unknown man suspected of using it. The force facial recognition system matched the image to Keenan Hughes of Connah's Quay. On Friday 17 May officers on patrol spotted Hughes on Winnington Street with Vincent Potter. A search found 99 wraps of crack cocaine worth about £990 and a hunting-style knife on Hughes. Potter had two phones, a lock knife and £960. Both pleaded guilty, and on 1 July 2024 Chester Crown Court jailed Hughes (18) for 28 months and Potter (19) for 34 months. PC Dean Butterworth said Hughes was not known locally and the technology quickly identified him as a suspect. The OECD AI Incidents Monitor records the case, listing a harm type of human or fundamental rights.", "category": "benefit created", "unexpected": false, "purpose": "Identify unknown suspects by comparing CCTV images against police custody image databases.", "system": "Cheshire Constabulary facial recognition system (retrospective)", "model_version": null, "developer": "Not named in the sources", "deployer": "Cheshire Constabulary, Northwich Proactive Policing Unit", "location": "Northwich, United Kingdom", "domain": "Policing / drug supply", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "Two county lines dealers convicted and 99 wraps of crack cocaine and two knives taken off the street, per the force and the court outcome." } ], "ai_role": { "status": "assessed", "statement": "The retrospective face match supplied the suspect's identity from CCTV. The arrest arose from a later patrol sighting, and the conviction rested on seized drugs and phone messages. Sources are the deploying force's release and a local newspaper quoting the named officer. The vendor, match score and any false-match safeguards are not stated." }, "evidence": [ { "kind": "press release", "citation": "Cheshire Constabulary, news release, 2 Jul 2024", "title": "Facial recognition technology helps to identify Northwich drug dealers", "url": "https://www.cheshire.police.uk/news/cheshire/news/articles/2024/6/facial-recognition-technology-helps-to-identify-northwich-drug-dealers/", "date": "2024-07-02", "written_by": "deployer", "quote": "The image was submitted to the force facial recognition system and came through as a match to a man called Keenan Hughes from Connah’s Quay." }, { "kind": "news report", "citation": "Rob Goulding, The Leader (Leader Live), 2 Jul 2024", "title": "County Lines drug dealers operating in Northwich jailed", "url": "https://www.leaderlive.co.uk/news/24424400.county-lines-drug-dealers-operating-northwich-jailed/", "date": "2024-07-02", "written_by": "independent", "quote": "Hughes was not known in the Northwich area, but thanks to the CCTV footage and facial recognition technology, we were quickly able to identify him as a suspect." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2024-07-02-ee42", "title": "Police use facial recognition AI to catch county lines drug dealers", "url": "https://oecd.ai/en/incidents/2024-07-02-ee42", "date": "2024-07-02", "written_by": "independent", "quote": "Its use directly led to the arrest and conviction of individuals involved in drug dealing, which is a harm to communities." }, { "kind": "news report", "citation": "Deeside.com (2 Jul 2024). Facial recognition technology helps to identify Deeside drug dealer (reproduces Cheshire Constabulary's release and PC Dean Butterworth's quotes).", "title": "Facial recognition technology helps to identify Deeside drug dealer", "url": "https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer", "date": "2024-07-02", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A usable CCTV still of an unknown suspect", "A custody-image database containing the suspect", "Officers able to corroborate the match through patrol and search" ], "must_hold": [ "The suspect must have a prior custody image", "Lawful retrospective use of facial recognition", "Corroborating evidence beyond the match" ], "echoes": [] }, "paired_incident": null, "headline": "Cheshire facial recognition match jails county lines dealers", "metric": "jailed Hughes (18) for 28 months and Potter (19) for 34 months", "comparator": { "against": "conventional identification of a suspect who was not known locally", "before": "Hughes was not known in the Northwich area", "after": "we were quickly able to identify him as a suspect", "basis": "assumed", "quote": "Hughes was not known in the Northwich area, but thanks to the CCTV footage and facial recognition technology, we were quickly able to identify him as a suspect.", "url": "https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer" }, "later": { "status": "litigated", "date": "2024-07-01", "note": "Both men were sentenced at Chester Crown Court on 1 July 2024, Hughes to 28 months and Potter to 34 months.", "quote": "Keenan Hughes, 18, of Connah's Quay, and Vincent Potter, 19, of no fixed abode, appeared at Chester Crown Court on Monday, 1 July, where they were sentenced to over five years in prison.", "url": "https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer" }, "value": { "amount": "£990", "what": "crack cocaine seized from Hughes (99 wraps, approximate street value)", "who": "Cheshire Constabulary's Northwich Proactive Policing Team (seized from Keenan Hughes)", "estimate": false, "quote": "99 individual wraps of crack cocaine, worth approximately £990", "url": "https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer" }, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer; https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer; https://www.deeside.com/facial-recognition-technology-helps-to-identify-deeside-drug-dealer", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Cheshire_Constabulary", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, a registry entry and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0232", "catalog_version": "1.0.0", "source_fingerprint": "0f073ec2418ca5c35fafb7ebb5811c382fb528a6b1785b9b795554ab9d1c0f50", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0232-source-0001", "AIBD-0232-source-0002", "AIBD-0232-source-0003", "AIBD-0232-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0232-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Not named in the sources", "deployer": "Cheshire Constabulary, Northwich Proactive Policing Unit" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The retrospective face match supplied the suspect's identity from CCTV. 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They recovered closed-circuit television (CCTV) of an unknown man suspected of using it. The force facial recognition system matched the image to Keenan Hughes of Connah's Quay. On Friday 17 May officers on patrol spotted Hughes on Winnington Street with Vincent Potter. A search found 99 wraps of crack cocaine worth about £990 and a hunting-style knife on Hughes. Potter had two phones, a lock knife and £960. Both pleaded guilty, and on 1 July 2024 Chester Crown Court jailed Hughes (18) for 28 months and Potter (19) for 34 months. PC Dean Butterworth said Hughes was not known locally and the technology quickly identified him as a suspect. The OECD AI Incidents Monitor records the case, listing a harm type of human or fundamental rights." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Cheshire Constabulary facial recognition system (retrospective)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Not named in the sources" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Cheshire Police facial recognition matches CCTV to Keenan Hughes; county lines dealers jailed for over five years", "headline": "Cheshire facial recognition match jails county lines dealers" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0f073ec2418ca5c35fafb7ebb5811c382fb528a6b1785b9b795554ab9d1c0f50" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Cheshire Constabulary's release states that in April 2024 officers in Northwich learned of a phone number used by the “Kyle” county lines gang. They recovered closed-circuit television (CCTV) of an unknown man suspected of using it. The force facial recognition system matched the image to Keenan Hughes of Connah's Quay. On Friday 17 May officers on patrol spotted Hughes on Winnington Street with Vincent Potter. A search found 99 wraps of crack cocaine worth about £990 and a hunting-style knife on Hughes. Potter had two phones, a lock knife and £960. Both pleaded guilty, and on 1 July 2024 Chester Crown Court jailed Hughes (18) for 28 months and Potter (19) for 34 months. PC Dean Butterworth said Hughes was not known locally and the technology quickly identified him as a suspect. The OECD AI Incidents Monitor records the case, listing a harm type of human or fundamental rights.", "replication": { "produced_by": [ "A usable CCTV still of an unknown suspect", "A custody-image database containing the suspect", "Officers able to corroborate the match through patrol and search" ], "must_hold": [ "The suspect must have a prior custody image", "Lawful retrospective use of facial recognition", "Corroborating evidence beyond the match" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0335", "case_url": "records/AIBD-0335.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Detect undeclared pools and buildings from aerial imagery so that they are added to the cadastral base and taxed. This was the tool's intended purpose." }, { "label": "AI contribution", "value": "The AI detects pool outlines on aerial photographs and compares them with declarations. An agent verifies each detection before the construction is added to the cadastral value (Ouest-France). The 140,000 pools and €40 million are the minister's own attribution to the tool, relayed by several news outlets. CNews cites the DGFiP annual report via Ouest-France. No official document is among the sources. A CGT Finances publiques official told Ouest-France that field colleagues put the algorithm's error threshold at 55–60%. So the detections required substantial human checking. No commune-level outcome is on record. The Dordogne figure is the most local one read." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "France" ] }, "record": { "id": "AIBD-0335", "title": "France's Foncier innovant aerial-image AI detects 140,000 undeclared pools and about €40 million in extra property tax by 20 March 2024; Dordogne alone 1,500 in 2023", "summary": "Minister Cazenave (20 March 2024): DGFiP's AI tool detected 140,000 undeclared pools, c. €40m extra property tax for local authorities; Dordogne found 1,500 in 2023. No commune-level figure found.", "date_observed": "2024-03-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 20 March 2024 Thomas Cazenave, minister for public accounts, presented the first bilan of the government's anti-fraud plan at Bercy. He said the DGFiP's algorithmic tool, Foncier innovant, introduced in October 2021, had detected 140,000 undeclared swimming pools. The tool detects pools and buildings on IGN aerial photographs and compares them with declarations. An agent then checks each detection. That would let local authorities collect about €40 million in property tax. Owners received DGFiP letters asking them to regularize (RMC, Ouest-France, L'Indépendant, 21 March 2024). CNews (11 June 2024) cites the DGFiP annual activity report as reported by Ouest-France. It gives 140,000 pools identified in 2023 and €40 million that should have been paid in 2023. That compares with 20,000 pools in the 2022 experiment in nine départements (20 Minutes). L'Indépendant instead states 120,000 for 2022, so the press accounts of the yearly split disagree. At département level, ici (France Bleu) reported on 24 March 2024 on the Dordogne public-finance office. It had found about 1,500 undeclared pools in 2023 among some 30,000 in the département. It would send property-tax catch-up notices by the end of March. No official DGFiP or ministry document is among the sources, as the ministry's website was not available. No commune-level result was found.", "category": "benefit created", "unexpected": false, "purpose": "Detect undeclared pools and buildings from aerial imagery so that they are added to the cadastral base and taxed. This was the tool's intended purpose.", "system": "Foncier innovant (DGFiP deep-learning detection of pools and buildings on IGN aerial photographs, built with Capgemini and Google)", "model_version": null, "developer": "Direction générale des Finances publiques (DGFiP) with Capgemini and Google", "deployer": "Direction générale des Finances publiques (DGFiP), ministère de l'Économie et des Finances", "location": "France (national; Dordogne example)", "domain": "Public finance: property-tax base and anti-fraud", "segment": "Tax administration", "benefit_type": "Increased Product Revenue", "taxonomy_note": "Tax yield is read as the authority's revenue; Trustible's description is written for product sales.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Owners of detected pools bear a cost; no individual beneficiary is documented." }, { "group": "organizational", "depth": 2, "reason": "Material to the DGFiP and to local authorities. The minister attributed about €40 million of additional property tax to the tool. Dordogne alone is recovering tax on about 1,500 pools." }, { "group": "societal", "depth": 1, "reason": "About €40 million nationally is small against total property-tax receipts. The press accounts disagree on the yearly split." } ], "ai_role": { "status": "assessed", "statement": "The AI detects pool outlines on aerial photographs and compares them with declarations. An agent verifies each detection before the construction is added to the cadastral value (Ouest-France). The 140,000 pools and €40 million are the minister's own attribution to the tool, relayed by several news outlets. CNews cites the DGFiP annual report via Ouest-France. No official document is among the sources. A CGT Finances publiques official told Ouest-France that field colleagues put the algorithm's error threshold at 55–60%. So the detections required substantial human checking. No commune-level outcome is on record. The Dordogne figure is the most local one read." }, "evidence": [ { "kind": "news report", "citation": "Dussourt, G. (2024). Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros. RMC, 21 Mar 2024 (quoting minister Thomas Cazenave).", "title": "Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "date": "2024-03-21", "written_by": "independent", "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière" }, { "kind": "news report", "citation": "Ouest-France (2024). Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés. 21 Mar 2024.", "title": "Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "l’outil algorithmique introduit par la DGFiP en octobre 2021 avait permis la détection de 140 000 piscines, pour 40 millions d’euros de recettes supplémentaires" }, { "kind": "news report", "citation": "Ouest-France (2024). Same article, statements on verification and on error rates (Olivier Vilois, CGT Finances publiques).", "title": "Garages, vérandas, piscines… (verification and error rate)", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "D’après mes collègues sur le terrain, le seuil d’erreurs des algorithmes atteint 55 à 60% en ce moment" }, { "kind": "news report", "citation": "CNews (2024). Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA. 11 Jun 2024 (citing the DGFiP annual activity report via Ouest-France).", "title": "Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA", "url": "https://www.cnews.fr/france/2024-06-11/piscines-non-declarees-le-fisc-repere-140000-fraudeurs-grace-lia-1511264", "date": "2024-06-11", "written_by": "independent", "quote": "En 2023, cette technique a permis à la Direction générale des finances publiques d'identifier 140.000 piscines non déclarées, selon son rapport annuel d'activité publié par Ouest France." }, { "kind": "news report", "citation": "Bertrand, M. (2024). Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle. ici (France Bleu Périgord), 24 Mar 2024.", "title": "Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle", "url": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279", "date": "2024-03-24", "written_by": "independent", "quote": "Les finances publiques de Dordogne ont repéré quelque 1.500 piscines non déclarées au fisc en Dordogne, au cours de l'année 2023, sur les quelque 30.000 piscines du département." }, { "kind": "news report", "citation": "20 Minutes (2024). Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023. 12 Jun 2024.", "title": "Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023", "url": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023", "date": "2024-06-12", "written_by": "independent", "quote": "En 2022, 20.000 piscines non déclarées avaient ainsi été repérées pour un redressement estimé à 10 millions d’euros." } ], "replication": { "produced_by": [ "IGN aerial photographs covering the whole territory", "A detection model built with Capgemini and Google, generalized nationally after the 2022 nine-département experiment", "Comparison of detected outlines with cadastral declarations", "Agent verification of each detection and letters inviting owners to regularize" ], "must_hold": [ "Recent, high-resolution aerial coverage", "A false-detection rate low enough for agents to verify (a union official cites 55–60% errors)", "Legal basis to add undeclared constructions to the cadastral value", "Local offices with capacity to send and handle catch-up notices" ], "echoes": [ { "date": "2022-12-31", "setting": "2022 experiment in nine départements: about 20,000 undeclared pools, about €10 million", "model_version": null, "outcome": "recurred", "source": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" }, { "date": "2024-03-24", "setting": "Dordogne département, 2023 campaign: about 1,500 undeclared pools, catch-up notices sent by end of March 2024", "model_version": null, "outcome": "recurred", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279" }, { "date": "2024-03-20", "setting": "Announced extension of the same tool from pools to undeclared buildings (garages, verandas); no outcome yet", "model_version": null, "outcome": "unknown", "source": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727" } ] }, "paired_incident": null, "headline": "Foncier innovant finds 140,000 undeclared pools", "metric": "detected 140,000 undeclared pools, c. €40m extra property tax", "comparator": { "against": "a surveyor noticing a new pool in a neighborhood by eye and checking the cadastre", "before": "le géomètre qui remarquait une nouvelle piscine dans un quartier et devait vérifier dans le cadastre si elle était déclarée ou pas", "after": "Dans toute la France, 140.000 piscines clandestines ont été repérées, ce qui va rapporter 40 millions d'euros.", "basis": "assumed", "quote": "Fini, le géomètre qui remarquait une nouvelle piscine dans un quartier et devait vérifier dans le cadastre si elle était déclarée ou pas. Désormais, les impôts utilisent des photos prises par avion et analysées par l'intelligence artificielle qui repère automatiquement les carrés bleus des piscines et les compare aux fichiers existants.", "url": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279" }, "later": { "status": "persisted", "date": "2024-06-12", "note": "In June 2024 the DGFiP's 2023 activity report, as relayed by 20 Minutes and CNews, restated the 140,000 pools and the 40 million euros of property tax, against 20,000 pools in 2022, and the plan to extend detection to undeclared buildings.", "quote": "140.000 piscines non déclarées ont été épinglées par le fisc en 2023, a dévoilé la Direction générale des finances publiques ce mardi dans son rapport d’activité 2023 relayé par Ouest-France.", "url": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" }, "value": { "amount": "40 millions d’euros", "what": "additional property tax (taxe foncière) to be collected on the 140,000 detected pools", "who": "local authorities (collectivités), which receive the taxe foncière", "estimate": true, "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html" }, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279; https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023; https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Revenue under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7; https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0335", "catalog_version": "1.0.0", "source_fingerprint": "87e51983bab417c733b46e4cfa086ffdca9be62d3de6113d831a444fa7d06335", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0335-source-0001", "AIBD-0335-source-0002", "AIBD-0335-source-0003", "AIBD-0335-source-0004", "AIBD-0335-source-0005", "AIBD-0335-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0335-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Direction générale des Finances publiques (DGFiP) with Capgemini and Google", "deployer": "Direction générale des Finances publiques (DGFiP), ministère de l'Économie et des Finances" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI detects pool outlines on aerial photographs and compares them with declarations. An agent verifies each detection before the construction is added to the cadastral value (Ouest-France). The 140,000 pools and €40 million are the minister's own attribution to the tool, relayed by several news outlets. CNews cites the DGFiP annual report via Ouest-France. No official document is among the sources. A CGT Finances publiques official told Ouest-France that field colleagues put the algorithm's error threshold at 55–60%. So the detections required substantial human checking. No commune-level outcome is on record. The Dordogne figure is the most local one read." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The AI detects pool outlines on aerial photographs and compares them with declarations. An agent verifies each detection before the construction is added to the cadastral value (Ouest-France). The 140,000 pools and €40 million are the minister's own attribution to the tool, relayed by several news outlets. CNews cites the DGFiP annual report via Ouest-France. No official document is among the sources. A CGT Finances publiques official told Ouest-France that field colleagues put the algorithm's error threshold at 55–60%. So the detections required substantial human checking. No commune-level outcome is on record. The Dordogne figure is the most local one read." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Owners of detected pools bear a cost; no individual beneficiary is documented." }, { "group": "organizational", "depth": 2, "reason": "Material to the DGFiP and to local authorities. The minister attributed about €40 million of additional property tax to the tool. Dordogne alone is recovering tax on about 1,500 pools." }, { "group": "societal", "depth": 1, "reason": "About €40 million nationally is small against total property-tax receipts. The press accounts disagree on the yearly split." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Owners of detected pools bear a cost; no individual beneficiary is documented." }, { "group": "organizational", "depth": 2, "reason": "Material to the DGFiP and to local authorities. The minister attributed about €40 million of additional property tax to the tool. Dordogne alone is recovering tax on about 1,500 pools." }, { "group": "societal", "depth": 1, "reason": "About €40 million nationally is small against total property-tax receipts. The press accounts disagree on the yearly split." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Owners of detected pools bear a cost; no individual beneficiary is documented." }, { "group": "organizational", "depth": 2, "reason": "Material to the DGFiP and to local authorities. The minister attributed about €40 million of additional property tax to the tool. Dordogne alone is recovering tax on about 1,500 pools." }, { "group": "societal", "depth": 1, "reason": "About €40 million nationally is small against total property-tax receipts. The press accounts disagree on the yearly split." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "40 millions d’euros", "what": "additional property tax (taxe foncière) to be collected on the 140,000 detected pools", "who": "local authorities (collectivités), which receive the taxe foncière", "estimate": true, "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "40 millions d’euros", "what": "additional property tax (taxe foncière) to be collected on the 140,000 detected pools", "who": "local authorities (collectivités), which receive the taxe foncière", "estimate": true, "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "40 millions d’euros", "what": "additional property tax (taxe foncière) to be collected on the 140,000 detected pools", "who": "local authorities (collectivités), which receive the taxe foncière", "estimate": true, "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "detected 140,000 undeclared pools, c. €40m extra property tax", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2024-06-12", "note": "In June 2024 the DGFiP's 2023 activity report, as relayed by 20 Minutes and CNews, restated the 140,000 pools and the 40 million euros of property tax, against 20,000 pools in 2022, and the plan to extend detection to undeclared buildings.", "quote": "140.000 piscines non déclarées ont été épinglées par le fisc en 2023, a dévoilé la Direction générale des finances publiques ce mardi dans son rapport d’activité 2023 relayé par Ouest-France.", "url": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0335" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Revenue", "taxonomy_note": "Tax yield is read as the authority's revenue; Trustible's description is written for product sales." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 20 March 2024 Thomas Cazenave, minister for public accounts, presented the first bilan of the government's anti-fraud plan at Bercy. He said the DGFiP's algorithmic tool, Foncier innovant, introduced in October 2021, had detected 140,000 undeclared swimming pools. The tool detects pools and buildings on IGN aerial photographs and compares them with declarations. An agent then checks each detection. That would let local authorities collect about €40 million in property tax. Owners received DGFiP letters asking them to regularize (RMC, Ouest-France, L'Indépendant, 21 March 2024). CNews (11 June 2024) cites the DGFiP annual activity report as reported by Ouest-France. It gives 140,000 pools identified in 2023 and €40 million that should have been paid in 2023. That compares with 20,000 pools in the 2022 experiment in nine départements (20 Minutes). L'Indépendant instead states 120,000 for 2022, so the press accounts of the yearly split disagree. At département level, ici (France Bleu) reported on 24 March 2024 on the Dordogne public-finance office. It had found about 1,500 undeclared pools in 2023 among some 30,000 in the département. It would send property-tax catch-up notices by the end of March. No official DGFiP or ministry document is among the sources, as the ministry's website was not available. No commune-level result was found.", "benefit_type": "Increased Product Revenue" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Owners of detected pools bear a cost; no individual beneficiary is documented." }, { "group": "organizational", "depth": 2, "reason": "Material to the DGFiP and to local authorities. The minister attributed about €40 million of additional property tax to the tool. Dordogne alone is recovering tax on about 1,500 pools." }, { "group": "societal", "depth": 1, "reason": "About €40 million nationally is small against total property-tax receipts. The press accounts disagree on the yearly split." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Detect undeclared pools and buildings from aerial imagery so that they are added to the cadastral base and taxed. This was the tool's intended purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "France (national; Dordogne example)", "developer_country": "France", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Detect undeclared pools and buildings from aerial imagery so that they are added to the cadastral base and taxed. This was the tool's intended purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-03-20", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279; https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023; https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Revenue under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7; https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "IGN aerial photographs covering the whole territory", "A detection model built with Capgemini and Google, generalized nationally after the 2022 nine-département experiment", "Comparison of detected outlines with cadastral declarations", "Agent verification of each detection and letters inviting owners to regularize" ], "must_hold": [ "Recent, high-resolution aerial coverage", "A false-detection rate low enough for agents to verify (a union official cites 55–60% errors)", "Legal basis to add undeclared constructions to the cadastral value", "Local offices with capacity to send and handle catch-up notices" ], "echoes": [ { "date": "2022-12-31", "setting": "2022 experiment in nine départements: about 20,000 undeclared pools, about €10 million", "outcome": "recurred", "source": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" }, { "date": "2024-03-24", "setting": "Dordogne département, 2023 campaign: about 1,500 undeclared pools, catch-up notices sent by end of March 2024", "outcome": "recurred", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279" }, { "date": "2024-03-20", "setting": "Announced extension of the same tool from pools to undeclared buildings (garages, verandas); no outcome yet", "outcome": "unknown", "source": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "IGN aerial photographs covering the whole territory", "A detection model built with Capgemini and Google, generalized nationally after the 2022 nine-département experiment", "Comparison of detected outlines with cadastral declarations", "Agent verification of each detection and letters inviting owners to regularize" ], "must_hold": [ "Recent, high-resolution aerial coverage", "A false-detection rate low enough for agents to verify (a union official cites 55–60% errors)", "Legal basis to add undeclared constructions to the cadastral value", "Local offices with capacity to send and handle catch-up notices" ], "echoes": [ { "date": "2022-12-31", "setting": "2022 experiment in nine départements: about 20,000 undeclared pools, about €10 million", "outcome": "recurred", "source": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" }, { "date": "2024-03-24", "setting": "Dordogne département, 2023 campaign: about 1,500 undeclared pools, catch-up notices sent by end of March 2024", "outcome": "recurred", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279" }, { "date": "2024-03-20", "setting": "Announced extension of the same tool from pools to undeclared buildings (garages, verandas); no outcome yet", "outcome": "unknown", "source": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279; https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023; https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Revenue under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7; https://lannuaire.service-public.gouv.fr/gouvernement/80515340-0ad9-4293-9348-35d7eb005fc7", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public finance: property-tax base and anti-fraud", "segment": "Tax administration" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Dussourt, G. (2024). Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros. RMC, 21 Mar 2024 (quoting minister Thomas Cazenave).", "title": "Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "date": "2024-03-21", "written_by": "independent", "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière" }, { "kind": "news report", "citation": "Ouest-France (2024). Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés. 21 Mar 2024.", "title": "Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "l’outil algorithmique introduit par la DGFiP en octobre 2021 avait permis la détection de 140 000 piscines, pour 40 millions d’euros de recettes supplémentaires" }, { "kind": "news report", "citation": "Ouest-France (2024). Same article, statements on verification and on error rates (Olivier Vilois, CGT Finances publiques).", "title": "Garages, vérandas, piscines… (verification and error rate)", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "D’après mes collègues sur le terrain, le seuil d’erreurs des algorithmes atteint 55 à 60% en ce moment" }, { "kind": "news report", "citation": "CNews (2024). Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA. 11 Jun 2024 (citing the DGFiP annual activity report via Ouest-France).", "title": "Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA", "url": "https://www.cnews.fr/france/2024-06-11/piscines-non-declarees-le-fisc-repere-140000-fraudeurs-grace-lia-1511264", "date": "2024-06-11", "written_by": "independent", "quote": "En 2023, cette technique a permis à la Direction générale des finances publiques d'identifier 140.000 piscines non déclarées, selon son rapport annuel d'activité publié par Ouest France." }, { "kind": "news report", "citation": "Bertrand, M. (2024). Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle. ici (France Bleu Périgord), 24 Mar 2024.", "title": "Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle", "url": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279", "date": "2024-03-24", "written_by": "independent", "quote": "Les finances publiques de Dordogne ont repéré quelque 1.500 piscines non déclarées au fisc en Dordogne, au cours de l'année 2023, sur les quelque 30.000 piscines du département." }, { "kind": "news report", "citation": "20 Minutes (2024). Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023. 12 Jun 2024.", "title": "Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023", "url": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023", "date": "2024-06-12", "written_by": "independent", "quote": "En 2022, 20.000 piscines non déclarées avaient ainsi été repérées pour un redressement estimé à 10 millions d’euros." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0335-source-0001", "AIBD-0335-source-0002", "AIBD-0335-source-0003", "AIBD-0335-source-0004", "AIBD-0335-source-0005", "AIBD-0335-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Dussourt, G. (2024). Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros. RMC, 21 Mar 2024 (quoting minister Thomas Cazenave).", "title": "Grâce à l'IA, l'Etat découvre 140.000 piscines non déclarées et récupère 40 millions d'euros", "url": "https://rmc.bfmtv.com/actualites/economie/grace-a-l-ia-l-etat-decouvre-140-000-piscines-non-declarees-et-recupere-40-millions-d-euros_AV-202403210525.html", "date": "2024-03-21", "written_by": "independent", "quote": "ce sont 140.000 piscines qui ont été détectées et qui permettront à nos collectivités de récolter 40 millions d’euros au titre de la taxe foncière" }, { "kind": "news report", "citation": "Ouest-France (2024). Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés. 21 Mar 2024.", "title": "Garages, vérandas, piscines… Comment le fisc repère les bâtiments non déclarés", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "l’outil algorithmique introduit par la DGFiP en octobre 2021 avait permis la détection de 140 000 piscines, pour 40 millions d’euros de recettes supplémentaires" }, { "kind": "news report", "citation": "Ouest-France (2024). Same article, statements on verification and on error rates (Olivier Vilois, CGT Finances publiques).", "title": "Garages, vérandas, piscines… (verification and error rate)", "url": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727", "date": "2024-03-21", "written_by": "independent", "quote": "D’après mes collègues sur le terrain, le seuil d’erreurs des algorithmes atteint 55 à 60% en ce moment" }, { "kind": "news report", "citation": "CNews (2024). Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA. 11 Jun 2024 (citing the DGFiP annual activity report via Ouest-France).", "title": "Piscines non déclarées : le fisc repère 140.000 fraudeurs grâce à l'IA", "url": "https://www.cnews.fr/france/2024-06-11/piscines-non-declarees-le-fisc-repere-140000-fraudeurs-grace-lia-1511264", "date": "2024-06-11", "written_by": "independent", "quote": "En 2023, cette technique a permis à la Direction générale des finances publiques d'identifier 140.000 piscines non déclarées, selon son rapport annuel d'activité publié par Ouest France." }, { "kind": "news report", "citation": "Bertrand, M. (2024). Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle. ici (France Bleu Périgord), 24 Mar 2024.", "title": "Le fisc découvre 1.500 piscines non-déclarées en Dordogne grâce à l'intelligence artificielle", "url": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279", "date": "2024-03-24", "written_by": "independent", "quote": "Les finances publiques de Dordogne ont repéré quelque 1.500 piscines non déclarées au fisc en Dordogne, au cours de l'année 2023, sur les quelque 30.000 piscines du département." }, { "kind": "news report", "citation": "20 Minutes (2024). Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023. 12 Jun 2024.", "title": "Taxe foncière : Grâce à l'IA, le fisc a repéré 140.000 piscines non déclarées en 2023", "url": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023", "date": "2024-06-12", "written_by": "independent", "quote": "En 2022, 20.000 piscines non déclarées avaient ainsi été repérées pour un redressement estimé à 10 millions d’euros." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Minister Cazenave (20 March 2024): DGFiP's AI tool detected 140,000 undeclared pools, c. €40m extra property tax for local authorities; Dordogne found 1,500 in 2023. No commune-level figure found.", "account": "On 20 March 2024 Thomas Cazenave, minister for public accounts, presented the first bilan of the government's anti-fraud plan at Bercy. He said the DGFiP's algorithmic tool, Foncier innovant, introduced in October 2021, had detected 140,000 undeclared swimming pools. The tool detects pools and buildings on IGN aerial photographs and compares them with declarations. An agent then checks each detection. That would let local authorities collect about €40 million in property tax. Owners received DGFiP letters asking them to regularize (RMC, Ouest-France, L'Indépendant, 21 March 2024). CNews (11 June 2024) cites the DGFiP annual activity report as reported by Ouest-France. It gives 140,000 pools identified in 2023 and €40 million that should have been paid in 2023. That compares with 20,000 pools in the 2022 experiment in nine départements (20 Minutes). L'Indépendant instead states 120,000 for 2022, so the press accounts of the yearly split disagree. At département level, ici (France Bleu) reported on 24 March 2024 on the Dordogne public-finance office. It had found about 1,500 undeclared pools in 2023 among some 30,000 in the département. It would send property-tax catch-up notices by the end of March. No official DGFiP or ministry document is among the sources, as the ministry's website was not available. No commune-level result was found." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Foncier innovant (DGFiP deep-learning detection of pools and buildings on IGN aerial photographs, built with Capgemini and Google)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Direction générale des Finances publiques (DGFiP) with Capgemini and Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "France's Foncier innovant aerial-image AI detects 140,000 undeclared pools and about €40 million in extra property tax by 20 March 2024; Dordogne alone 1,500 in 2023", "headline": "Foncier innovant finds 140,000 undeclared pools" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "87e51983bab417c733b46e4cfa086ffdca9be62d3de6113d831a444fa7d06335" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 20 March 2024 Thomas Cazenave, minister for public accounts, presented the first bilan of the government's anti-fraud plan at Bercy. He said the DGFiP's algorithmic tool, Foncier innovant, introduced in October 2021, had detected 140,000 undeclared swimming pools. The tool detects pools and buildings on IGN aerial photographs and compares them with declarations. An agent then checks each detection. That would let local authorities collect about €40 million in property tax. Owners received DGFiP letters asking them to regularize (RMC, Ouest-France, L'Indépendant, 21 March 2024). CNews (11 June 2024) cites the DGFiP annual activity report as reported by Ouest-France. It gives 140,000 pools identified in 2023 and €40 million that should have been paid in 2023. That compares with 20,000 pools in the 2022 experiment in nine départements (20 Minutes). L'Indépendant instead states 120,000 for 2022, so the press accounts of the yearly split disagree. At département level, ici (France Bleu) reported on 24 March 2024 on the Dordogne public-finance office. It had found about 1,500 undeclared pools in 2023 among some 30,000 in the département. It would send property-tax catch-up notices by the end of March. No official DGFiP or ministry document is among the sources, as the ministry's website was not available. No commune-level result was found.", "replication": { "produced_by": [ "IGN aerial photographs covering the whole territory", "A detection model built with Capgemini and Google, generalized nationally after the 2022 nine-département experiment", "Comparison of detected outlines with cadastral declarations", "Agent verification of each detection and letters inviting owners to regularize" ], "must_hold": [ "Recent, high-resolution aerial coverage", "A false-detection rate low enough for agents to verify (a union official cites 55–60% errors)", "Legal basis to add undeclared constructions to the cadastral value", "Local offices with capacity to send and handle catch-up notices" ], "echoes": [ { "date": "2022-12-31", "setting": "2022 experiment in nine départements: about 20,000 undeclared pools, about €10 million", "outcome": "recurred", "source": "https://www.20minutes.fr/economie/4095936-20240612-taxe-fonciere-grace-ia-fisc-repere-140-000-piscines-non-declarees-2023" }, { "date": "2024-03-24", "setting": "Dordogne département, 2023 campaign: about 1,500 undeclared pools, catch-up notices sent by end of March 2024", "outcome": "recurred", "source": "https://www.ici.fr/infos/societe/le-fisc-decouvre-1500-piscines-non-declarees-en-dordogne-grace-a-l-intelligence-artificielle-4899279" }, { "date": "2024-03-20", "setting": "Announced extension of the same tool from pools to undeclared buildings (garages, verandas); no outcome yet", "outcome": "unknown", "source": "https://www.ouest-france.fr/economie/budget/garages-verandas-piscines-comment-le-fisc-repere-les-batiments-non-declares-46394b0a-e780-11ee-95e5-de9a0852a727" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0227", "case_url": "records/AIBD-0227.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Detect visible firearms in existing security-camera feeds, verify with human analysts and alert police within seconds to reduce response times to potential shootings." }, { "label": "AI contribution", "value": "The chain (AI detection, human verification, dispatch) is described only in ZeroEyes' release and the vendor's later interview. The chief's endorsement is quoted in that release. No police report, court filing or independent reporter observation is among the sources. The AI's necessity (whether anyone else reported the shooting) is thus undocumented." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0227", "title": "ZeroEyes detection at Hobbs' Del Norte Pool lot leads police to people firing at clays near a street; one charged", "summary": "On 30 November 2023 ZeroEyes alerted Hobbs police to a gun on a camera at Del Norte Pool's lot; officers detained people shooting clays near a street and charged one, per the police chief.", "date_observed": "2023-11-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Odessa American (reproduced by GovTech) reports, from a ZeroEyes news release, on an alert at 6:35 p.m. MDT on 30 November 2023. ZeroEyes alerted the Hobbs Police Department and the Eagle I.C. dispatch center. A gun had been detected on a security camera in Del Norte Pool's parking lot. Those involved were shooting sporting clays close to North Grimes Street. Responding officers detained them and charges were filed against one. Hobbs Police Chief August Fons is quoted saying detection and rapid human verification helped stop the activity before anyone was hurt. Lubbock Lights later reported ZeroEyes co-founder Sam Alaimo showing images from the incident. He said it resulted in an arrest with no one hurt and listed the City of Hobbs and its school system among local clients. 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ZeroEyes alerted the Hobbs Police Department and the Eagle I.C. dispatch center. A gun had been detected on a security camera in Del Norte Pool's parking lot. Those involved were shooting sporting clays close to North Grimes Street. Responding officers detained them and charges were filed against one. Hobbs Police Chief August Fons is quoted saying detection and rapid human verification helped stop the activity before anyone was hurt. Lubbock Lights later reported ZeroEyes co-founder Sam Alaimo showing images from the incident. He said it resulted in an arrest with no one hurt and listed the City of Hobbs and its school system among local clients. 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ZeroEyes alerted the Hobbs Police Department and the Eagle I.C. dispatch center. A gun had been detected on a security camera in Del Norte Pool's parking lot. Those involved were shooting sporting clays close to North Grimes Street. Responding officers detained them and charges were filed against one. Hobbs Police Chief August Fons is quoted saying detection and rapid human verification helped stop the activity before anyone was hurt. Lubbock Lights later reported ZeroEyes co-founder Sam Alaimo showing images from the incident. He said it resulted in an arrest with no one hurt and listed the City of Hobbs and its school system among local clients. 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This standoff was that use." }, { "label": "AI contribution", "value": "The accounts describe a remotely deployed quadruped with a camera and an articulating arm. None says that any autonomous function made a decision in this incident. The robot contributed a camera view confirming the man was asleep and, per the LAPD's report, a physical barrier between him and the gun. The Monitor's “AI-powered” label rests on the Boston Dynamics platform, not on anything the system is reported to have decided." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0379", "title": "LAPD's Boston Dynamics Spot quadruped is sent onto a Hollywood Metro bus during a two-hour standoff with an armed man found asleep; he wakes, surrenders and is arrested, and the gun proves to be a BB gun", "summary": "LAPD deployed its Boston Dynamics Spot quadruped during a 8 November 2023 standoff with an armed man asleep on a Hollywood Metro bus; he woke, surrendered and was arrested; the gun was a BB gun.", "date_observed": "2023-11-08", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Just before 4 a.m. on 8 November 2023, a passenger on a Metro bus in Hollywood reported an armed man aboard. The bus was at Melrose and Western avenues. The driver pulled over and evacuated the other passengers. Officers got no response from the man, and a two-hour standoff followed (Los Angeles Times via Police1, 9 November 2023). The LAPD sent in its Boston Dynamics Spot quadruped. Its camera confirmed the man was asleep with a gun (Police1). The LAPD's quarterly report to the Police Commission says the robot was a barrier between suspect and firearm (NBC Los Angeles, 8 March 2024). The man woke, left the bus and was taken into custody about 6:40 a.m. The weapon was a BB gun (Police1). Deputy Chief David Kowalski approves each use. The robot cost nearly $300,000 (NBC). International Business Times' October 2024 account says the robot removed the gun.", "category": "protective response", "unexpected": false, "purpose": "Acquired, in Deputy Chief Kowalski's words, “to save lives” by putting a camera and an articulating arm into dangerous scenes ahead of officers. This standoff was that use.", "system": "Boston Dynamics Spot quadruped robot (LAPD 'Quadruped Unmanned Ground Vehicle') with camera and articulating arm", "model_version": null, "developer": "Boston Dynamics", "deployer": "Los Angeles Police Department", "location": "Los Angeles, United States", "domain": "Law enforcement / tactical operations", "segment": "Armed standoffs and barricaded suspects", "benefit_type": "Reduced Risk", "taxonomy_note": "Trustible's Reduced Violence covers a weapon found, but what the sources establish is that officers kept their distance while a camera confirmed a sleeping, apparently armed man and the gun proved to be a BB gun; the good is the lowered risk to officers and the public while the standoff ran.", "registry_ids": { "oecd_aim": "2024-10-02-1544" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The LAPD could confirm from the robot's camera that the man was asleep. By its own report, it kept the robot between him and the gun. The two-hour standoff ended in an arrest with no injury reported. A measurable result for the unit, with no counterfactual that officers would otherwise have been hurt." }, { "group": "personal", "depth": null, "reason": "The passengers had been evacuated before the robot was used, and the suspect was taken into custody unharmed. None of the reports ties either outcome to the robot rather than to the man waking and leaving the bus." } ], "ai_role": { "status": "assessed", "statement": "The accounts describe a remotely deployed quadruped with a camera and an articulating arm. None says that any autonomous function made a decision in this incident. The robot contributed a camera view confirming the man was asleep and, per the LAPD's report, a physical barrier between him and the gun. The Monitor's “AI-powered” label rests on the Boston Dynamics platform, not on anything the system is reported to have decided." }, "evidence": [ { "kind": "news report", "citation": "Goldberg, N., Los Angeles Times via Police1 (2023). LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus. Police1, November 9, 2023.", "title": "LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus", "url": "https://www.police1.com/police-robots/lapd-robot-dog-brings-peaceful-end-to-standoff-after-sniffing-out-armed-man-on-bus", "date": "2023-11-09", "written_by": "independent", "quote": "The guy eventually woke up, got off the bus and officers were able to talk to him and take him into custody for further questioning." }, { "kind": "news report", "citation": "NBC Los Angeles (2024). LAPD reports 2 more uses of its robotic dog. NBC Los Angeles, March 8, 2024.", "title": "LAPD reports 2 more uses of its robotic dog", "url": "https://www.nbclosangeles.com/investigations/lapd-reports-2-more-uses-of-its-robotic-dog/3359391/", "date": "2024-03-08", "written_by": "independent", "quote": "create a barrier between the suspect and the firearm" }, { "kind": "news report", "citation": "Urban, E. (2024). LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized. International Business Times, October 3, 2024.", "title": "LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized", "url": "https://www.ibtimes.com/lapd-robot-dog-spot-3745457", "date": "2024-10-03", "written_by": "independent", "quote": "The main reason that we acquired Spot is to save lives." }, { "kind": "other", "citation": "Los Angeles City Clerk (2023). Council File 22-1438, Quadruped Unmanned Ground Vehicle donation to the LAPD. Los Angeles City Clerk Connect, May 23, 2023.", "title": "Council File 22-1438", "url": "https://cityclerk.lacity.org/lacityclerkconnect/index.cfm?fa=ccfi.viewrecord&cfnumber=22-1438", "date": "2023-05-23", "written_by": "deployer", "quote": "Report from Board of Police Commissioners" }, { "kind": "registry label", "citation": "OECD.AI (2024). LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns. OECD AI Incidents and Hazards Monitor, 2024-10-02.", "title": "LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns", "url": "https://oecd.ai/en/incidents/2024-10-02-1544", "date": "2024-10-02", "written_by": "independent", "quote": "to safely disarm an armed suspect on a Metro bus, preventing injury" } ], "replication": { "produced_by": [ "An armed or apparently armed person unresponsive in a confined space that officers cannot safely approach", "A tactical unit with a camera-and-arm ground robot on call and a deputy chief's approval for each use" ], "must_hold": [ "Officers position the robot and watch its camera before any approach rather than acting on its own motion", "Each deployment is approved in advance and reported to the Police Commission and City Council" ], "echoes": [] }, "paired_incident": null, "headline": "Robot dog checks armed man on Hollywood bus; standoff ends in arrest", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-10-02-1544", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Boston_Dynamics; https://en.wikipedia.org/wiki/Los_Angeles_Police_Department", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports, a registry entry and the deployer's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0379", "catalog_version": "1.0.0", "source_fingerprint": "60b68178677d3993985f56f004eb346c79bb0003c8a8d5bd6b296ca89336d3d1", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0379-source-0001", "AIBD-0379-source-0002", "AIBD-0379-source-0003", "AIBD-0379-source-0004", "AIBD-0379-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Boston Dynamics", "deployer": "Los Angeles Police Department" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The accounts describe a remotely deployed quadruped with a camera and an articulating arm. None says that any autonomous function made a decision in this incident. The robot contributed a camera view confirming the man was asleep and, per the LAPD's report, a physical barrier between him and the gun. The Monitor's “AI-powered” label rests on the Boston Dynamics platform, not on anything the system is reported to have decided." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The accounts describe a remotely deployed quadruped with a camera and an articulating arm. None says that any autonomous function made a decision in this incident. The robot contributed a camera view confirming the man was asleep and, per the LAPD's report, a physical barrier between him and the gun. The Monitor's “AI-powered” label rests on the Boston Dynamics platform, not on anything the system is reported to have decided." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The LAPD could confirm from the robot's camera that the man was asleep. 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The bus was at Melrose and Western avenues. The driver pulled over and evacuated the other passengers. Officers got no response from the man, and a two-hour standoff followed (Los Angeles Times via Police1, 9 November 2023). The LAPD sent in its Boston Dynamics Spot quadruped. Its camera confirmed the man was asleep with a gun (Police1). The LAPD's quarterly report to the Police Commission says the robot was a barrier between suspect and firearm (NBC Los Angeles, 8 March 2024). The man woke, left the bus and was taken into custody about 6:40 a.m. The weapon was a BB gun (Police1). Deputy Chief David Kowalski approves each use. The robot cost nearly $300,000 (NBC). 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LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus. Police1, November 9, 2023.", "title": "LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus", "url": "https://www.police1.com/police-robots/lapd-robot-dog-brings-peaceful-end-to-standoff-after-sniffing-out-armed-man-on-bus", "date": "2023-11-09", "written_by": "independent", "quote": "The guy eventually woke up, got off the bus and officers were able to talk to him and take him into custody for further questioning." }, { "kind": "news report", "citation": "NBC Los Angeles (2024). LAPD reports 2 more uses of its robotic dog. NBC Los Angeles, March 8, 2024.", "title": "LAPD reports 2 more uses of its robotic dog", "url": "https://www.nbclosangeles.com/investigations/lapd-reports-2-more-uses-of-its-robotic-dog/3359391/", "date": "2024-03-08", "written_by": "independent", "quote": "create a barrier between the suspect and the firearm" }, { "kind": "news report", "citation": "Urban, E. (2024). LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized. International Business Times, October 3, 2024.", "title": "LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized", "url": "https://www.ibtimes.com/lapd-robot-dog-spot-3745457", "date": "2024-10-03", "written_by": "independent", "quote": "The main reason that we acquired Spot is to save lives." }, { "kind": "other", "citation": "Los Angeles City Clerk (2023). Council File 22-1438, Quadruped Unmanned Ground Vehicle donation to the LAPD. Los Angeles City Clerk Connect, May 23, 2023.", "title": "Council File 22-1438", "url": "https://cityclerk.lacity.org/lacityclerkconnect/index.cfm?fa=ccfi.viewrecord&cfnumber=22-1438", "date": "2023-05-23", "written_by": "deployer", "quote": "Report from Board of Police Commissioners" }, { "kind": "registry label", "citation": "OECD.AI (2024). LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns. OECD AI Incidents and Hazards Monitor, 2024-10-02.", "title": "LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns", "url": "https://oecd.ai/en/incidents/2024-10-02-1544", "date": "2024-10-02", "written_by": "independent", "quote": "to safely disarm an armed suspect on a Metro bus, preventing injury" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0379-source-0001", "AIBD-0379-source-0002", "AIBD-0379-source-0003", "AIBD-0379-source-0004", "AIBD-0379-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Goldberg, N., Los Angeles Times via Police1 (2023). LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus. Police1, November 9, 2023.", "title": "LAPD robot dog brings peaceful end to standoff after 'sniffing out' armed man on bus", "url": "https://www.police1.com/police-robots/lapd-robot-dog-brings-peaceful-end-to-standoff-after-sniffing-out-armed-man-on-bus", "date": "2023-11-09", "written_by": "independent", "quote": "The guy eventually woke up, got off the bus and officers were able to talk to him and take him into custody for further questioning." }, { "kind": "news report", "citation": "NBC Los Angeles (2024). LAPD reports 2 more uses of its robotic dog. NBC Los Angeles, March 8, 2024.", "title": "LAPD reports 2 more uses of its robotic dog", "url": "https://www.nbclosangeles.com/investigations/lapd-reports-2-more-uses-of-its-robotic-dog/3359391/", "date": "2024-03-08", "written_by": "independent", "quote": "create a barrier between the suspect and the firearm" }, { "kind": "news report", "citation": "Urban, E. (2024). LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized. International Business Times, October 3, 2024.", "title": "LAPD Defends Unleashing Robot Dog on City, Promises 'Spot' Will Never Be Weaponized", "url": "https://www.ibtimes.com/lapd-robot-dog-spot-3745457", "date": "2024-10-03", "written_by": "independent", "quote": "The main reason that we acquired Spot is to save lives." }, { "kind": "other", "citation": "Los Angeles City Clerk (2023). Council File 22-1438, Quadruped Unmanned Ground Vehicle donation to the LAPD. Los Angeles City Clerk Connect, May 23, 2023.", "title": "Council File 22-1438", "url": "https://cityclerk.lacity.org/lacityclerkconnect/index.cfm?fa=ccfi.viewrecord&cfnumber=22-1438", "date": "2023-05-23", "written_by": "deployer", "quote": "Report from Board of Police Commissioners" }, { "kind": "registry label", "citation": "OECD.AI (2024). LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns. OECD AI Incidents and Hazards Monitor, 2024-10-02.", "title": "LAPD Deploys AI Robot Dog 'Spot' to Disarm Armed Suspect, Raising Community Concerns", "url": "https://oecd.ai/en/incidents/2024-10-02-1544", "date": "2024-10-02", "written_by": "independent", "quote": "to safely disarm an armed suspect on a Metro bus, preventing injury" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "LAPD deployed its Boston Dynamics Spot quadruped during a 8 November 2023 standoff with an armed man asleep on a Hollywood Metro bus; he woke, surrendered and was arrested; the gun was a BB gun.", "account": "Just before 4 a.m. on 8 November 2023, a passenger on a Metro bus in Hollywood reported an armed man aboard. The bus was at Melrose and Western avenues. The driver pulled over and evacuated the other passengers. Officers got no response from the man, and a two-hour standoff followed (Los Angeles Times via Police1, 9 November 2023). The LAPD sent in its Boston Dynamics Spot quadruped. Its camera confirmed the man was asleep with a gun (Police1). The LAPD's quarterly report to the Police Commission says the robot was a barrier between suspect and firearm (NBC Los Angeles, 8 March 2024). The man woke, left the bus and was taken into custody about 6:40 a.m. The weapon was a BB gun (Police1). Deputy Chief David Kowalski approves each use. The robot cost nearly $300,000 (NBC). International Business Times' October 2024 account says the robot removed the gun." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Boston Dynamics Spot quadruped robot (LAPD 'Quadruped Unmanned Ground Vehicle') with camera and articulating arm" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Boston Dynamics" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "LAPD's Boston Dynamics Spot quadruped is sent onto a Hollywood Metro bus during a two-hour standoff with an armed man found asleep; he wakes, surrenders and is arrested, and the gun proves to be a BB gun", "headline": "Robot dog checks armed man on Hollywood bus; standoff ends in arrest" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "60b68178677d3993985f56f004eb346c79bb0003c8a8d5bd6b296ca89336d3d1" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Just before 4 a.m. on 8 November 2023, a passenger on a Metro bus in Hollywood reported an armed man aboard. The bus was at Melrose and Western avenues. The driver pulled over and evacuated the other passengers. Officers got no response from the man, and a two-hour standoff followed (Los Angeles Times via Police1, 9 November 2023). The LAPD sent in its Boston Dynamics Spot quadruped. Its camera confirmed the man was asleep with a gun (Police1). The LAPD's quarterly report to the Police Commission says the robot was a barrier between suspect and firearm (NBC Los Angeles, 8 March 2024). The man woke, left the bus and was taken into custody about 6:40 a.m. The weapon was a BB gun (Police1). Deputy Chief David Kowalski approves each use. The robot cost nearly $300,000 (NBC). International Business Times' October 2024 account says the robot removed the gun.", "replication": { "produced_by": [ "An armed or apparently armed person unresponsive in a confined space that officers cannot safely approach", "A tactical unit with a camera-and-arm ground robot on call and a deputy chief's approval for each use" ], "must_hold": [ "Officers position the robot and watch its camera before any approach rather than acting on its own motion", "Each deployment is approved in advance and reported to the Police Commission and City Council" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0120", "case_url": "records/AIBD-0120.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "The tool was built to spot smoke in ALERTCalifornia camera feeds and notify Cal Fire emergency command centers. Crews can then reach a fire in its incipient phase. This detection was that purpose." }, { "label": "AI contribution", "value": "The model's contribution was the detection. Its algorithm flagged a smoke column in a Cleveland National Forest camera feed at about 3 am. Human operators verified it before Cal Fire was alerted. The containment was done by Captain Yeary's crews. No source records whether a 911 call would have come later. The time gained is documented only for the program as a whole. TIME reports 77 fires identified before any 911 call in the first two months. UC San Diego's Neal Driscoll says the alerts beat 911 calls about 40 percent of the time. AFP also notes the system sometimes misreads dust, insects or weather as smoke. That is why the human verification step sits between the model and the dispatch." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0120", "title": "ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna; human operators verify it and Cal Fire holds the fire to under a quarter acre", "summary": "AI on Cleveland National Forest cameras flagged smoke at about 3 am on 5 August 2023; Cal Fire held the verified fire under a quarter acre.", "date_observed": "2023-08-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At about 3 am on 5 August 2023, the ALERTCalifornia AI flagged a column of smoke in the Cleveland National Forest near Mount Laguna. That is about 40 miles east of San Diego. The AI, a machine-learned model run by UC San Diego, watches live feeds from more than 1,000 cameras. Human operators verified the detection and alerted Cal Fire Captain Kris Yeary. His crews put it out at less than a quarter acre. Yeary told AFP that it could have become a devastating fire had the AI not alerted them. The AI watch over the feeds began at the end of June 2023 under a Cal Fire trial. By September 2023 the tool reached all 21 Cal Fire dispatch centers.", "category": "protective response", "unexpected": false, "purpose": "The tool was built to spot smoke in ALERTCalifornia camera feeds and notify Cal Fire emergency command centers. Crews can then reach a fire in its incipient phase. This detection was that purpose.", "system": "ALERTCalifornia AI wildfire detection (machine-learned smoke and anomaly detection on the ALERTCalifornia pan-tilt-zoom camera network)", "model_version": null, "developer": "ALERTCalifornia, University of California San Diego, with industry partner Digital Path", "deployer": "California Department of Forestry and Fire Protection (CAL FIRE)", "location": "Mount Laguna, California, United States", "domain": "Emergency services (wildfire detection and response)", "segment": "State fire agency using a university camera network", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2023-09-12-f6ac" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Mount Laguna station received a verified alert at about 3 am. Its crews extinguished the fire at under a quarter acre. Cal Fire intelligence specialist Suzann Leininger says the system “is getting us time to react in a faster manner”." }, { "group": "societal", "depth": 2, "reason": "Residents, property and forest around Mount Laguna were spared a larger fire. Captain Yeary's statement that it “could have been a devastating fire” is a judgment, not a proven counterfactual." } ], "ai_role": { "status": "assessed", "statement": "The model's contribution was the detection. Its algorithm flagged a smoke column in a Cleveland National Forest camera feed at about 3 am. Human operators verified it before Cal Fire was alerted. The containment was done by Captain Yeary's crews. No source records whether a 911 call would have come later. The time gained is documented only for the program as a whole. TIME reports 77 fires identified before any 911 call in the first two months. UC San Diego's Neal Driscoll says the alerts beat 911 calls about 40 percent of the time. AFP also notes the system sometimes misreads dust, insects or weather as smoke. That is why the human verification step sits between the model and the dispatch." }, "evidence": [ { "kind": "news report", "citation": "Fonsegrives R. / AFP via Phys.org (2023). California firefighters use AI to battle wildfires. AFP wire report.", "title": "California firefighters use AI to battle wildfires", "url": "https://phys.org/news/2023-09-california-firefighters-ai-wildfires.html", "date": "2023-09-12", "written_by": "independent", "quote": "Had the AI not alerted us to it, it could have gotten much bigger" }, { "kind": "news report", "citation": "AFP via Asharq Al-Awsat English (2023). California Firefighters Use AI to Battle Wildfires. Same AFP wire text (not independent of the Phys.org copy).", "title": "California Firefighters Use AI to Battle Wildfires", "url": "https://english.aawsat.com/node/4540976", "date": "2023-09-12", "written_by": "independent", "quote": "Human operators were able to verify the machines were correct and alerted Yeary" }, { "kind": "news report", "citation": "AFP via France 24 (2023). California firefighters use AI to battle wildfires. Same AFP wire text (not independent).", "title": "California firefighters use AI to battle wildfires", "url": "https://www.france24.com/en/live-news/20230912-california-firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "We beat 911 calls about 40 percent of the time. And it's going to get better." }, { "kind": "news report", "citation": "Fonsegrives R. / AFP via Oman Observer (2023). Firefighters use AI to battle wildfires. Same AFP wire text (not independent).", "title": "Firefighters use AI to battle wildfires", "url": "https://www.omanobserver.om/article/1142729/opinion/firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "It's getting us time to react in a faster manner." }, { "kind": "press release", "citation": "CAL FIRE and ALERTCalifornia (2023). CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial. Agency press release, Sacramento.", "title": "CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial", "url": "https://alertcalifornia.org/cal-fire-and-university-of-california-san-diegos-alertcalifornia-program-join-forces-to-enhance-wildfire-response-with-artificial-intelligence-implementation-trial/", "date": "2023-07-10", "written_by": "makers", "quote": "a machine-learned model has been created to accurately differentiate between smoke and other particles in the air" }, { "kind": "news report", "citation": "Dixit P. / TIME (2023). AlertCalifornia and Cal Fire AI Wildfire Detector. The Best Inventions of 2023.", "title": "AlertCalifornia and Cal Fire AI Wildfire Detector", "url": "https://time.com/collection/best-inventions-2023/6327137/alertcalifornia-ai-wildfire-detector/", "date": "2023-10-24", "written_by": "independent", "quote": "In the first two months, the system had correctly identified 77 fires before any 911 calls came in." }, { "kind": "press release", "citation": "Scully C. / UC San Diego Today (2023). ALERTCalifornia and CAL FIRE's Fire Detection AI Program Named One of TIME's Best Inventions of 2023. University news release.", "title": "ALERTCalifornia and CAL FIRE's Fire Detection AI Program Named One of TIME's Best Inventions of 2023", "url": "https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023", "date": "2023-10-24", "written_by": "makers", "quote": "The AI tool became available to all 21 CAL FIRE 911 Dispatch Centers in September 2023." }, { "kind": "news report", "citation": "Jennewein C. / Times of San Diego (2023). Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023. Describes a second AI-first detection near Grass Valley on 11 September 2023.", "title": "Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023", "url": "https://timesofsandiego.com/tech/2023/10/24/time-magazine-californias-ai-wildfire-detection-system-a-top-invention-of-2023/", "date": "2023-10-24", "written_by": "independent", "quote": "the first 911 call was not made until 6:01 a.m., and firefighters were already at the scene" }, { "kind": "press release", "citation": "ALERTCalifornia / UC San Diego (n.d.). Technology. Programme web page describing the AI detection workflow and watchstander vetting.", "title": "ALERTCalifornia: Technology", "url": "https://alertcalifornia.org/technology/", "date": null, "written_by": "makers", "quote": "If the incident is vetted and confirmed by trained watchstanders, firefighters respond quickly to extinguish the fire at the incipient phase." } ], "replication": { "produced_by": [ "A camera network with machine-learned smoke detection watching remote forest at night, when human spotting is weakest", "A verification step by trained operators that turns a model flag into a confirmed alert to the local Cal Fire station", "A fire still small at detection, with a crew close enough to reach it before it grows" ], "must_hold": [ "The detection must precede any human report for the time gained to be attributable to the model. For this fire no source documents the absence of a 911 call", "Human verification must be fast enough that the alert still arrives in the incipient phase", "False positives from dust, insects or weather must be screened out so that crews keep trusting the alerts" ], "echoes": [] }, "paired_incident": null, "headline": "ALERTCalifornia AI flags smoke; fire held under a quarter acre", "metric": "Cal Fire held the verified fire under a quarter acre", "comparator": { "against": "911 calls from the public as the first notice of a fire", "before": "not stated", "after": "the AI beats 911 calls about 40 percent of the time", "basis": "measured", "quote": "\"We beat 911 calls about 40 percent of the time. And it's going to get better.\"", "url": "https://english.aawsat.com/node/4540976" }, "later": { "status": "scaled", "date": "2023-09-01", "note": "After the summer trial the AI tool was rolled out to all 21 Cal Fire dispatch centers in September 2023 (day not given; first day of month used), and TIME named it one of the best inventions of 2023 on 24 October 2023.", "quote": "The AI tool became available to all 21 Cal Fire dispatch centers in September. The new tool is especially effective in spotting anomalies in remote locations and has proven effective at night, even alerting firefighters before 911 calls.", "url": "https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-09-12-f6ac", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://english.aawsat.com/node/4540976; https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://alertcalifornia.org/about/; https://en.wikipedia.org/wiki/California_Department_of_Forestry_and_Fire_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports and three press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0120", "catalog_version": "1.0.0", "source_fingerprint": "6a95bb51aedb168ccf10e609ab16860e9bcb15daf5287425833fc7fb98bb145b", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0120-source-0001", "AIBD-0120-source-0002", "AIBD-0120-source-0003", "AIBD-0120-source-0004", "AIBD-0120-source-0005", "AIBD-0120-source-0006", "AIBD-0120-source-0007", "AIBD-0120-source-0008", "AIBD-0120-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "ALERTCalifornia, University of California San Diego, with industry partner Digital Path", "deployer": "California Department of Forestry and Fire Protection (CAL FIRE)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The model's contribution was the detection. Its algorithm flagged a smoke column in a Cleveland National Forest camera feed at about 3 am. Human operators verified it before Cal Fire was alerted. The containment was done by Captain Yeary's crews. No source records whether a 911 call would have come later. The time gained is documented only for the program as a whole. TIME reports 77 fires identified before any 911 call in the first two months. UC San Diego's Neal Driscoll says the alerts beat 911 calls about 40 percent of the time. AFP also notes the system sometimes misreads dust, insects or weather as smoke. That is why the human verification step sits between the model and the dispatch." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "ALERTCalifornia flagged smoke in a camera feed at about 3 a.m. Operators verified the detection and notified Cal Fire, whose crews contained the fire.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The model's contribution was the detection. Its algorithm flagged a smoke column in a Cleveland National Forest camera feed at about 3 am. Human operators verified it before Cal Fire was alerted. The containment was done by Captain Yeary's crews. No source records whether a 911 call would have come later. The time gained is documented only for the program as a whole. TIME reports 77 fires identified before any 911 call in the first two months. UC San Diego's Neal Driscoll says the alerts beat 911 calls about 40 percent of the time. AFP also notes the system sometimes misreads dust, insects or weather as smoke. That is why the human verification step sits between the model and the dispatch." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Mount Laguna station received a verified alert at about 3 am. Its crews extinguished the fire at under a quarter acre. Cal Fire intelligence specialist Suzann Leininger says the system “is getting us time to react in a faster manner”." }, { "group": "societal", "depth": 2, "reason": "Residents, property and forest around Mount Laguna were spared a larger fire. Captain Yeary's statement that it “could have been a devastating fire” is a judgment, not a proven counterfactual." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: six independent news reports and three press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Mount Laguna station received a verified alert at about 3 am. Its crews extinguished the fire at under a quarter acre. Cal Fire intelligence specialist Suzann Leininger says the system “is getting us time to react in a faster manner”." }, { "group": "societal", "depth": 2, "reason": "Residents, property and forest around Mount Laguna were spared a larger fire. Captain Yeary's statement that it “could have been a devastating fire” is a judgment, not a proven counterfactual." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "Cal Fire's Mount Laguna station received a verified alert at about 3 am. Its crews extinguished the fire at under a quarter acre. Cal Fire intelligence specialist Suzann Leininger says the system “is getting us time to react in a faster manner”." }, { "group": "societal", "depth": 2, "reason": "Residents, property and forest around Mount Laguna were spared a larger fire. Captain Yeary's statement that it “could have been a devastating fire” is a judgment, not a proven counterfactual." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, 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That is about 40 miles east of San Diego. The AI, a machine-learned model run by UC San Diego, watches live feeds from more than 1,000 cameras. Human operators verified the detection and alerted Cal Fire Captain Kris Yeary. His crews put it out at less than a quarter acre. Yeary told AFP that it could have become a devastating fire had the AI not alerted them. The AI watch over the feeds began at the end of June 2023 under a Cal Fire trial. 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For this fire no source documents the absence of a 911 call", "Human verification must be fast enough that the alert still arrives in the incipient phase", "False positives from dust, insects or weather must be screened out so that crews keep trusting the alerts" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-09-12-f6ac", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://english.aawsat.com/node/4540976; https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://alertcalifornia.org/about/; https://en.wikipedia.org/wiki/California_Department_of_Forestry_and_Fire_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Emergency services (wildfire detection and response)", "segment": "State fire agency using a university camera network" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Fonsegrives R. / AFP via Phys.org (2023). 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Same AFP wire text (not independent).", "title": "California firefighters use AI to battle wildfires", "url": "https://www.france24.com/en/live-news/20230912-california-firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "We beat 911 calls about 40 percent of the time. And it's going to get better." }, { "kind": "news report", "citation": "Fonsegrives R. / AFP via Oman Observer (2023). Firefighters use AI to battle wildfires. Same AFP wire text (not independent).", "title": "Firefighters use AI to battle wildfires", "url": "https://www.omanobserver.om/article/1142729/opinion/firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "It's getting us time to react in a faster manner." }, { "kind": "press release", "citation": "CAL FIRE and ALERTCalifornia (2023). CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial. Agency press release, Sacramento.", "title": "CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial", "url": "https://alertcalifornia.org/cal-fire-and-university-of-california-san-diegos-alertcalifornia-program-join-forces-to-enhance-wildfire-response-with-artificial-intelligence-implementation-trial/", "date": "2023-07-10", "written_by": "makers", "quote": "a machine-learned model has been created to accurately differentiate between smoke and other particles in the air" }, { "kind": "news report", "citation": "Dixit P. / TIME (2023). AlertCalifornia and Cal Fire AI Wildfire Detector. 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University news release.", "title": "ALERTCalifornia and CAL FIRE's Fire Detection AI Program Named One of TIME's Best Inventions of 2023", "url": "https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023", "date": "2023-10-24", "written_by": "makers", "quote": "The AI tool became available to all 21 CAL FIRE 911 Dispatch Centers in September 2023." }, { "kind": "news report", "citation": "Jennewein C. / Times of San Diego (2023). Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023. Describes a second AI-first detection near Grass Valley on 11 September 2023.", "title": "Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023", "url": "https://timesofsandiego.com/tech/2023/10/24/time-magazine-californias-ai-wildfire-detection-system-a-top-invention-of-2023/", "date": "2023-10-24", "written_by": "independent", "quote": "the first 911 call was not made until 6:01 a.m., and firefighters were already at the scene" }, { "kind": "press release", "citation": "ALERTCalifornia / UC San Diego (n.d.). Technology. 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Same AFP wire text (not independent).", "title": "California firefighters use AI to battle wildfires", "url": "https://www.france24.com/en/live-news/20230912-california-firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "We beat 911 calls about 40 percent of the time. And it's going to get better." }, { "kind": "news report", "citation": "Fonsegrives R. / AFP via Oman Observer (2023). Firefighters use AI to battle wildfires. Same AFP wire text (not independent).", "title": "Firefighters use AI to battle wildfires", "url": "https://www.omanobserver.om/article/1142729/opinion/firefighters-use-ai-to-battle-wildfires", "date": "2023-09-12", "written_by": "independent", "quote": "It's getting us time to react in a faster manner." }, { "kind": "press release", "citation": "CAL FIRE and ALERTCalifornia (2023). CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial. Agency press release, Sacramento.", "title": "CAL FIRE and University of California San Diego's ALERTCalifornia Program Join Forces to Enhance Wildfire Response with Artificial Intelligence Implementation Trial", "url": "https://alertcalifornia.org/cal-fire-and-university-of-california-san-diegos-alertcalifornia-program-join-forces-to-enhance-wildfire-response-with-artificial-intelligence-implementation-trial/", "date": "2023-07-10", "written_by": "makers", "quote": "a machine-learned model has been created to accurately differentiate between smoke and other particles in the air" }, { "kind": "news report", "citation": "Dixit P. / TIME (2023). AlertCalifornia and Cal Fire AI Wildfire Detector. The Best Inventions of 2023.", "title": "AlertCalifornia and Cal Fire AI Wildfire Detector", "url": "https://time.com/collection/best-inventions-2023/6327137/alertcalifornia-ai-wildfire-detector/", "date": "2023-10-24", "written_by": "independent", "quote": "In the first two months, the system had correctly identified 77 fires before any 911 calls came in." }, { "kind": "press release", "citation": "Scully C. / UC San Diego Today (2023). ALERTCalifornia and CAL FIRE's Fire Detection AI Program Named One of TIME's Best Inventions of 2023. University news release.", "title": "ALERTCalifornia and CAL FIRE's Fire Detection AI Program Named One of TIME's Best Inventions of 2023", "url": "https://today.ucsd.edu/story/alertcalifornia-and-cal-fires-fire-detection-ai-program-named-one-of-times-best-inventions-of-2023", "date": "2023-10-24", "written_by": "makers", "quote": "The AI tool became available to all 21 CAL FIRE 911 Dispatch Centers in September 2023." }, { "kind": "news report", "citation": "Jennewein C. / Times of San Diego (2023). Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023. Describes a second AI-first detection near Grass Valley on 11 September 2023.", "title": "Time Magazine: California's AI Wildfire Detection System a Top Invention of 2023", "url": "https://timesofsandiego.com/tech/2023/10/24/time-magazine-californias-ai-wildfire-detection-system-a-top-invention-of-2023/", "date": "2023-10-24", "written_by": "independent", "quote": "the first 911 call was not made until 6:01 a.m., and firefighters were already at the scene" }, { "kind": "press release", "citation": "ALERTCalifornia / UC San Diego (n.d.). Technology. Programme web page describing the AI detection workflow and watchstander vetting.", "title": "ALERTCalifornia: Technology", "url": "https://alertcalifornia.org/technology/", "written_by": "makers", "quote": "If the incident is vetted and confirmed by trained watchstanders, firefighters respond quickly to extinguish the fire at the incipient phase." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AI on Cleveland National Forest cameras flagged smoke at about 3 am on 5 August 2023; Cal Fire held the verified fire under a quarter acre.", "account": "At about 3 am on 5 August 2023, the ALERTCalifornia AI flagged a column of smoke in the Cleveland National Forest near Mount Laguna. That is about 40 miles east of San Diego. The AI, a machine-learned model run by UC San Diego, watches live feeds from more than 1,000 cameras. Human operators verified the detection and alerted Cal Fire Captain Kris Yeary. His crews put it out at less than a quarter acre. Yeary told AFP that it could have become a devastating fire had the AI not alerted them. The AI watch over the feeds began at the end of June 2023 under a Cal Fire trial. By September 2023 the tool reached all 21 Cal Fire dispatch centers." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ALERTCalifornia AI wildfire detection (machine-learned smoke and anomaly detection on the ALERTCalifornia pan-tilt-zoom camera network)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "ALERTCalifornia, University of California San Diego, with industry partner Digital Path" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna; human operators verify it and Cal Fire holds the fire to under a quarter acre", "headline": "ALERTCalifornia AI flags smoke; fire held under a quarter acre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "6a95bb51aedb168ccf10e609ab16860e9bcb15daf5287425833fc7fb98bb145b" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At about 3 am on 5 August 2023, the ALERTCalifornia AI flagged a column of smoke in the Cleveland National Forest near Mount Laguna. That is about 40 miles east of San Diego. The AI, a machine-learned model run by UC San Diego, watches live feeds from more than 1,000 cameras. Human operators verified the detection and alerted Cal Fire Captain Kris Yeary. His crews put it out at less than a quarter acre. Yeary told AFP that it could have become a devastating fire had the AI not alerted them. The AI watch over the feeds began at the end of June 2023 under a Cal Fire trial. By September 2023 the tool reached all 21 Cal Fire dispatch centers.", "replication": { "produced_by": [ "A camera network with machine-learned smoke detection watching remote forest at night, when human spotting is weakest", "A verification step by trained operators that turns a model flag into a confirmed alert to the local Cal Fire station", "A fire still small at detection, with a crew close enough to reach it before it grows" ], "must_hold": [ "The detection must precede any human report for the time gained to be attributable to the model. For this fire no source documents the absence of a 911 call", "Human verification must be fast enough that the alert still arrives in the incipient phase", "False positives from dust, insects or weather must be screened out so that crews keep trusting the alerts" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0346", "case_url": "records/AIBD-0346.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Rapid building-damage assessment from pre- and post-event satellite imagery for humanitarian assistance and disaster response. This use was that purpose." }, { "label": "AI contribution", "value": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0346", "title": "xView2 machine-learning damage maps may have been used by UN INSARAG teams in Adıyaman and by AFAD, the World Bank, IFRC and WFP after the 6 February 2023 Türkiye earthquakes", "summary": "MIT Technology Review (20 February 2023), quoting DIU: xView2 used by two UN INSARAG teams in Adıyaman and by AFAD, World Bank, IFRC and WFP; Microsoft reports 3,849 damaged buildings mapped for AFAD.", "date_observed": "2023-02-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "category": "protective response", "unexpected": false, "purpose": "Rapid building-damage assessment from pre- and post-event satellite imagery for humanitarian assistance and disaster response. This use was that purpose.", "system": "xView2 building damage assessment models (semantic segmentation on satellite imagery, trained on the xBD dataset)", "model_version": null, "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)", "location": "Adıyaman and four cities in southeast Türkiye including Kahramanmaraş (Marash)", "domain": "Disaster response / post-earthquake damage assessment", "segment": "Humanitarian and civil-protection organizations", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The responder-use claim rests on the developer's account relayed by one news item and Microsoft's own report.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. The reports come from the developer and a World Bank specialist, not the responding agencies." }, { "group": "societal", "depth": 1, "reason": "No downstream effect on rescues, shelter siting or reconstruction is documented. The maps covered part of the affected zone after imagery became usable on 9 February." } ], "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." }, "evidence": [ { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ], "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "date": null, "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "model_version": null, "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "model_version": null, "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] }, "paired_incident": null, "headline": "UN INSARAG teams in Adıyaman may have used xView2 damage maps", "metric": "estimating 3,849 buildings damaged or destroyed", "comparator": { "against": "conventional damage assessment", "before": "weeks", "after": "hours or minutes", "basis": "assumed", "quote": "this same degree of assessment would typically take weeks and now takes hours or minutes", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, "later": { "status": "unknown", "date": null, "note": "Direct Relief's 10 December 2025 article describes a retrospective research study of the Microsoft and Google 2023 models that found they disagreed in significant ways; no later operational use of xView2 was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Switzerland", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Defense_Innovation_Unit; https://en.wikipedia.org/wiki/International_Search_and_Rescue_Advisory_Group", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UN INSARAG.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the makers' report and the makers' preprint. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0346", "catalog_version": "1.0.0", "source_fingerprint": "0552f28b520cecd7eb1ac0a7189af9fb84ad3f2ef360d27b88bb995479c5f0d7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0346-source-0001", "AIBD-0346-source-0002", "AIBD-0346-source-0003", "AIBD-0346-source-0004", "AIBD-0346-source-0005", "AIBD-0346-source-0006", "AIBD-0346-source-0007", "AIBD-0346-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. 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It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. 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(2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0346-source-0001", "AIBD-0346-source-0002", "AIBD-0346-source-0003", "AIBD-0346-source-0004", "AIBD-0346-source-0005", "AIBD-0346-source-0006", "AIBD-0346-source-0007", "AIBD-0346-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MIT Technology Review (20 February 2023), quoting DIU: xView2 used by two UN INSARAG teams in Adıyaman and by AFAD, World Bank, IFRC and WFP; Microsoft reports 3,849 damaged buildings mapped for AFAD.", "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "xView2 building damage assessment models (semantic segmentation on satellite imagery, trained on the xBD dataset)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "xView2 machine-learning damage maps may have been used by UN INSARAG teams in Adıyaman and by AFAD, the World Bank, IFRC and WFP after the 6 February 2023 Türkiye earthquakes", "headline": "UN INSARAG teams in Adıyaman may have used xView2 damage maps" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0552f28b520cecd7eb1ac0a7189af9fb84ad3f2ef360d27b88bb995479c5f0d7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0368", "case_url": "records/AIBD-0368.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Instagram's self-harm detection was built to flag content indicating self-harm or suicide risk and route alerts to agencies that can intervene. An alert that brought police to a girl posting self-harm videos was that purpose." }, { "label": "AI contribution", "value": "Police say the alert came by email from Instagram's US office after the videos were posted. An ASP described the platform's AI algorithms as flagging non-graphic self-harm content such as scars, search words and hashtags. No source says whether a classifier or a user report started the alert, or whether Instagram staff reviewed it. Everything after the email was human work. Bhopal and Indore technical teams traced the location in about an hour. Satna police found and counseled her." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0368", "title": "Instagram self-harm alert to Madhya Pradesh cyber police leads Satna officers to a 17-year-old within about an hour; she is counseled and sent home (3 November 2022)", "summary": "3 November 2022, about 5 pm: Instagram emailed MP cyber police a self-harm alert; Bhopal and Indore teams traced a 17-year-old to Satna in about an hour; she was counseled and sent home.", "date_observed": "2022-11-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India).", "category": "protective response", "unexpected": false, "purpose": "Instagram's self-harm detection was built to flag content indicating self-harm or suicide risk and route alerts to agencies that can intervene. An alert that brought police to a girl posting self-harm videos was that purpose.", "system": "Instagram self-harm content detection and alert routing", "model_version": null, "developer": "Meta Platforms (Instagram)", "deployer": "Instagram; alert acted on by the Madhya Pradesh State Cyber Police (Bhopal headquarters and Indore unit) and Satna Police", "location": "Satna, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "The good is a welfare check and counseling for a teen flagged for self-harm, with only a minor injury documented.", "registry_ids": { "oecd_aim": "2022-11-04-76c5" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "A 17-year-old had posted videos with poison and an apparent cut. She was reached and counseled within about an hour of the alert and sent home with a small cut on her wrist. By her own account she wanted to scare her boyfriend. An averted suicide rests on the police's framing and is not established." } ], "ai_role": { "status": "assessed", "statement": "Police say the alert came by email from Instagram's US office after the videos were posted. An ASP described the platform's AI algorithms as flagging non-graphic self-harm content such as scars, search words and hashtags. No source says whether a classifier or a user report started the alert, or whether Instagram staff reviewed it. Everything after the email was human work. Bhopal and Indore technical teams traced the location in about an hour. Satna police found and counseled her." }, "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "date": null, "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ], "replication": { "produced_by": [ "Instagram's self-harm detection and alert routing, which per the police flags non-graphic self-harm content and emails state cyber police with account information", "A state cyber police unit (Bhopal headquarters and Indore unit) able to trace the account's location from the emailed information, here in about an hour", "District police able to reach the person and hold a counseling session with a parent present" ], "must_hold": [ "The account's IP or other details resolve to a reachable district", "The person is found before any act. Here she was found with a small cut on her wrist", "A standing channel exists between Instagram and the state cyber police. This was the unit's second such alert" ], "echoes": [ { "date": null, "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "model_version": null, "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] }, "paired_incident": null, "headline": "Instagram self-harm alert brings Satna police to a 17-year-old", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-11-04-76c5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. 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This was the unit's second such alert" ], "echoes": [ { "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-11-04-76c5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0368-source-0001", "AIBD-0368-source-0002", "AIBD-0368-source-0003", "AIBD-0368-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "TNN (2022). Instagram alert helps cops prevent a suicide in Madhya Pradesh. The Times of India (Bhopal), 4 November 2022.", "title": "Instagram alert helps cops prevent a suicide in Madhya Pradesh", "url": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms", "date": "2022-11-04", "written_by": "independent", "quote": "She told us that she only wanted to scare her boyfriend as he was not talking to her for five days. She was sent home after a session of counseling" }, { "kind": "news report", "citation": "ANI (2022). Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna. Asian News International, 5 November 2022.", "title": "Madhya Pradesh: Instagram's timely alert to cyber police helps save minor girl's life in Satna", "url": "https://www.aninews.in/news/national/general-news/madhya-pradesh-instagrams-timely-alert-to-cyber-police-helps-save-minor-girls-life-in-satna20221105161736", "date": "2022-11-05", "written_by": "independent", "quote": "After an effort of one hour, we traced the location of the girl. We find that she is a resident of the Satna district of Madhya Pradesh" }, { "kind": "news report", "citation": "Team Latestly (2022). Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life. LatestLY, 4 November 2022.", "title": "Madhya Pradesh: Instagram Timely Alert to Indore Cyber Cops Saves Satna Girl From Ending Life", "url": "https://www.latestly.com/india/news/madhya-pradesh-instagram-timely-alert-to-indore-cyber-cops-saves-satna-girl-from-ending-life-4417111.html", "date": "2022-11-04", "written_by": "independent", "quote": "AI algorithms on Instagram don't allow any graphic images of self-harm, and any non-graphic, self-harm related content, such as scars, or search words and hashtags, trigger alerts" }, { "kind": "registry label", "citation": "OECD.AI (2022). Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh. OECD AI Incidents and Hazards Monitor, entry 2022-11-04-76c5.", "title": "Instagram AI Alert Enables Police to Prevent Teen Suicide in Madhya Pradesh", "url": "https://oecd.ai/en/incidents/2022-11-04-76c5", "written_by": "independent", "quote": "Instagram's AI system detected self-harm content posted by a 17-year-old girl in Satna, Madhya Pradesh, and sent an alert to cyber police." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "3 November 2022, about 5 pm: Instagram emailed MP cyber police a self-harm alert; Bhopal and Indore teams traced a 17-year-old to Satna in about an hour; she was counseled and sent home.", "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India)." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Instagram self-harm content detection and alert routing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms (Instagram)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Instagram self-harm alert to Madhya Pradesh cyber police leads Satna officers to a 17-year-old within about an hour; she is counseled and sent home (3 November 2022)", "headline": "Instagram self-harm alert brings Satna police to a 17-year-old" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "df724059f4e078b0baf3ddb1b276f72659cf7cc9167fb308345002ee63116233" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 3 November 2022 at about 5 pm, Madhya Pradesh's state cyber police received an alert emailed by Instagram's US office. It said a user in the state had suffered a body injury. The Indore cyber-crime unit shared the “self-harm” alert with the cyber headquarters in Bhopal (ANI, 5 November 2022; Times of India, 4 November 2022). The girl, 17, had uploaded two videos. In one, a packet of poison lay beside her as she acted as if drunk. In the other she appeared to cut a vein (ANI). The Times of India notes a small cut on her wrist. State Cyber SP Vaibhav Shrivastava said: “After an effort of one hour, we traced the location of the girl”. She was in Satna district, whose police were informed (ANI). Satna SP Ashutosh Gupta said officers located her and called her in with her mother. She said she only wanted to scare her boyfriend and was sent home after counseling (Times of India).", "replication": { "produced_by": [ "Instagram's self-harm detection and alert routing, which per the police flags non-graphic self-harm content and emails state cyber police with account information", "A state cyber police unit (Bhopal headquarters and Indore unit) able to trace the account's location from the emailed information, here in about an hour", "District police able to reach the person and hold a counseling session with a parent present" ], "must_hold": [ "The account's IP or other details resolve to a reachable district", "The person is found before any act. Here she was found with a small cut on her wrist", "A standing channel exists between Instagram and the state cyber police. This was the unit's second such alert" ], "echoes": [ { "setting": "Chhattisgarh, earlier: per ASP Vaibhav Srivastava, a previous Instagram “suicide” alert to the Madhya Pradesh cyber cell led to a girl being traced to Chhattisgarh and saved by local police", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhopal/instagram-alert-helps-cops-prevent-a-suicide-in-madhya-pradesh/articleshow/95288119.cms" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0367", "case_url": "records/AIBD-0367.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose." }, { "label": "AI contribution", "value": "The reports describe DAR as software that links entries across the weapons register, residents' registration data, the police case system and the wanted-persons portal. None mentions machine learning. The minister himself describes plain linking, while the OECD AI Incidents Monitor calls it an AI system. On the ministry's account the software's contribution was speed of identification. That was an email address in fractions of a second and about 20 minutes per shooting participant. Whether manual queries would have reached the same people more slowly is not addressed. The claims come from the deploying ministry and an unnamed investigator." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Germany" ] }, "record": { "id": "AIBD-0367", "title": "The Palantir-based DAR software identifies the author of an emailed amok threat against Siegen's city administration in fractions of a second and 51 of about 100 participants in a Duisburg shooting (28 October 2022)", "summary": "NRW Interior Ministry, 28 Oct 2022: Palantir-based DAR resolved an amok-threat email against Siegen's city administration in seconds and identified 51 of about 100 people at a Duisburg clan shooting.", "date_observed": "2022-10-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "category": "benefit created", "unexpected": false, "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose.", "system": "DAR (Datenbankübergreifende Recherche und Analyse), Palantir software used by North Rhine-Westphalia police", "model_version": null, "developer": "Palantir Technologies", "deployer": "North Rhine-Westphalia police, under the NRW Ministry of the Interior (contract since 2019)", "location": "Siegen, Germany", "domain": "Policing / criminal investigation", "segment": "Cross-database analysis for state police", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2022-10-28-8649" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "NRW police resolved an amok-threat email address to a person in fractions of a second. They identified 51 of about 100 shooting participants at about 20 minutes each, per the ministry and a Duisburg investigator. No comparison with conventional methods and no charges or convictions are reported." }, { "group": "societal", "depth": null, "reason": "Siegen's administration and public had the author of a threatened amok attack identified. The sources do not say whether the threat was credible, whether the person was charged, or what harm was averted." } ], "ai_role": { "status": "assessed", "statement": "The reports describe DAR as software that links entries across the weapons register, residents' registration data, the police case system and the wanted-persons portal. None mentions machine learning. The minister himself describes plain linking, while the OECD AI Incidents Monitor calls it an AI system. On the ministry's account the software's contribution was speed of identification. That was an email address in fractions of a second and about 20 minutes per shooting participant. Whether manual queries would have reached the same people more slowly is not addressed. The claims come from the deploying ministry and an unnamed investigator." }, "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "date": null, "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ], "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "date": null, "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "model_version": null, "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "date": null, "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "model_version": null, "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] }, "paired_incident": null, "headline": "Palantir-based DAR identifies the author of a Siegen amok threat", "metric": "51 of the roughly 100 participants", "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "Germany", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-10-28-8649", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Palantir_Technologies; https://de.wikipedia.org/wiki/Polizei_Nordrhein-Westfalen", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0367", "catalog_version": "1.0.0", "source_fingerprint": "f8bb557bc7f642dcd2c31905ee08cd079662a321cced3883eaae0125429f4a68", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0367-source-0001", "AIBD-0367-source-0002", "AIBD-0367-source-0003", "AIBD-0367-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Palantir Technologies", "deployer": "North Rhine-Westphalia police, under the NRW Ministry of the Interior (contract since 2019)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The reports describe DAR as software that links entries across the weapons register, residents' registration data, the police case system and the wanted-persons portal. 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In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "NRW police resolved an amok-threat email address to a person in fractions of a second. They identified 51 of about 100 shooting participants at about 20 minutes each, per the ministry and a Duisburg investigator. No comparison with conventional methods and no charges or convictions are reported." }, { "group": "societal", "reason": "Siegen's administration and public had the author of a threatened amok attack identified. The sources do not say whether the threat was credible, whether the person was charged, or what harm was averted." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. Identifying suspects from an email address or from a nickname and phone number was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Siegen, Germany", "developer_country": "United States", "deployer_country": "Germany" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "DAR was procured to let North Rhine-Westphalia police search and link entries across police and administrative databases during investigations. 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The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-10-28-8649", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Palantir_Technologies; https://de.wikipedia.org/wiki/Polizei_Nordrhein-Westfalen", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Policing / criminal investigation", "segment": "Cross-database analysis for state police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0367-source-0001", "AIBD-0367-source-0002", "AIBD-0367-source-0003", "AIBD-0367-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "WELT (2022). Ministerium: «Palantir» klärte unter anderem Amokdrohung auf. WELT, 28 October 2022.", "title": "Ministerium: «Palantir» klärte unter anderem Amokdrohung auf", "url": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die E-Mail-Adresse habe via «Palantir» in Sekundenbruchteilen die verdächtige Person enthüllt, hieß es aus dem Innenministerium." }, { "kind": "news report", "citation": "Corman T, Triesch A (2022). Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf. RP Online, 28 October 2022.", "title": "Düsseldorf: „Palantir“ klärt Amokdrohung und Schießerei in Duisburg auf", "url": "https://rp-online.de/nrw/panorama/duesseldorf-palantir-klaert-amokdrohung-und-schiesserei-in-duisburg-auf_aid-79054087", "date": "2022-10-28", "written_by": "independent", "quote": "Wie ein Duisburger Ermittler sagte, konnte man schnell 51 der rund 100 Beteiligten identifizieren." }, { "kind": "news report", "citation": "Westfalenpost (2022). Siegen: Amokdrohung gegen Stadtverwaltung. WP.de, 28 October 2022.", "title": "Siegen: Amokdrohung gegen Stadtverwaltung", "url": "https://www.wp.de/staedte/siegerland/article236775413/Siegen-Amokdrohung-gegen-Stadtverwaltung.html", "date": "2022-10-28", "written_by": "independent", "quote": "Die Stadtverwaltung gerät in den Fokus eines Verdächtigen. Er schickt eine Amokdrohung." }, { "kind": "registry label", "citation": "OECD.AI (2022). AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations. OECD AI Incidents and Hazards Monitor, entry 2022-10-28-8649.", "title": "AI System 'Palantir' Enables Rapid Suspect Identification in NRW Police Investigations", "url": "https://oecd.ai/en/incidents/2022-10-28-8649", "written_by": "independent", "quote": "a mass shooting in Duisburg and an emailed amok threat to Siegen city administration" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "NRW Interior Ministry, 28 Oct 2022: Palantir-based DAR resolved an amok-threat email against Siegen's city administration in seconds and identified 51 of about 100 people at a Duisburg clan shooting.", "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT)." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "DAR (Datenbankübergreifende Recherche und Analyse), Palantir software used by North Rhine-Westphalia police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Palantir Technologies" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "The Palantir-based DAR software identifies the author of an emailed amok threat against Siegen's city administration in fractions of a second and 51 of about 100 participants in a Duisburg shooting (28 October 2022)", "headline": "Palantir-based DAR identifies the author of a Siegen amok threat" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f8bb557bc7f642dcd2c31905ee08cd079662a321cced3883eaae0125429f4a68" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 28 October 2022 North Rhine-Westphalia's (NRW) Interior Ministry said DAR, the police's Palantir software, was central in several recent cases (WELT; RP Online). In October someone had emailed the Siegen city administration announcing an amok attack. The threat was over the city's “topless” permission for swimming pools. The ministry said the email address revealed the suspect via Palantir in fractions of a second. A shooting in Duisburg-Hamborn between a Turkish-Arab clan and the Hells Angels injured four people. A Duisburg investigator said police quickly identified 51 of the roughly 100 participants, about 20 minutes each (RP Online; WELT). 49 of them were assigned to rival groups. Interior Minister Herbert Reul addressed the Landtag's interior committee on 27 October. He said DAR links the weapons register, residents' registration, the police case system and the wanted-persons portal. The ministry puts the 2019–2025 cost at about 39 million euros. The opposition attacks it on data-protection and cost grounds (WELT).", "replication": { "produced_by": [ "Police and administrative databases (weapons register, residents' registration, case-processing system, wanted-persons portal) linked in one Palantir-based search", "An identifier from the case, such as an email address, a nickname or a phone number, that matches an entry somewhere in the linked data" ], "must_hold": [ "The person already appears in one of the linked databases. The system links records and does not generate new ones", "Investigators have lawful access to the linked registers. The opposition contests the arrangement on data-protection grounds", "Licensing continues. The ministry puts the 2019–2025 cost at about 39 million euros" ], "echoes": [ { "setting": "August 2022, North Rhine-Westphalia: per the ministry, DAR led from one burglar to a whole gang", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" }, { "setting": "August 2022, North Rhine-Westphalia: DAR identified a man suspected of abusing a 13-year-old girl from only a nickname and a phone number with fake owner data", "outcome": "recurred", "source": "https://www.welt.de/regionales/nrw/article241830977/Ministerium-Palantir-klaerte-unter-anderem-Amokdrohung-auf.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0369", "case_url": "records/AIBD-0369.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Carscoops describes Rekor's Public Safety Network as aggregating vehicle location data from its customers so police can examine driving patterns. Flagging a pattern associated with drug trafficking for a stop is that function, not a side effect." }, { "label": "AI contribution", "value": "Per Carscoops, Rekor-supplied analysis over 480 cameras and two years of reads produced the ground for the stop. That was several trips on routes typically used by drug traffickers with quick turnarounds. Everything after the flag was police work; the conviction rested on what the search found. The original Forbes report, which cited court filings, is not among the sources. The sources on record give no accuracy figure, no count of other flagged drivers and no court ruling on the stop. Carscoops quotes Ben Gold calling avoidance of the system unfeasible and Brett Max Kaufman calling the surveillance's scale massive." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0369", "title": "Westchester County police use Rekor plate-reader pattern analysis to stop David Zayas in Scarsdale; crack cocaine, cash and a pistol found and he is convicted", "summary": "Westchester County PD's Rekor system flagged David Zayas's driving pattern across 480 cameras; a March 2022 stop in Scarsdale found crack cocaine, cash and a pistol; he was convicted (Carscoops).", "date_observed": "2022-03-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Carscoops reports that the Westchester County Police Department, New York, used technology provided by Rekor. It examined the driving patterns of anyone passing its 480 license-plate-reader cameras over a two-year period. It determined that David Zayas had made several trips along routes typically used by drug traffickers and turned around quickly to go home. In March 2022 Zayas was driving a gray Chevrolet in Scarsdale. Officers pulled him over, searched the car and found crack cocaine, cash and a pistol. The case ended in his conviction (Carscoops, 18 July 2023, drawing on Forbes). The Star's AFP Relaxnews item adds that police took an interest in a vehicle registered to Zayas in March 2022. The OECD Monitor summary says the system analyzed billions of plate records and lists rights and public-interest harms. Carscoops quotes ACLU lawyer Brett Max Kaufman and defense attorney Ben Gold criticizing the surveillance. Only the month is given, so 1 March 2022 is used.", "category": "benefit created", "unexpected": false, "purpose": "Carscoops describes Rekor's Public Safety Network as aggregating vehicle location data from its customers so police can examine driving patterns. Flagging a pattern associated with drug trafficking for a stop is that function, not a side effect.", "system": "Rekor license-plate-reader pattern analysis (Rekor Public Safety Network data) used by the Westchester County Police Department", "model_version": null, "developer": "Rekor", "deployer": "Westchester County Police Department", "location": "Scarsdale, United States", "domain": "Policing / drug trafficking", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2023-07-17-994a" }, "ratings": [ { "group": "societal", "depth": 2, "reason": "A driver found with crack cocaine, cash and a pistol was convicted after the AI-flagged stop, per Carscoops. The quantities, the charges and the sentence are not in the sources on record, which do not include the Forbes report that cited court documents." } ], "ai_role": { "status": "assessed", "statement": "Per Carscoops, Rekor-supplied analysis over 480 cameras and two years of reads produced the ground for the stop. That was several trips on routes typically used by drug traffickers with quick turnarounds. Everything after the flag was police work; the conviction rested on what the search found. The original Forbes report, which cited court filings, is not among the sources. The sources on record give no accuracy figure, no count of other flagged drivers and no court ruling on the stop. Carscoops quotes Ben Gold calling avoidance of the system unfeasible and Brett Max Kaufman calling the surveillance's scale massive." }, "evidence": [ { "kind": "news report", "citation": "Bell, S. (2023). Police Use AI To Analyze Driving Patterns And Surveil All Drivers. Carscoops, 18 July 2023.", "title": "Police Use AI To Analyze Driving Patterns And Surveil All Drivers", "url": "https://www.carscoops.com/2023/07/police-use-ai-to-analyze-driving-patterns-and-surveil-all-drivers", "date": "2023-07-18", "written_by": "independent", "quote": "Westchester PD was able to determine that Zayas made several trips along routes typically used by drug traffickers, and turned around quickly to go home." }, { "kind": "news report", "citation": "Bell, S. (2023). Police Use AI To Analyze Driving Patterns And Surveil All Drivers. Carscoops, 18 July 2023 (second quotation).", "title": "Police Use AI To Analyze Driving Patterns And Surveil All Drivers", "url": "https://www.carscoops.com/2023/07/police-use-ai-to-analyze-driving-patterns-and-surveil-all-drivers", "date": "2023-07-18", "written_by": "independent", "quote": "Westchester PD pulled him over and searched his car and found crack cocaine, cash, and a pistol." }, { "kind": "news report", "citation": "AFP Relaxnews (2023). How AI helped catch a drug trafficker. The Star (Malaysia), 20 July 2023.", "title": "How AI helped catch a drug trafficker", "url": "https://www.thestar.com.my/tech/tech-news/2023/07/20/how-ai-helped-catch-a-drug-trafficker", "date": "2023-07-20", "written_by": "independent", "quote": "In March 2022, the police took an interest in the behaviour of a vehicle registered to a certain David Zayas." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2023). AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York. OECD.AI, 17 July 2023.", "title": "AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York", "url": "https://oecd.ai/en/incidents/2023-07-17-994a", "date": "2023-07-17", "written_by": "independent", "quote": "This led to the stop and arrest of David Zayas, who was found with drugs, cash, and a weapon." } ], "replication": { "produced_by": [ "A county network of 480 license-plate-reader cameras feeding Rekor's aggregated vehicle-location data", "Pattern analysis over two years of reads that flags routes and quick turnarounds associated with drug trafficking", "A traffic stop and vehicle search by officers acting on the flag" ], "must_hold": [ "The flagged pattern must be a lawful basis for a stop and search, which defense counsel contested", "Officers must corroborate the pattern with evidence found at the stop", "Retention and analysis of plate data over years must be permitted" ], "echoes": [] }, "paired_incident": null, "headline": "Rekor plate-pattern analysis leads to a Scarsdale drug stop", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-07-17-994a", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rekor.ai/; https://publicsafety.westchestercountyny.gov/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. 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That was several trips on routes typically used by drug traffickers with quick turnarounds. Everything after the flag was police work; the conviction rested on what the search found. The original Forbes report, which cited court filings, is not among the sources. The sources on record give no accuracy figure, no count of other flagged drivers and no court ruling on the stop. 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Carscoops, 18 July 2023 (second quotation).", "title": "Police Use AI To Analyze Driving Patterns And Surveil All Drivers", "url": "https://www.carscoops.com/2023/07/police-use-ai-to-analyze-driving-patterns-and-surveil-all-drivers", "date": "2023-07-18", "written_by": "independent", "quote": "Westchester PD pulled him over and searched his car and found crack cocaine, cash, and a pistol." }, { "kind": "news report", "citation": "AFP Relaxnews (2023). How AI helped catch a drug trafficker. The Star (Malaysia), 20 July 2023.", "title": "How AI helped catch a drug trafficker", "url": "https://www.thestar.com.my/tech/tech-news/2023/07/20/how-ai-helped-catch-a-drug-trafficker", "date": "2023-07-20", "written_by": "independent", "quote": "In March 2022, the police took an interest in the behaviour of a vehicle registered to a certain David Zayas." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2023). AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York. OECD.AI, 17 July 2023.", "title": "AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York", "url": "https://oecd.ai/en/incidents/2023-07-17-994a", "date": "2023-07-17", "written_by": "independent", "quote": "This led to the stop and arrest of David Zayas, who was found with drugs, cash, and a weapon." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0369-source-0001", "AIBD-0369-source-0002", "AIBD-0369-source-0003", "AIBD-0369-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Bell, S. (2023). Police Use AI To Analyze Driving Patterns And Surveil All Drivers. Carscoops, 18 July 2023.", "title": "Police Use AI To Analyze Driving Patterns And Surveil All Drivers", "url": "https://www.carscoops.com/2023/07/police-use-ai-to-analyze-driving-patterns-and-surveil-all-drivers", "date": "2023-07-18", "written_by": "independent", "quote": "Westchester PD was able to determine that Zayas made several trips along routes typically used by drug traffickers, and turned around quickly to go home." }, { "kind": "news report", "citation": "Bell, S. (2023). Police Use AI To Analyze Driving Patterns And Surveil All Drivers. Carscoops, 18 July 2023 (second quotation).", "title": "Police Use AI To Analyze Driving Patterns And Surveil All Drivers", "url": "https://www.carscoops.com/2023/07/police-use-ai-to-analyze-driving-patterns-and-surveil-all-drivers", "date": "2023-07-18", "written_by": "independent", "quote": "Westchester PD pulled him over and searched his car and found crack cocaine, cash, and a pistol." }, { "kind": "news report", "citation": "AFP Relaxnews (2023). How AI helped catch a drug trafficker. The Star (Malaysia), 20 July 2023.", "title": "How AI helped catch a drug trafficker", "url": "https://www.thestar.com.my/tech/tech-news/2023/07/20/how-ai-helped-catch-a-drug-trafficker", "date": "2023-07-20", "written_by": "independent", "quote": "In March 2022, the police took an interest in the behaviour of a vehicle registered to a certain David Zayas." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2023). AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York. OECD.AI, 17 July 2023.", "title": "AI-Powered License Plate Surveillance Leads to Drug Trafficker Arrest in New York", "url": "https://oecd.ai/en/incidents/2023-07-17-994a", "date": "2023-07-17", "written_by": "independent", "quote": "This led to the stop and arrest of David Zayas, who was found with drugs, cash, and a weapon." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Westchester County PD's Rekor system flagged David Zayas's driving pattern across 480 cameras; a March 2022 stop in Scarsdale found crack cocaine, cash and a pistol; he was convicted (Carscoops).", "account": "Carscoops reports that the Westchester County Police Department, New York, used technology provided by Rekor. It examined the driving patterns of anyone passing its 480 license-plate-reader cameras over a two-year period. It determined that David Zayas had made several trips along routes typically used by drug traffickers and turned around quickly to go home. In March 2022 Zayas was driving a gray Chevrolet in Scarsdale. Officers pulled him over, searched the car and found crack cocaine, cash and a pistol. The case ended in his conviction (Carscoops, 18 July 2023, drawing on Forbes). The Star's AFP Relaxnews item adds that police took an interest in a vehicle registered to Zayas in March 2022. The OECD Monitor summary says the system analyzed billions of plate records and lists rights and public-interest harms. Carscoops quotes ACLU lawyer Brett Max Kaufman and defense attorney Ben Gold criticizing the surveillance. Only the month is given, so 1 March 2022 is used." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Rekor license-plate-reader pattern analysis (Rekor Public Safety Network data) used by the Westchester County Police Department" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rekor" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Westchester County police use Rekor plate-reader pattern analysis to stop David Zayas in Scarsdale; crack cocaine, cash and a pistol found and he is convicted", "headline": "Rekor plate-pattern analysis leads to a Scarsdale drug stop" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d6b8af6c234b5590e85d31593bf1abe48e30ebfbbeabb5bb0fbf7aea9258142a" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Carscoops reports that the Westchester County Police Department, New York, used technology provided by Rekor. It examined the driving patterns of anyone passing its 480 license-plate-reader cameras over a two-year period. It determined that David Zayas had made several trips along routes typically used by drug traffickers and turned around quickly to go home. In March 2022 Zayas was driving a gray Chevrolet in Scarsdale. Officers pulled him over, searched the car and found crack cocaine, cash and a pistol. The case ended in his conviction (Carscoops, 18 July 2023, drawing on Forbes). The Star's AFP Relaxnews item adds that police took an interest in a vehicle registered to Zayas in March 2022. The OECD Monitor summary says the system analyzed billions of plate records and lists rights and public-interest harms. Carscoops quotes ACLU lawyer Brett Max Kaufman and defense attorney Ben Gold criticizing the surveillance. Only the month is given, so 1 March 2022 is used.", "replication": { "produced_by": [ "A county network of 480 license-plate-reader cameras feeding Rekor's aggregated vehicle-location data", "Pattern analysis over two years of reads that flags routes and quick turnarounds associated with drug trafficking", "A traffic stop and vehicle search by officers acting on the flag" ], "must_hold": [ "The flagged pattern must be a lawful basis for a stop and search, which defense counsel contested", "Officers must corroborate the pattern with evidence found at the stop", "Retention and analysis of plate data over years must be permitted" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0174", "case_url": "records/AIBD-0174.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Identify high-risk customers, transactions and hidden relationships for AML/CFT and trade-based money laundering investigations." }, { "label": "AI contribution", "value": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Hong Kong", "China" ] }, "record": { "id": "AIBD-0174", "title": "A third company linked to suspected money laundering", "summary": "Bank F’s network tool found phone numbers and email addresses shared by three companies. Further account checks indicated possible money laundering.", "date_observed": "2021-01-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Hong Kong Monetary Authority's report “AML/CFT Regtech: Case Studies and Insights” (January 2021; press release 21 January 2021) presents anonymized bank case studies. Bank F is a subsidiary and regional head office of a large global bank in Asia-Pacific, per the report. It implemented a network (graph) analytics solution in Hong Kong in 2013. It identified high-risk customers and transactions from wholesale client, correspondent banking, trade, payments and external registry data. In a 2015 example, a relationship manager gathered intelligence about trade-based money laundering (TBML) related to a drug cartel in one account. The manager linked it to a second account via the CRM tool. The tool then identified additional companies linked to the first two parties through linked phone numbers and email addresses. Account reviews for Companies A, B and C revealed potential money laundering concerns. The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. The report’s USD 50 million figure assumes that 10% of those exposures would otherwise be unrecoverable. It does not document USD 50 million in realized savings. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. The 2015 example carries no day.", "category": "protective response", "unexpected": false, "purpose": "Identify high-risk customers, transactions and hidden relationships for AML/CFT and trade-based money laundering investigations.", "system": "Bank F network (graph) analytics solution implemented in Hong Kong in 2013", "model_version": null, "developer": "Bank F (anonymized subsidiary and regional head office of a large global bank); the report names no vendor", "deployer": "Bank F, Hong Kong", "location": "Hong Kong, China", "domain": "Anti-money laundering", "segment": "Trade finance and trade-based money laundering", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The report records reviews of around 100 customer relationships. It also records a third linked company whose review revealed potential money laundering concerns. Losses avoided are estimated, not observed." }, { "group": "societal", "depth": null, "reason": "The foreword's statement about neutralizing mule account networks is general and not tied to a documented outcome." } ], "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." }, "evidence": [ { "kind": "descriptive report", "citation": "Hong Kong Monetary Authority and Deloitte, AML/CFT Regtech: Case Studies and Insights (January 2021), Bank F, 24–25.", "title": "AML/CFT Regtech: Case Studies and Insights", "url": "https://www.hkma.gov.hk/media/eng/doc/key-information/guidelines-and-circular/2021/20210121e2a1.pdf", "date": "2021-01-21", "written_by": "independent", "quote": "Since deployment, the tool has led to the review of around 100 customer relationships for AML/CFT reasons." }, { "kind": "press release", "citation": "Hong Kong Monetary Authority, press release, 21 January 2021", "title": "AML/CFT Regtech: Case Studies and Insights", "url": "https://www.hkma.gov.hk/eng/news-and-media/press-releases/2021/01/20210121-3/", "date": "2021-01-21", "written_by": "independent", "quote": "AML/CFT Regtech: Case Studies and Insights", "supports_benefit": false } ], "replication": { "produced_by": [ "Integration of wholesale client, correspondent banking, trade, payments and external registry data in one network model", "A human intelligence lead (the relationship manager's TBML suspicion) used as the seed for network expansion" ], "must_hold": [ "Data of sufficient quality across sources, which the report says Bank F stressed", "Governance that lets analytic findings trigger account reviews and STR filings" ], "echoes": [ { "date": null, "setting": "Bank F, top 20 commercial banking sites globally (2016)", "model_version": null, "outcome": "recurred", "source": "HKMA report: coverage of about 80% of the trade lending book; around 100 relationships reviewed since deployment" }, { "date": null, "setting": "Bank A, Hong Kong, pilot using IP addresses and login data for about 50 customers (2020)", "model_version": null, "outcome": "recurred", "source": "HKMA report: undisclosed clusters of relationships identified from shared IP addresses and simultaneous logins" }, { "date": "2021-01-21", "setting": "Fraud and Money Laundering Intelligence Taskforce member banks", "model_version": null, "outcome": "unknown", "source": "HKMA foreword: techniques 'neutralising mule account networks linked to face mask scams', no figures" } ] }, "paired_incident": null, "headline": "A third company linked to suspected money laundering", "metric": "even if 10% were unrecoverable the tool contributed around USD 50 million in savings", "comparator": null, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the January 2021 HKMA report was found for Bank F's tool; the report's own scale-up figures predate it.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://www.hkma.gov.hk/media/eng/doc/key-information/guidelines-and-circular/2021/20210121e2a1.pdf", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent descriptive report and one independent press release. For the benefit itself, the cited sources include no independent study of the benefit and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0174", "catalog_version": "1.0.0", "source_fingerprint": "f71e252dd0e9ecdf3e46dff9d0e825723825b89435967072388b85b57705a63e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0174-source-0001", "AIBD-0174-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Bank F (anonymized subsidiary and regional head office of a large global bank); the report names no vendor", "deployer": "Bank F, Hong Kong" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The report records reviews of around 100 customer relationships. It also records a third linked company whose review revealed potential money laundering concerns. Losses avoided are estimated, not observed." }, { "group": "societal", "reason": "The foreword's statement about neutralizing mule account networks is general and not tied to a documented outcome." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent descriptive report and one independent press release. 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Losses avoided are estimated, not observed." }, { "group": "societal", "reason": "The foreword's statement about neutralizing mule account networks is general and not tied to a documented outcome." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The report records reviews of around 100 customer relationships. It also records a third linked company whose review revealed potential money laundering concerns. 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The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. The report’s USD 50 million figure assumes that 10% of those exposures would otherwise be unrecoverable. It does not document USD 50 million in realized savings. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. The 2015 example carries no day." } }, "provenance": { "kind": "legacy_record", "basis": "Wording aligned with the source review of 25 October 2026; the longer account retains previously collected details.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Bank F network (graph) analytics solution implemented in Hong Kong in 2013" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Bank F (anonymized subsidiary and regional head office of a large global bank); the report names no vendor" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "A third company linked to suspected money laundering", "headline": "A third company linked to suspected money laundering" } }, "provenance": { "kind": "legacy_record", "basis": "Wording aligned with the source review of 25 October 2026; the longer account retains previously collected details.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f71e252dd0e9ecdf3e46dff9d0e825723825b89435967072388b85b57705a63e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Hong Kong Monetary Authority's report “AML/CFT Regtech: Case Studies and Insights” (January 2021; press release 21 January 2021) presents anonymized bank case studies. Bank F is a subsidiary and regional head office of a large global bank in Asia-Pacific, per the report. It implemented a network (graph) analytics solution in Hong Kong in 2013. It identified high-risk customers and transactions from wholesale client, correspondent banking, trade, payments and external registry data. In a 2015 example, a relationship manager gathered intelligence about trade-based money laundering (TBML) related to a drug cartel in one account. The manager linked it to a second account via the CRM tool. The tool then identified additional companies linked to the first two parties through linked phone numbers and email addresses. Account reviews for Companies A, B and C revealed potential money laundering concerns. The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. Even if 10% were unrecoverable, the tool contributed around USD 50 million in savings, it says. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. The 2015 example carries no day.", "replication": { "produced_by": [ "Integration of wholesale client, correspondent banking, trade, payments and external registry data in one network model", "A human intelligence lead (the relationship manager's TBML suspicion) used as the seed for network expansion" ], "must_hold": [ "Data of sufficient quality across sources, which the report says Bank F stressed", "Governance that lets analytic findings trigger account reviews and STR filings" ], "echoes": [ { "setting": "Bank F, top 20 commercial banking sites globally (2016)", "outcome": "recurred", "source": "HKMA report: coverage of about 80% of the trade lending book; around 100 relationships reviewed since deployment" }, { "setting": "Bank A, Hong Kong, pilot using IP addresses and login data for about 50 customers (2020)", "outcome": "recurred", "source": "HKMA report: undisclosed clusters of relationships identified from shared IP addresses and simultaneous logins" }, { "date": "2021-01-21", "setting": "Fraud and Money Laundering Intelligence Taskforce member banks", "outcome": "unknown", "source": "HKMA foreword: techniques 'neutralising mule account networks linked to face mask scams', no figures" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 2, "record_mapped": 28, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0223", "case_url": "records/AIBD-0223.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Verify arriving international travelers' identities by comparing a live photo with passport or visa photos in government holdings, to detect impostors and expedite entry." }, { "label": "AI contribution", "value": "The facial comparison match (less than two seconds per CBP) produced the mismatch that triggered secondary inspection. The search and removal were human actions. The documentation is CBP's own release (deployer, not developer) plus NBC with a named CBP official. The algorithm vendor and the match threshold are not in the sources. CBP's “99% match rate” claim is unaudited here." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0223", "title": "CBP facial comparison at Dulles flags a French-passport impostor on day three; Congolese ID found in his shoe", "summary": "CBP's new facial comparison system at Dulles reported a mismatch for a man presenting a French passport on 22 August 2018; a Congolese ID was found in his shoe and he was returned to Brazil.", "date_observed": "2018-08-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 22 August 2018, the third day of facial comparison biometrics at Washington Dulles International Airport, a 26-year-old man arrived from Sao Paulo. He presented a French passport at primary inspection. CBP's release states the officer used the facial comparison biometric technology, which confirmed the man was not a match to the passport. In secondary inspection officers found his authentic Republic of Congo identification card concealed in his shoe. CBP Area Port Director Daniel Mattina told NBC News the man was apprehended, processed and returned to Brazil. CBP called him the first impostor detected with the technology. CBP releases record a second (8 September 2018) and third (1 October 2018) impostor at Dulles and, by September 2021, more than 900 impostors nationally. The sources do not name the matching-algorithm vendor.", "category": "harm prevented", "unexpected": false, "purpose": "Verify arriving international travelers' identities by comparing a live photo with passport or visa photos in government holdings, to detect impostors and expedite entry.", "system": "CBP facial comparison biometric system (biometric entry/exit, Traveler Verification Service)", "model_version": null, "developer": "Not named in the sources", "deployer": "U.S. Customs and Border Protection, Office of Field Operations, Washington Dulles International Airport", "location": "Sterling, United States", "domain": "Border security", "segment": "Government", "benefit_type": "Reduced Violence", "taxonomy_note": "The crime stopped was passport fraud, not a violent act.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "An attempt to enter the United States on another person's genuine passport was stopped and the traveler removed. Sources document no further threat beyond the document fraud." }, { "group": "organizational", "depth": 1, "reason": "CBP gained a verified first detection for a system then three days into use at Dulles. Subsequent releases report repeated catches." } ], "ai_role": { "status": "assessed", "statement": "The facial comparison match (less than two seconds per CBP) produced the mismatch that triggered secondary inspection. The search and removal were human actions. The documentation is CBP's own release (deployer, not developer) plus NBC with a named CBP official. The algorithm vendor and the match threshold are not in the sources. CBP's “99% match rate” claim is unaudited here." }, "evidence": [ { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 23 Aug 2018", "title": "CBP at Washington Dulles International Airport intercepted an imposter using new cutting-edge Facial Comparison Biometrics technology", "url": "https://www.cbp.gov/newsroom/local-media-release/cbp-washington-dulles-international-airport-intercepted-imposter-using", "date": "2018-08-23", "written_by": "deployer", "quote": "The officer utilized CBP’s new facial comparison biometric technology which confirmed the man was not a match to the passport he presented." }, { "kind": "news report", "citation": "Tom Costello and Ethan Sacks, NBC News, 23 Aug 2018", "title": "New facial recognition tech catches first impostor at D.C. airport", "url": "https://www.nbcnews.com/news/us-news/new-facial-recognition-tech-catches-first-impostor-d-c-airport-n903236", "date": "2018-08-23", "written_by": "independent", "quote": "So he was trying to enter the country illegally, we apprehended him, processed him and returned him back to Brazil," }, { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 24 Sep 2021", "title": "Laredo CBP Officers Detect Impostor Through Facial Biometrics", "url": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "date": "2021-09-24", "written_by": "deployer", "quote": "CBP has leveraged facial biometrics to prevent more than 900 impostors from illegally entering the United States" } ], "replication": { "produced_by": [ "Live photo at primary inspection compared against passport/visa photos retrieved from government holdings via airline manifests", "Officer referral to secondary inspection on a reported mismatch", "Traveler using a genuine document issued to someone else" ], "must_hold": [ "A reference photo of the document's true bearer must exist in DHS holdings", "Officers must act on mismatches rather than override them", "Photo quality and demographic performance of the matcher must be adequate" ], "echoes": [ { "date": "2018-09-08", "setting": "Washington Dulles; Ghanaian woman presenting a U.S. passport", "model_version": null, "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/second-impostor-three-weeks-caught-cbp-biometric-verification" }, { "date": "2018-10-01", "setting": "Washington Dulles; Cameroonian woman presenting a U.S. passport, arrested under 18 USC 1544", "model_version": null, "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/national-media-release/dulles-cbp-s-new-biometric-verification-technology-catches-third" }, { "date": "2021-09-20", "setting": "Laredo Gateway to the Americas Bridge; Mexican woman presenting a U.S. permanent resident card", "model_version": null, "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" } ] }, "paired_incident": null, "headline": "CBP facial comparison at Dulles flags a French-passport impostor", "metric": "by September 2021, more than 900 impostors nationally", "comparator": null, "later": { "status": "scaled", "date": "2021-09-24", "note": "By September 2021 CBP reported that facial biometrics had stopped more than 900 impostors nationwide since September 2018 and that close to 100 million travelers had been processed.", "quote": "Since September 2018, CBP has leveraged facial biometrics to prevent more than 900 impostors from illegally entering the United States by using genuine travel documents that were issued to other people.", "url": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" }, "value": null, "developer_country": null, "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0223", "catalog_version": "1.0.0", "source_fingerprint": "db88b80b0b25a1bd4071bfc8291de447404b6d96fb0a7ee2ccba5687e66bfd98", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0223-source-0001", "AIBD-0223-source-0002", "AIBD-0223-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Not named in the sources", "deployer": "U.S. Customs and Border Protection, Office of Field Operations, Washington Dulles International Airport" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The facial comparison match (less than two seconds per CBP) produced the mismatch that triggered secondary inspection. The search and removal were human actions. The documentation is CBP's own release (deployer, not developer) plus NBC with a named CBP official. The algorithm vendor and the match threshold are not in the sources. CBP's “99% match rate” claim is unaudited here." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The facial comparison match (less than two seconds per CBP) produced the mismatch that triggered secondary inspection. The search and removal were human actions. The documentation is CBP's own release (deployer, not developer) plus NBC with a named CBP official. The algorithm vendor and the match threshold are not in the sources. CBP's “99% match rate” claim is unaudited here." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "An attempt to enter the United States on another person's genuine passport was stopped and the traveler removed. Sources document no further threat beyond the document fraud." }, { "group": "organizational", "depth": 1, "reason": "CBP gained a verified first detection for a system then three days into use at Dulles. Subsequent releases report repeated catches." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and two press releases from the deployer. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "An attempt to enter the United States on another person's genuine passport was stopped and the traveler removed. Sources document no further threat beyond the document fraud." }, { "group": "organizational", "depth": 1, "reason": "CBP gained a verified first detection for a system then three days into use at Dulles. Subsequent releases report repeated catches." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "An attempt to enter the United States on another person's genuine passport was stopped and the traveler removed. Sources document no further threat beyond the document fraud." }, { "group": "organizational", "depth": 1, "reason": "CBP gained a verified first detection for a system then three days into use at Dulles. Subsequent releases report repeated catches." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "by September 2021, more than 900 impostors nationally", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2021-09-24", "note": "By September 2021 CBP reported that facial biometrics had stopped more than 900 impostors nationwide since September 2018 and that close to 100 million travelers had been processed.", "quote": "Since September 2018, CBP has leveraged facial biometrics to prevent more than 900 impostors from illegally entering the United States by using genuine travel documents that were issued to other people.", "url": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0223" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "The crime stopped was passport fraud, not a violent act." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 22 August 2018, the third day of facial comparison biometrics at Washington Dulles International Airport, a 26-year-old man arrived from Sao Paulo. He presented a French passport at primary inspection. CBP's release states the officer used the facial comparison biometric technology, which confirmed the man was not a match to the passport. In secondary inspection officers found his authentic Republic of Congo identification card concealed in his shoe. CBP Area Port Director Daniel Mattina told NBC News the man was apprehended, processed and returned to Brazil. CBP called him the first impostor detected with the technology. CBP releases record a second (8 September 2018) and third (1 October 2018) impostor at Dulles and, by September 2021, more than 900 impostors nationally. The sources do not name the matching-algorithm vendor.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 2, "reason": "An attempt to enter the United States on another person's genuine passport was stopped and the traveler removed. Sources document no further threat beyond the document fraud." }, { "group": "organizational", "depth": 1, "reason": "CBP gained a verified first detection for a system then three days into use at Dulles. Subsequent releases report repeated catches." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Verify arriving international travelers' identities by comparing a live photo with passport or visa photos in government holdings, to detect impostors and expedite entry." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Sterling, United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Verify arriving international travelers' identities by comparing a live photo with passport or visa photos in government holdings, to detect impostors and expedite entry." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2018-08-22", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Live photo at primary inspection compared against passport/visa photos retrieved from government holdings via airline manifests", "Officer referral to secondary inspection on a reported mismatch", "Traveler using a genuine document issued to someone else" ], "must_hold": [ "A reference photo of the document's true bearer must exist in DHS holdings", "Officers must act on mismatches rather than override them", "Photo quality and demographic performance of the matcher must be adequate" ], "echoes": [ { "date": "2018-09-08", "setting": "Washington Dulles; Ghanaian woman presenting a U.S. passport", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/second-impostor-three-weeks-caught-cbp-biometric-verification" }, { "date": "2018-10-01", "setting": "Washington Dulles; Cameroonian woman presenting a U.S. passport, arrested under 18 USC 1544", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/national-media-release/dulles-cbp-s-new-biometric-verification-technology-catches-third" }, { "date": "2021-09-20", "setting": "Laredo Gateway to the Americas Bridge; Mexican woman presenting a U.S. permanent resident card", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Live photo at primary inspection compared against passport/visa photos retrieved from government holdings via airline manifests", "Officer referral to secondary inspection on a reported mismatch", "Traveler using a genuine document issued to someone else" ], "must_hold": [ "A reference photo of the document's true bearer must exist in DHS holdings", "Officers must act on mismatches rather than override them", "Photo quality and demographic performance of the matcher must be adequate" ], "echoes": [ { "date": "2018-09-08", "setting": "Washington Dulles; Ghanaian woman presenting a U.S. passport", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/second-impostor-three-weeks-caught-cbp-biometric-verification" }, { "date": "2018-10-01", "setting": "Washington Dulles; Cameroonian woman presenting a U.S. passport, arrested under 18 USC 1544", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/national-media-release/dulles-cbp-s-new-biometric-verification-technology-catches-third" }, { "date": "2021-09-20", "setting": "Laredo Gateway to the Americas Bridge; Mexican woman presenting a U.S. permanent resident card", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/U.S._Customs_and_Border_Protection", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Border security", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 23 Aug 2018", "title": "CBP at Washington Dulles International Airport intercepted an imposter using new cutting-edge Facial Comparison Biometrics technology", "url": "https://www.cbp.gov/newsroom/local-media-release/cbp-washington-dulles-international-airport-intercepted-imposter-using", "date": "2018-08-23", "written_by": "deployer", "quote": "The officer utilized CBP’s new facial comparison biometric technology which confirmed the man was not a match to the passport he presented." }, { "kind": "news report", "citation": "Tom Costello and Ethan Sacks, NBC News, 23 Aug 2018", "title": "New facial recognition tech catches first impostor at D.C. airport", "url": "https://www.nbcnews.com/news/us-news/new-facial-recognition-tech-catches-first-impostor-d-c-airport-n903236", "date": "2018-08-23", "written_by": "independent", "quote": "So he was trying to enter the country illegally, we apprehended him, processed him and returned him back to Brazil," }, { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 24 Sep 2021", "title": "Laredo CBP Officers Detect Impostor Through Facial Biometrics", "url": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "date": "2021-09-24", "written_by": "deployer", "quote": "CBP has leveraged facial biometrics to prevent more than 900 impostors from illegally entering the United States" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0223-source-0001", "AIBD-0223-source-0002", "AIBD-0223-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 23 Aug 2018", "title": "CBP at Washington Dulles International Airport intercepted an imposter using new cutting-edge Facial Comparison Biometrics technology", "url": "https://www.cbp.gov/newsroom/local-media-release/cbp-washington-dulles-international-airport-intercepted-imposter-using", "date": "2018-08-23", "written_by": "deployer", "quote": "The officer utilized CBP’s new facial comparison biometric technology which confirmed the man was not a match to the passport he presented." }, { "kind": "news report", "citation": "Tom Costello and Ethan Sacks, NBC News, 23 Aug 2018", "title": "New facial recognition tech catches first impostor at D.C. airport", "url": "https://www.nbcnews.com/news/us-news/new-facial-recognition-tech-catches-first-impostor-d-c-airport-n903236", "date": "2018-08-23", "written_by": "independent", "quote": "So he was trying to enter the country illegally, we apprehended him, processed him and returned him back to Brazil," }, { "kind": "press release", "citation": "U.S. Customs and Border Protection, local media release, 24 Sep 2021", "title": "Laredo CBP Officers Detect Impostor Through Facial Biometrics", "url": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics", "date": "2021-09-24", "written_by": "deployer", "quote": "CBP has leveraged facial biometrics to prevent more than 900 impostors from illegally entering the United States" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "CBP's new facial comparison system at Dulles reported a mismatch for a man presenting a French passport on 22 August 2018; a Congolese ID was found in his shoe and he was returned to Brazil.", "account": "On 22 August 2018, the third day of facial comparison biometrics at Washington Dulles International Airport, a 26-year-old man arrived from Sao Paulo. He presented a French passport at primary inspection. CBP's release states the officer used the facial comparison biometric technology, which confirmed the man was not a match to the passport. In secondary inspection officers found his authentic Republic of Congo identification card concealed in his shoe. CBP Area Port Director Daniel Mattina told NBC News the man was apprehended, processed and returned to Brazil. CBP called him the first impostor detected with the technology. CBP releases record a second (8 September 2018) and third (1 October 2018) impostor at Dulles and, by September 2021, more than 900 impostors nationally. The sources do not name the matching-algorithm vendor." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "CBP facial comparison biometric system (biometric entry/exit, Traveler Verification Service)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Not named in the sources" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "CBP facial comparison at Dulles flags a French-passport impostor on day three; Congolese ID found in his shoe", "headline": "CBP facial comparison at Dulles flags a French-passport impostor" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "db88b80b0b25a1bd4071bfc8291de447404b6d96fb0a7ee2ccba5687e66bfd98" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 22 August 2018, the third day of facial comparison biometrics at Washington Dulles International Airport, a 26-year-old man arrived from Sao Paulo. He presented a French passport at primary inspection. CBP's release states the officer used the facial comparison biometric technology, which confirmed the man was not a match to the passport. In secondary inspection officers found his authentic Republic of Congo identification card concealed in his shoe. CBP Area Port Director Daniel Mattina told NBC News the man was apprehended, processed and returned to Brazil. CBP called him the first impostor detected with the technology. CBP releases record a second (8 September 2018) and third (1 October 2018) impostor at Dulles and, by September 2021, more than 900 impostors nationally. The sources do not name the matching-algorithm vendor.", "replication": { "produced_by": [ "Live photo at primary inspection compared against passport/visa photos retrieved from government holdings via airline manifests", "Officer referral to secondary inspection on a reported mismatch", "Traveler using a genuine document issued to someone else" ], "must_hold": [ "A reference photo of the document's true bearer must exist in DHS holdings", "Officers must act on mismatches rather than override them", "Photo quality and demographic performance of the matcher must be adequate" ], "echoes": [ { "date": "2018-09-08", "setting": "Washington Dulles; Ghanaian woman presenting a U.S. passport", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/second-impostor-three-weeks-caught-cbp-biometric-verification" }, { "date": "2018-10-01", "setting": "Washington Dulles; Cameroonian woman presenting a U.S. passport, arrested under 18 USC 1544", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/national-media-release/dulles-cbp-s-new-biometric-verification-technology-catches-third" }, { "date": "2021-09-20", "setting": "Laredo Gateway to the Americas Bridge; Mexican woman presenting a U.S. permanent resident card", "outcome": "recurred", "source": "https://www.cbp.gov/newsroom/local-media-release/laredo-cbp-officers-detect-impostor-through-facial-biometrics" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0240", "case_url": "records/AIBD-0240.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Anthropic's consumer terms (effective 8 October 2025) reserve the right “at our sole discretion” to report Inputs to law enforcement. Its privacy policy (effective 10 September 2026) allows disclosure the company believes “reasonably necessary” to “prevent serious harm to any person or to property”. The arrest report says the platform's safety measures are “designed to monitor for key phrases and potentially threatening content”. Flagging a stated shooting threat, reviewing it by hand and referring it to police was that purpose. Of an August 2026 referral Anthropic said “This is our safeguards process working as intended”." }, { "label": "AI contribution", "value": "The arrest report says the platform “utilizes safety and security measures designed to monitor for key phrases and potentially threatening content”. It says the messages were “escalated to a human review team”. An automated layer therefore surfaced the two messages, and people judged their severity and made the referral. The sources do not say whether the flag came from a classifier or a keyword rule. Straight Arrow News, citing court records, says the review team escalated to the FBI, which notified the sheriff's office. Identifying the user, the home visit, the Miranda interview and the charging decision were the work of Lee County deputies and Intelligence Detective Thomas Alejo. The arrest report calls the platform “Anthropic Artificial Intelligence”. Press reports name Claude, and court records note the user told the detective she used Perplexity." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0240", "title": "Anthropic's threat monitoring flags a Bonita Springs woman's messages about shooting up the Lee County Sheriff's Office; human reviewers tip police, who detain her without incident and find no gun", "summary": "Anthropic safeguards flagged a threat to shoot up a Florida sheriff's office; reviewers tipped police, who arrested the user; no gun found.", "date_observed": "2026-09-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A user of Anthropic's AI chat service wrote at about 05:10 on 26 September 2026. She said she was going to “shoot up” the Lee County Sheriff's Office in Florida. At 01:07 on 27 September she wrote that she had got a new gun. Anthropic's safety monitoring flagged the messages, and because of their severity a human review team reported them to law enforcement. The sheriff's office logged the tip on 29 September and identified the user as Carli Michelle Heller, 30, of Bonita Springs. Deputies detained her at home without incident at 00:51 on 30 September. A detective charged her under Florida Statute 836.10, written threats of violence. No weapon was seized, and she described the AI as a personal diary.", "category": "protective response", "unexpected": false, "purpose": "Anthropic's consumer terms (effective 8 October 2025) reserve the right “at our sole discretion” to report Inputs to law enforcement. Its privacy policy (effective 10 September 2026) allows disclosure the company believes “reasonably necessary” to “prevent serious harm to any person or to property”. The arrest report says the platform's safety measures are “designed to monitor for key phrases and potentially threatening content”. Flagging a stated shooting threat, reviewing it by hand and referring it to police was that purpose. Of an August 2026 referral Anthropic said “This is our safeguards process working as intended”.", "system": "Anthropic Claude consumer-chat safety monitoring: automated flagging of threatening content, escalation to a human review team, referral to law enforcement", "model_version": null, "developer": "Anthropic", "deployer": "Anthropic", "location": "Bonita Springs, United States", "domain": "Online safety and threat referral to law enforcement", "segment": "Media, social platforms, and marketing", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-10-04-dced" }, "ratings": [ { "group": "organizational", "depth": 1, "reason": "The Lee County Sheriff's Office, the threatened party, received a tip quoting a stated plan to shoot up its building and a claimed new gun. It identified the user and resolved the matter at her home within about three days without incident. No weapon was seized, so no averted attack is shown." }, { "group": "societal", "depth": 1, "reason": "A written threat of a mass shooting at a public building was examined within days rather than left unread. It was at least the third Anthropic referral to police since August 2026. The benefit is bounded by the absence of a weapon, the user's diary account, and expert doubt that the words were a true threat." } ], "ai_role": { "status": "assessed", "statement": "The arrest report says the platform “utilizes safety and security measures designed to monitor for key phrases and potentially threatening content”. It says the messages were “escalated to a human review team”. An automated layer therefore surfaced the two messages, and people judged their severity and made the referral. The sources do not say whether the flag came from a classifier or a keyword rule. Straight Arrow News, citing court records, says the review team escalated to the FBI, which notified the sheriff's office. Identifying the user, the home visit, the Miranda interview and the charging decision were the work of Lee County deputies and Intelligence Detective Thomas Alejo. The arrest report calls the platform “Anthropic Artificial Intelligence”. Press reports name Claude, and court records note the user told the detective she used Perplexity." }, "evidence": [ { "kind": "court document", "citation": "Lee County Sheriff's Office, Alejo, T. S. (2026). Arrest / Notice to Appear and Probable Cause Statement, agency report 26-530248, clerk case 26CF017549, Heller, Carli Michelle. Redacted copy hosted by the Daily Caller.", "title": "Arrest report and probable cause statement, LCSO report 26-530248 (Heller, Carli Michelle)", "url": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "date": "2026-09-30", "written_by": "beneficiary", "quote": "Due to the severity of the aforementioned statements, the information was escalated to a human review team, which subsequently reported the statements to law enforcement." }, { "kind": "news report", "citation": "WINK News, Alvarado, D. (2026). Woman arrested after AI threat against Lee County Sheriff's Office: Investigators. WINK News.", "title": "Woman arrested after AI threat against Lee County Sheriff's Office: Investigators", "url": "https://www.winknews.com/news/woman-arrested-after-ai-threat-against-lee-county-sheriffs-office-investigators/article_3d4c5915-7015-43c0-b86a-d7fa5eadf958.html", "date": "2026-09-30", "written_by": "independent", "quote": "Deputies identified Heller and went to her Bonita Springs home and she was detained without incident before an LCSO intelligence detective took over the investigation." }, { "kind": "news report", "citation": "The Daily Caller, Kresak, D. (2026). Florida Woman Nabbed After Chatbot Maker Flags Alleged Threat to 'Shoot Up' Sheriff's Office. The Daily Caller.", "title": "Florida Woman Nabbed After Chatbot Maker Flags Alleged Threat to 'Shoot Up' Sheriff's Office", "url": "https://dailycaller.com/2026/10/05/chatbot-maker-tips-off-cops-florida-woman-allegedly-threatens-sheriffs-office/", "date": "2026-10-05", "written_by": "independent", "quote": "When someone uses AI to make or facilitate a threat, we have to take that threat seriously." }, { "kind": "news report", "citation": "Straight Arrow News, Pavlou, D. (2026). A chatbot message led to a felony charge. Experts say it may not hold up. Straight Arrow News.", "title": "A chatbot message led to a felony charge. Experts say it may not hold up", "url": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/", "date": "2026-10-08", "written_by": "independent", "quote": "She denied owning any firearms, and the arrest form indicates authorities didn’t seize a weapon." }, { "kind": "news report", "citation": "Tom's Hardware, Downing, S. (2026). Anthropic reports Florida woman’s Claude ‘diary’ threat to shoot up sheriff’s office, felony charge follows — it’s at least the third such conversation to reach police since August. Tom's Hardware.", "title": "Anthropic reports Florida woman’s Claude ‘diary’ threat to shoot up sheriff’s office, felony charge follows — it’s at least the third such conversation to reach police since August", "url": "https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropic-reports-florida-womans-claude-diary-threat-to-shoot-up-sheriffs-office-felony-charge-follows-its-at-least-the-third-such-conversation-to-reach-police-since-august", "date": "2026-10-05", "written_by": "independent", "quote": "court records listing the Sept. 30 felony charge under Florida Statute 836.10: written or electronic threat of a mass shooting or act of terrorism" }, { "kind": "other", "citation": "Volokh, E. (2026). Can a Chatbot Prompt Be a Punishable Threat? Reason, The Volokh Conspiracy.", "title": "Can a Chatbot Prompt Be a Punishable Threat?", "url": "https://reason.com/volokh/2026/10/08/can-a-chatbot-prompt-be-a-punishable-threat/", "date": "2026-10-08", "written_by": "independent", "quote": "Simply typing a prompt into a chatbot, expecting it to be viewed only by the software, wouldn't qualify." }, { "kind": "news report", "citation": "The San Francisco Standard, Jones, R. (2026). Anthropic called SFPD over threat against CEO. Claude user says it was a misunderstanding. The San Francisco Standard.", "title": "Anthropic called SFPD over threat against CEO. Claude user says it was a misunderstanding", "url": "https://sfstandard.com/2026/09/04/anthropic-threat-claude-sfpd/", "date": "2026-09-04", "written_by": "independent", "quote": "We banned this account, consistent with our standard practice, and referred the case to law enforcement. This is our safeguards process working as intended." }, { "kind": "other", "citation": "Anthropic (2026). Privacy Policy, effective 10 September 2026. Anthropic.", "title": "Privacy Policy", "url": "https://www.anthropic.com/legal/privacy", "date": "2026-09-10", "written_by": "makers", "quote": "(ii) prevent serious harm to any person or to property; (iii) detect, prevent, or otherwise address fraud or other illegal activity" }, { "kind": "other", "citation": "Anthropic (2025). Consumer Terms of Service, effective 8 October 2025. Anthropic.", "title": "Consumer Terms of Service", "url": "https://www.anthropic.com/legal/consumer-terms", "date": "2025-10-08", "written_by": "makers", "quote": "We reserve the right, at our sole discretion, to report information from or about you, including but not limited to Inputs, Outputs, or Actions to law enforcement." } ], "replication": { "produced_by": [ "A vendor practice of screening consumer chats for threatening content and escalating severe cases to a human review team. That team can refer them to law enforcement, which Anthropic's consumer terms and privacy policy permit", "A tip carrying enough account information for the sheriff's office to identify a named resident within days", "A local agency that treated a stated threat against its own building as credible enough to send deputies the same night" ], "must_hold": [ "The referral must identify a real person and a real risk. The Lee County Sheriff's Office told reporters it does not count tips from Anthropic. No public figures exist on how often such referrals are made or prove unfounded", "The disclosure must stay within the emergency exception of the Stored Communications Act. An EFF attorney told Straight Arrow News “there is always the risk of over-reporting”, and the Monitor logs reputational and psychological harm to the user", "The response must remain proportionate. Here no weapon was found, and the user described a personal diary. Whether a chatbot prompt is a punishable threat under Florida Statute 836.10 is contested by the defense and by Eugene Volokh", "The attribution to Claude rests on press reports and the arrest report's phrase “Anthropic Artificial Intelligence”. Court records also note the user told the detective she used Perplexity, and her attorney declined to say which" ], "echoes": [] }, "paired_incident": null, "headline": "Anthropic flag on a shooting threat leads to a Florida arrest", "metric": null, "comparator": null, "later": { "status": "litigated", "date": "2026-10-08", "note": "Heller pleaded not guilty and her arraignment was set for 2 November 2026 while prosecutors had not yet filed a formal charge; the arrest form sets a $10,000 bond and a 20 October 2026 appearance date, and First Amendment scholar Eugene Volokh doubts the charge will hold.", "quote": "Heller's arraignment is scheduled for Nov. 2", "url": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-10-04-dced", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Anthropic", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: five independent news reports, one other independent source, two other sources from the makers and the beneficiary's court document. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0240", "catalog_version": "1.0.0", "source_fingerprint": "c87231c7cd74f35a314296f55b276a55c0341260a19641f61d8c311faf2b20c2", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0240-source-0001", "AIBD-0240-source-0002", "AIBD-0240-source-0003", "AIBD-0240-source-0004", "AIBD-0240-source-0005", "AIBD-0240-source-0006", "AIBD-0240-source-0007", "AIBD-0240-source-0008", "AIBD-0240-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Anthropic", "deployer": "Anthropic" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The arrest report says the platform “utilizes safety and security measures designed to monitor for key phrases and potentially threatening content”. It says the messages were “escalated to a human review team”. An automated layer therefore surfaced the two messages, and people judged their severity and made the referral. The sources do not say whether the flag came from a classifier or a keyword rule. Straight Arrow News, citing court records, says the review team escalated to the FBI, which notified the sheriff's office. Identifying the user, the home visit, the Miranda interview and the charging decision were the work of Lee County deputies and Intelligence Detective Thomas Alejo. The arrest report calls the platform “Anthropic Artificial Intelligence”. Press reports name Claude, and court records note the user told the detective she used Perplexity." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The platform automatically flagged threatening messages for human review. Reviewers referred the messages to law enforcement.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The arrest report says the platform “utilizes safety and security measures designed to monitor for key phrases and potentially threatening content”. It says the messages were “escalated to a human review team”. An automated layer therefore surfaced the two messages, and people judged their severity and made the referral. The sources do not say whether the flag came from a classifier or a keyword rule. Straight Arrow News, citing court records, says the review team escalated to the FBI, which notified the sheriff's office. Identifying the user, the home visit, the Miranda interview and the charging decision were the work of Lee County deputies and Intelligence Detective Thomas Alejo. The arrest report calls the platform “Anthropic Artificial Intelligence”. Press reports name Claude, and court records note the user told the detective she used Perplexity." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The Lee County Sheriff's Office, the threatened party, received a tip quoting a stated plan to shoot up its building and a claimed new gun. It identified the user and resolved the matter at her home within about three days without incident. No weapon was seized, so no averted attack is shown." }, { "group": "societal", "depth": 1, "reason": "A written threat of a mass shooting at a public building was examined within days rather than left unread. It was at least the third Anthropic referral to police since August 2026. The benefit is bounded by the absence of a weapon, the user's diary account, and expert doubt that the words were a true threat." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: five independent news reports, one other independent source, two other sources from the makers and the beneficiary's court document. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The Lee County Sheriff's Office, the threatened party, received a tip quoting a stated plan to shoot up its building and a claimed new gun. It identified the user and resolved the matter at her home within about three days without incident. No weapon was seized, so no averted attack is shown." }, { "group": "societal", "depth": 1, "reason": "A written threat of a mass shooting at a public building was examined within days rather than left unread. It was at least the third Anthropic referral to police since August 2026. The benefit is bounded by the absence of a weapon, the user's diary account, and expert doubt that the words were a true threat." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Police examined a threat against their own office and detained the user within days. No weapon was found. Her diary explanation and legal objections leave doubt that an attack was prevented." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The Lee County Sheriff's Office, the threatened party, received a tip quoting a stated plan to shoot up its building and a claimed new gun. It identified the user and resolved the matter at her home within about three days without incident. No weapon was seized, so no averted attack is shown." }, { "group": "societal", "depth": 1, "reason": "A written threat of a mass shooting at a public building was examined within days rather than left unread. It was at least the third Anthropic referral to police since August 2026. The benefit is bounded by the absence of a weapon, the user's diary account, and expert doubt that the words were a true threat." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "No public figures establish how often Anthropic’s referrals are substantiated. The arrest report names Anthropic but does not identify the detection method. Coverage names Claude, while court records also report that the user mentioned Perplexity. No weapon or averted attack is established." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-10-04-dced" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2026-10-08", "note": "Heller pleaded not guilty and her arraignment was set for 2 November 2026 while prosecutors had not yet filed a formal charge; the arrest form sets a $10,000 bond and a 20 October 2026 appearance date, and First Amendment scholar Eugene Volokh doubts the charge will hold.", "quote": "Heller's arraignment is scheduled for Nov. 2", "url": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Reviewers assessed the messages before making the referral. Deputies identified and interviewed the user and made the charging decision." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0240" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "The Lee County Sheriff’s Office was the threatened institution and recipient of the tip. A wider safety benefit is unproved." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The Lee County Sheriff’s Office was the threatened institution and recipient of the tip. A wider safety benefit is unproved." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A user of Anthropic's AI chat service wrote at about 05:10 on 26 September 2026. She said she was going to “shoot up” the Lee County Sheriff's Office in Florida. At 01:07 on 27 September she wrote that she had got a new gun. Anthropic's safety monitoring flagged the messages, and because of their severity a human review team reported them to law enforcement. The sheriff's office logged the tip on 29 September and identified the user as Carli Michelle Heller, 30, of Bonita Springs. Deputies detained her at home without incident at 00:51 on 30 September. A detective charged her under Florida Statute 836.10, written threats of violence. No weapon was seized, and she described the AI as a personal diary.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Anthropic’s reviewers reported threatening messages to police, who identified and detained the user without incident. No weapon was found." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The Lee County Sheriff's Office, the threatened party, received a tip quoting a stated plan to shoot up its building and a claimed new gun. It identified the user and resolved the matter at her home within about three days without incident. No weapon was seized, so no averted attack is shown." }, { "group": "societal", "depth": 1, "reason": "A written threat of a mass shooting at a public building was examined within days rather than left unread. It was at least the third Anthropic referral to police since August 2026. The benefit is bounded by the absence of a weapon, the user's diary account, and expert doubt that the words were a true threat." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Anthropic's consumer terms (effective 8 October 2025) reserve the right “at our sole discretion” to report Inputs to law enforcement. Its privacy policy (effective 10 September 2026) allows disclosure the company believes “reasonably necessary” to “prevent serious harm to any person or to property”. The arrest report says the platform's safety measures are “designed to monitor for key phrases and potentially threatening content”. Flagging a stated shooting threat, reviewing it by hand and referring it to police was that purpose. Of an August 2026 referral Anthropic said “This is our safeguards process working as intended”." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Bonita Springs, United States", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Anthropic's consumer terms (effective 8 October 2025) reserve the right “at our sole discretion” to report Inputs to law enforcement. Its privacy policy (effective 10 September 2026) allows disclosure the company believes “reasonably necessary” to “prevent serious harm to any person or to property”. The arrest report says the platform's safety measures are “designed to monitor for key phrases and potentially threatening content”. Flagging a stated shooting threat, reviewing it by hand and referring it to police was that purpose. Of an August 2026 referral Anthropic said “This is our safeguards process working as intended”." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-09-29", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-10-04-dced", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Anthropic", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A vendor practice of screening consumer chats for threatening content and escalating severe cases to a human review team. That team can refer them to law enforcement, which Anthropic's consumer terms and privacy policy permit", "A tip carrying enough account information for the sheriff's office to identify a named resident within days", "A local agency that treated a stated threat against its own building as credible enough to send deputies the same night" ], "must_hold": [ "The referral must identify a real person and a real risk. The Lee County Sheriff's Office told reporters it does not count tips from Anthropic. No public figures exist on how often such referrals are made or prove unfounded", "The disclosure must stay within the emergency exception of the Stored Communications Act. An EFF attorney told Straight Arrow News “there is always the risk of over-reporting”, and the Monitor logs reputational and psychological harm to the user", "The response must remain proportionate. Here no weapon was found, and the user described a personal diary. Whether a chatbot prompt is a punishable threat under Florida Statute 836.10 is contested by the defense and by Eugene Volokh", "The attribution to Claude rests on press reports and the arrest report's phrase “Anthropic Artificial Intelligence”. Court records also note the user told the detective she used Perplexity, and her attorney declined to say which" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Reviewers need enough information to identify the person and assess a credible risk. Disclosure and police action need a lawful basis and a proportionate response." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-10-04-dced", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Anthropic; https://en.wikipedia.org/wiki/Anthropic", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Online safety and threat referral to law enforcement", "segment": "Media, social platforms, and marketing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "court document", "citation": "Lee County Sheriff's Office, Alejo, T. S. (2026). Arrest / Notice to Appear and Probable Cause Statement, agency report 26-530248, clerk case 26CF017549, Heller, Carli Michelle. Redacted copy hosted by the Daily Caller.", "title": "Arrest report and probable cause statement, LCSO report 26-530248 (Heller, Carli Michelle)", "url": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "date": "2026-09-30", "written_by": "beneficiary", "quote": "Due to the severity of the aforementioned statements, the information was escalated to a human review team, which subsequently reported the statements to law enforcement." }, { "kind": "news report", "citation": "WINK News, Alvarado, D. (2026). Woman arrested after AI threat against Lee County Sheriff's Office: Investigators. WINK News.", "title": "Woman arrested after AI threat against Lee County Sheriff's Office: Investigators", "url": "https://www.winknews.com/news/woman-arrested-after-ai-threat-against-lee-county-sheriffs-office-investigators/article_3d4c5915-7015-43c0-b86a-d7fa5eadf958.html", "date": "2026-09-30", "written_by": "independent", "quote": "Deputies identified Heller and went to her Bonita Springs home and she was detained without incident before an LCSO intelligence detective took over the investigation." }, { "kind": "news report", "citation": "The Daily Caller, Kresak, D. (2026). Florida Woman Nabbed After Chatbot Maker Flags Alleged Threat to 'Shoot Up' Sheriff's Office. The Daily Caller.", "title": "Florida Woman Nabbed After Chatbot Maker Flags Alleged Threat to 'Shoot Up' Sheriff's Office", "url": "https://dailycaller.com/2026/10/05/chatbot-maker-tips-off-cops-florida-woman-allegedly-threatens-sheriffs-office/", "date": "2026-10-05", "written_by": "independent", "quote": "When someone uses AI to make or facilitate a threat, we have to take that threat seriously." }, { "kind": "news report", "citation": "Straight Arrow News, Pavlou, D. (2026). A chatbot message led to a felony charge. Experts say it may not hold up. Straight Arrow News.", "title": "A chatbot message led to a felony charge. Experts say it may not hold up", "url": "https://san.com/cc/a-chatbot-message-led-to-a-felony-charge-experts-say-it-may-not-hold-up/", "date": "2026-10-08", "written_by": "independent", "quote": "She denied owning any firearms, and the arrest form indicates authorities didn’t seize a weapon." }, { "kind": "news report", "citation": "Tom's Hardware, Downing, S. (2026). Anthropic reports Florida woman’s Claude ‘diary’ threat to shoot up sheriff’s office, felony charge follows — it’s at least the third such conversation to reach police since August. Tom's Hardware.", "title": "Anthropic reports Florida woman’s Claude ‘diary’ threat to shoot up sheriff’s office, felony charge follows — it’s at least the third such conversation to reach police since August", "url": "https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropic-reports-florida-womans-claude-diary-threat-to-shoot-up-sheriffs-office-felony-charge-follows-its-at-least-the-third-such-conversation-to-reach-police-since-august", "date": "2026-10-05", "written_by": "independent", "quote": "court records listing the Sept. 30 felony charge under Florida Statute 836.10: written or electronic threat of a mass shooting or act of terrorism" }, { "kind": "other", "citation": "Volokh, E. (2026). Can a Chatbot Prompt Be a Punishable Threat? Reason, The Volokh Conspiracy.", "title": "Can a Chatbot Prompt Be a Punishable Threat?", "url": "https://reason.com/volokh/2026/10/08/can-a-chatbot-prompt-be-a-punishable-threat/", "date": "2026-10-08", "written_by": "independent", "quote": "Simply typing a prompt into a chatbot, expecting it to be viewed only by the software, wouldn't qualify." }, { "kind": "news report", "citation": "The San Francisco Standard, Jones, R. (2026). Anthropic called SFPD over threat against CEO. Claude user says it was a misunderstanding. The San Francisco Standard.", "title": "Anthropic called SFPD over threat against CEO. Claude user says it was a misunderstanding", "url": "https://sfstandard.com/2026/09/04/anthropic-threat-claude-sfpd/", "date": "2026-09-04", "written_by": "independent", "quote": "We banned this account, consistent with our standard practice, and referred the case to law enforcement. This is our safeguards process working as intended." }, { "kind": "other", "citation": "Anthropic (2026). Privacy Policy, effective 10 September 2026. Anthropic.", "title": "Privacy Policy", "url": "https://www.anthropic.com/legal/privacy", "date": "2026-09-10", "written_by": "makers", "quote": "(ii) prevent serious harm to any person or to property; (iii) detect, prevent, or otherwise address fraud or other illegal activity" }, { "kind": "other", "citation": "Anthropic (2025). Consumer Terms of Service, effective 8 October 2025. Anthropic.", "title": "Consumer Terms of Service", "url": "https://www.anthropic.com/legal/consumer-terms", "date": "2025-10-08", "written_by": "makers", "quote": "We reserve the right, at our sole discretion, to report information from or about you, including but not limited to Inputs, Outputs, or Actions to law enforcement." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0240-source-0001", "AIBD-0240-source-0002", "AIBD-0240-source-0003", "AIBD-0240-source-0004", "AIBD-0240-source-0005", "AIBD-0240-source-0006", "AIBD-0240-source-0007", "AIBD-0240-source-0008", "AIBD-0240-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "text", "text": "A sheriff’s detective wrote the arrest statement. It documents the referral and police response. It does not establish that an attack was averted, and no weapon was seized." }, "provenance": { "kind": "source_review", "basis": "The identified originals and their locators are listed in the source assessment.", "record_fields": [], "source_refs": [ "AIBD-0240-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A sheriff’s detective wrote the arrest statement. It documents the referral and police response. It does not establish that an attack was averted, and no weapon was seized." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0240-source-0001", "retained_source_order": [ "AIBD-0240-source-0001", "AIBD-0240-source-0002", "AIBD-0240-source-0003", "AIBD-0240-source-0004", "AIBD-0240-source-0005", "AIBD-0240-source-0006", "AIBD-0240-source-0007", "AIBD-0240-source-0008", "AIBD-0240-source-0009" ], "origins": [ { "url": "https://cdn01.dailycaller.com/wp-content/uploads/2026/10/26-530248_Heller__Carli_Redacted.pdf", "locator": "Arrest/probable-cause form, pp. 1–2; author signature, referral narrative, and weapons-seized field.", "contribution": "Firsthand account of the tip, review, detention, and absence of a seized weapon.", "relationship": "The claimed beneficiary’s police statement filed with the clerk, not a judicial determination.", "source_refs": [ "AIBD-0240-source-0001" ] } ], "excluded_or_consolidated_sources": [], "review_scope": "Whether the specified sources establish the reported benefit, their document types, and their authorship relationships. Other fields and sources were not comprehensively reassessed." } }, "provenance": { "kind": "source_review", "basis": "A limited source-role review; each inspected source and its contribution is identified.", "record_fields": [], "source_refs": [ "AIBD-0240-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A sheriff’s detective wrote the arrest statement. It documents the referral and police response. It does not establish that an attack was averted, and no weapon was seized." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Anthropic safeguards flagged a threat to shoot up a Florida sheriff's office; reviewers tipped police, who arrested the user; no gun found.", "account": "A user of Anthropic's AI chat service wrote at about 05:10 on 26 September 2026. She said she was going to “shoot up” the Lee County Sheriff's Office in Florida. At 01:07 on 27 September she wrote that she had got a new gun. Anthropic's safety monitoring flagged the messages, and because of their severity a human review team reported them to law enforcement. The sheriff's office logged the tip on 29 September and identified the user as Carli Michelle Heller, 30, of Bonita Springs. Deputies detained her at home without incident at 00:51 on 30 September. A detective charged her under Florida Statute 836.10, written threats of violence. No weapon was seized, and she described the AI as a personal diary." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Anthropic Claude consumer-chat safety monitoring: automated flagging of threatening content, escalation to a human review team, referral to law enforcement" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Anthropic" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Anthropic's threat monitoring flags a Bonita Springs woman's messages about shooting up the Lee County Sheriff's Office; human reviewers tip police, who detain her without incident and find no gun", "headline": "Anthropic flag on a shooting threat leads to a Florida arrest" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c87231c7cd74f35a314296f55b276a55c0341260a19641f61d8c311faf2b20c2" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A user of Anthropic's AI chat service wrote at about 05:10 on 26 September 2026. She said she was going to “shoot up” the Lee County Sheriff's Office in Florida. At 01:07 on 27 September she wrote that she had got a new gun. Anthropic's safety monitoring flagged the messages, and because of their severity a human review team reported them to law enforcement. The sheriff's office logged the tip on 29 September and identified the user as Carli Michelle Heller, 30, of Bonita Springs. Deputies detained her at home without incident at 00:51 on 30 September. A detective charged her under Florida Statute 836.10, written threats of violence. No weapon was seized, and she described the AI as a personal diary.", "replication": { "produced_by": [ "A vendor practice of screening consumer chats for threatening content and escalating severe cases to a human review team. That team can refer them to law enforcement, which Anthropic's consumer terms and privacy policy permit", "A tip carrying enough account information for the sheriff's office to identify a named resident within days", "A local agency that treated a stated threat against its own building as credible enough to send deputies the same night" ], "must_hold": [ "The referral must identify a real person and a real risk. The Lee County Sheriff's Office told reporters it does not count tips from Anthropic. No public figures exist on how often such referrals are made or prove unfounded", "The disclosure must stay within the emergency exception of the Stored Communications Act. An EFF attorney told Straight Arrow News “there is always the risk of over-reporting”, and the Monitor logs reputational and psychological harm to the user", "The response must remain proportionate. Here no weapon was found, and the user described a personal diary. Whether a chatbot prompt is a punishable threat under Florida Statute 836.10 is contested by the defense and by Eugene Volokh", "The attribution to Claude rests on press reports and the arrest report's phrase “Anthropic Artificial Intelligence”. Court records also note the user told the detective she used Perplexity, and her attorney declined to say which" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 2, "record_mapped": 30, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0258", "case_url": "records/AIBD-0258.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Crash Detection is built to recognize a severe vehicle crash from motion and sound signals and alert emergency services automatically." }, { "label": "AI contribution", "value": "The phone's crash algorithm sent the notification. The search located the wreckage the following day. The cited source does not say the alert's coordinates guided it or that anyone was rescued alive." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0258", "title": "A passenger's iPhone Crash Detection sends a “strong impact” alert to the local fire service before a sightseeing helicopter near Mount Aso goes missing, 20 January 2026", "summary": "SETN (21 Jan 2026, citing Japanese media): the helicopter took off at 10:52 on 20 Jan 2026 and lost contact at 11:04; the fire department had already received a collision notification from a passen...", "date_observed": "2026-01-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Taiwan's SETN reported on 21 Jan 2026, citing Japanese media, on a sightseeing helicopter operated by Aso Cadre Dominion. It carried a pilot in his sixties and two Taiwanese passengers (a 41-year-old man and a 36-year-old woman). It took off at 10:52 Japan time on 20 Jan 2026 and lost contact at 11:04. Before contact was lost, the local fire department received a “strong impact” notification from a passenger's phone, apparently the iPhone Crash Detection feature. Callbacks to the phone failed. An object believed to be the helicopter was found around the first crater of Mount Aso's Nakadake. The article does not state the fate of the three occupants. It does not cite a named Japanese agency. It quotes a firefighter and a nurse on crash detection in general. Apple's feature, on iPhone 14 and later, uses motion sensors and an on-device algorithm to call emergency services after a 10-second countdown. The OECD AI Incidents Monitor lists the case as a protective response. The link to transport safety is indirect. The device alerted responders to an aviation crash it did not prevent.", "category": "protective response", "unexpected": false, "purpose": "Crash Detection is built to recognize a severe vehicle crash from motion and sound signals and alert emergency services automatically.", "system": "Apple Crash Detection (iPhone 14 and later)", "model_version": "iPhone 14或後續機型（所有機型），且搭載iOS 16", "developer": "Apple", "deployer": "A helicopter passenger's iPhone", "location": null, "domain": "Aviation / emergency response", "segment": "Consumer devices / automatic crash alerting", "benefit_type": "Improved Physical Health", "taxonomy_note": "Crash notification to firefighters is read as a rescue; the occupants' outcome is not established.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "The fire service received an automatic impact alert before the aircraft was reported missing. The article does not quantify how it shaped the search." }, { "group": "personal", "depth": null, "reason": "The source does not state whether the occupants survived." }, { "group": "societal", "depth": null, "reason": "No wider outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "The phone's crash algorithm sent the notification. The search located the wreckage the following day. The cited source does not say the alert's coordinates guided it or that anyone was rescued alive." }, "evidence": [ { "kind": "news report", "citation": "SETN 三立新聞網 (21 Jan 2026). 阿蘇火山墜機！iPhone「內建1功能」10秒救命 沒訊號也可呼救.", "title": "阿蘇火山墜機！iPhone「內建1功能」10秒救命　沒訊號也可呼救", "url": "https://www.setn.com/News.aspx?NewsID=1784034", "date": "2026-01-21", "written_by": "independent", "quote": "當地消防局在直升機失聯前，有收到來自乘客的「強烈撞擊」通報" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-01-20-af0b: AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash.", "title": "AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash", "url": "https://oecd.ai/en/incidents/2026-01-20-af0b", "date": "2026-01-20", "written_by": "independent", "quote": "After a sightseeing helicopter crashed near Japan's Mount Aso, the iPhone crash detection feature automatically alerted emergency services, enabling rapid search and rescue." }, { "kind": "news report", "citation": "Bernama-Kyodo (18 Feb 2026). Three bodies found at helicopter crash site in southwest Japan volcanic crater. The Star.", "title": "Three bodies found at helicopter crash site in southwest Japan volcanic crater", "url": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater", "date": "2026-02-18", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A passenger carrying a crash-detection-capable phone", "An impact profile that met the algorithm's severity threshold", "A cellular or emergency-SOS path from a volcanic mountain area" ], "must_hold": [ "The alert carries usable coordinates", "Responders can reach the site quickly enough to change the outcome" ], "echoes": [ { "date": "2023-01-30", "setting": "OECD AIM aggregate entry on Apple Crash Detection producing both false alarms and emergency responses (registry listing only)", "model_version": null, "outcome": "unknown", "source": "https://oecd.ai/en/incidents/2023-01-30-10fc" } ] }, "paired_incident": null, "headline": "Apple Crash Detection alerts firefighters to a Mount Aso crash", "metric": null, "comparator": null, "later": { "status": "reversed", "date": "2026-02-18", "note": "The alert located the crash site but no one was rescued: on 18 February 2026 the fire department said bodies believed to be the three occupants had been found inside the crater, so the 'rapid rescue' framing of the first reports was not borne out.", "quote": "Bodies believed to be those of three people aboard a sightseeing helicopter that crashed into a volcanic crater on Mt. Aso in Kumamoto Prefecture, southwestern Japan, in January have been found, the local fire department said Wednesday (Feb 18).", "url": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater" }, "value": null, "developer_country": "United States", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater; https://www.setn.com/News.aspx?NewsID=1784034", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Apple_Inc.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0258", "catalog_version": "1.0.0", "source_fingerprint": "cf2ce059eb6055834dd91affee6c74d0f3f54577100c6540473d5adfc2dab536", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0258-source-0001", "AIBD-0258-source-0002", "AIBD-0258-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Apple", "deployer": "A helicopter passenger's iPhone" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The phone's crash algorithm sent the notification. The search located the wreckage the following day. The cited source does not say the alert's coordinates guided it or that anyone was rescued alive." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The phone's crash algorithm sent the notification. The search located the wreckage the following day. The cited source does not say the alert's coordinates guided it or that anyone was rescued alive." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The fire service received an automatic impact alert before the aircraft was reported missing. The article does not quantify how it shaped the search." }, { "group": "personal", "reason": "The source does not state whether the occupants survived." }, { "group": "societal", "reason": "No wider outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The fire service received an automatic impact alert before the aircraft was reported missing. The article does not quantify how it shaped the search." }, { "group": "personal", "reason": "The source does not state whether the occupants survived." }, { "group": "societal", "reason": "No wider outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The fire service received an automatic impact alert before the aircraft was reported missing. The article does not quantify how it shaped the search." }, { "group": "personal", "reason": "The source does not state whether the occupants survived." }, { "group": "societal", "reason": "No wider outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": 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"evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "reversed", "date": "2026-02-18", "note": "The alert located the crash site but no one was rescued: on 18 February 2026 the fire department said bodies believed to be the three occupants had been found inside the crater, so the 'rapid rescue' framing of the first reports was not borne out.", "quote": "Bodies believed to be those of three people aboard a sightseeing helicopter that crashed into a volcanic crater on Mt. Aso in Kumamoto Prefecture, southwestern Japan, in January have been found, the local fire department said Wednesday (Feb 18).", "url": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0258" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "Crash notification to firefighters is read as a rescue; the occupants' outcome is not established." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Taiwan's SETN reported on 21 Jan 2026, citing Japanese media, on a sightseeing helicopter operated by Aso Cadre Dominion. It carried a pilot in his sixties and two Taiwanese passengers (a 41-year-old man and a 36-year-old woman). It took off at 10:52 Japan time on 20 Jan 2026 and lost contact at 11:04. Before contact was lost, the local fire department received a “strong impact” notification from a passenger's phone, apparently the iPhone Crash Detection feature. Callbacks to the phone failed. An object believed to be the helicopter was found around the first crater of Mount Aso's Nakadake. The article does not state the fate of the three occupants. It does not cite a named Japanese agency. It quotes a firefighter and a nurse on crash detection in general. Apple's feature, on iPhone 14 and later, uses motion sensors and an on-device algorithm to call emergency services after a 10-second countdown. The OECD AI Incidents Monitor lists the case as a protective response. The link to transport safety is indirect. The device alerted responders to an aviation crash it did not prevent.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "The fire service received an automatic impact alert before the aircraft was reported missing. The article does not quantify how it shaped the search." }, { "group": "personal", "reason": "The source does not state whether the occupants survived." }, { "group": "societal", "reason": "No wider outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Crash Detection is built to recognize a severe vehicle crash from motion and sound signals and alert emergency services automatically." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Crash Detection is built to recognize a severe vehicle crash from motion and sound signals and alert emergency services automatically." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-01-20", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater; https://www.setn.com/News.aspx?NewsID=1784034", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Apple_Inc.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A passenger carrying a crash-detection-capable phone", "An impact profile that met the algorithm's severity threshold", "A cellular or emergency-SOS path from a volcanic mountain area" ], "must_hold": [ "The alert carries usable coordinates", "Responders can reach the site quickly enough to change the outcome" ], "echoes": [ { "date": "2023-01-30", "setting": "OECD AIM aggregate entry on Apple Crash Detection producing both false alarms and emergency responses (registry listing only)", "outcome": "unknown", "source": "https://oecd.ai/en/incidents/2023-01-30-10fc" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater; https://www.setn.com/News.aspx?NewsID=1784034", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Apple_Inc.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Aviation / emergency response", "segment": "Consumer devices / automatic crash alerting" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SETN 三立新聞網 (21 Jan 2026). 阿蘇火山墜機！iPhone「內建1功能」10秒救命 沒訊號也可呼救.", "title": "阿蘇火山墜機！iPhone「內建1功能」10秒救命　沒訊號也可呼救", "url": "https://www.setn.com/News.aspx?NewsID=1784034", "date": "2026-01-21", "written_by": "independent", "quote": "當地消防局在直升機失聯前，有收到來自乘客的「強烈撞擊」通報" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-01-20-af0b: AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash.", "title": "AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash", "url": "https://oecd.ai/en/incidents/2026-01-20-af0b", "date": "2026-01-20", "written_by": "independent", "quote": "After a sightseeing helicopter crashed near Japan's Mount Aso, the iPhone crash detection feature automatically alerted emergency services, enabling rapid search and rescue." }, { "kind": "news report", "citation": "Bernama-Kyodo (18 Feb 2026). Three bodies found at helicopter crash site in southwest Japan volcanic crater. The Star.", "title": "Three bodies found at helicopter crash site in southwest Japan volcanic crater", "url": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater", "date": "2026-02-18", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0258-source-0001", "AIBD-0258-source-0002", "AIBD-0258-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SETN 三立新聞網 (21 Jan 2026). 阿蘇火山墜機！iPhone「內建1功能」10秒救命 沒訊號也可呼救.", "title": "阿蘇火山墜機！iPhone「內建1功能」10秒救命　沒訊號也可呼救", "url": "https://www.setn.com/News.aspx?NewsID=1784034", "date": "2026-01-21", "written_by": "independent", "quote": "當地消防局在直升機失聯前，有收到來自乘客的「強烈撞擊」通報" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-01-20-af0b: AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash.", "title": "AI Crash Detection on Smartphones Enables Rapid Rescue After Japan Helicopter Crash", "url": "https://oecd.ai/en/incidents/2026-01-20-af0b", "date": "2026-01-20", "written_by": "independent", "quote": "After a sightseeing helicopter crashed near Japan's Mount Aso, the iPhone crash detection feature automatically alerted emergency services, enabling rapid search and rescue." }, { "kind": "news report", "citation": "Bernama-Kyodo (18 Feb 2026). Three bodies found at helicopter crash site in southwest Japan volcanic crater. The Star.", "title": "Three bodies found at helicopter crash site in southwest Japan volcanic crater", "url": "https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/18/three-bodies-found-at-helicopter-crash-site-in-southwest-japan-volcanic-crater", "date": "2026-02-18", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "SETN (21 Jan 2026, citing Japanese media): the helicopter took off at 10:52 on 20 Jan 2026 and lost contact at 11:04; the fire department had already received a collision notification from a passen...", "account": "Taiwan's SETN reported on 21 Jan 2026, citing Japanese media, on a sightseeing helicopter operated by Aso Cadre Dominion. It carried a pilot in his sixties and two Taiwanese passengers (a 41-year-old man and a 36-year-old woman). It took off at 10:52 Japan time on 20 Jan 2026 and lost contact at 11:04. Before contact was lost, the local fire department received a “strong impact” notification from a passenger's phone, apparently the iPhone Crash Detection feature. Callbacks to the phone failed. An object believed to be the helicopter was found around the first crater of Mount Aso's Nakadake. The article does not state the fate of the three occupants. It does not cite a named Japanese agency. It quotes a firefighter and a nurse on crash detection in general. Apple's feature, on iPhone 14 and later, uses motion sensors and an on-device algorithm to call emergency services after a 10-second countdown. The OECD AI Incidents Monitor lists the case as a protective response. The link to transport safety is indirect. The device alerted responders to an aviation crash it did not prevent." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The phone detected a possible collision and sent a notification." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The minimum requirements were an iPhone 14 or later and iOS 16 or later. They do not establish the device or installed release in this event." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "iPhone 14或後續機型（所有機型），且搭載iOS 16" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Apple Crash Detection (iPhone 14 and later)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Apple" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "A passenger's iPhone Crash Detection sends a “strong impact” alert to the local fire service before a sightseeing helicopter near Mount Aso goes missing, 20 January 2026", "headline": "Apple Crash Detection alerts firefighters to a Mount Aso crash" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "cf2ce059eb6055834dd91affee6c74d0f3f54577100c6540473d5adfc2dab536" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Taiwan's SETN reported on 21 Jan 2026, citing Japanese media, on a sightseeing helicopter operated by Aso Cadre Dominion. It carried a pilot in his sixties and two Taiwanese passengers (a 41-year-old man and a 36-year-old woman). It took off at 10:52 Japan time on 20 Jan 2026 and lost contact at 11:04. Before contact was lost, the local fire department received a “strong impact” notification from a passenger's phone, apparently the iPhone Crash Detection feature. Callbacks to the phone failed. An object believed to be the helicopter was found around the first crater of Mount Aso's Nakadake. The article does not state the fate of the three occupants. It does not cite a named Japanese agency. It quotes a firefighter and a nurse on crash detection in general. Apple's feature, on iPhone 14 and later, uses motion sensors and an on-device algorithm to call emergency services after a 10-second countdown. The OECD AI Incidents Monitor lists the case as a protective response. The link to transport safety is indirect. The device alerted responders to an aviation crash it did not prevent.", "replication": { "produced_by": [ "A passenger carrying a crash-detection-capable phone", "An impact profile that met the algorithm's severity threshold", "A cellular or emergency-SOS path from a volcanic mountain area" ], "must_hold": [ "The alert carries usable coordinates", "Responders can reach the site quickly enough to change the outcome" ], "echoes": [ { "date": "2023-01-30", "setting": "OECD AIM aggregate entry on Apple Crash Detection producing both false alarms and emergency responses (registry listing only)", "outcome": "unknown", "source": "https://oecd.ai/en/incidents/2023-01-30-10fc" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0207", "case_url": "records/AIBD-0207.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Sheng Siong rolled out its AI-driven facial recognition CCTV system in April 2024 as a measure against shoplifters. It worked with the Singapore Police Force under its Shop Theft for Retailers (STAR) program. In August 2025 it announced an extension to all 83 outlets. Staff upload the photograph of a suspect identified from footage. The system alerts store managers' phones when that person enters any branch. Sheng Siong said thefts per store rose from 4.91 in 2023 to 8 in 2024. Its CEO attributed the rise to catching cases the human eye had missed. Identifying a repeat shoplifter and flagging her return was exactly its designed purpose." }, { "label": "AI contribution", "value": "The system's documented contribution came after a human detection. On 10 September 2025 a staff member found discrepancies in a stock check. Colleagues reviewed CCTV and saw Tan leaving without paying. They uploaded her face. The Straits Times reports that the system “then automatically identified the other instances” of theft. That turned one observed theft into an amalgamated charge covering seven visits. On 11 September the system's alert reached the retail manager's phone when she entered. He intercepted her at the self-checkout kiosk with three unscanned bottles. The interception, the bag check, the police call, the prosecution and the restitution were human and institutional. No source names the vendor or the model, gives an accuracy figure, or reports a false match in this case." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Singapore" ] }, "record": { "id": "AIBD-0207", "title": "Sheng Siong's facial-recognition CCTV, given a shoplifter's face after a 10 September 2025 stock check at its Punggol Central outlet, matches her to six earlier wine thefts and alerts the manager's phone when she returns at noon the next day; staff stop her with three unscanned bottles, police arrest her, she repays the chain and is jailed eight days on 12 May 2026", "summary": "Sheng Siong's facial recognition matched a wine thief to six prior thefts and flagged her return; arrest, restitution, eight days' jail.", "date_observed": "2025-09-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Catherine Tan Li Eng, 51, took 19 Jacob's Creek wine bottles worth about S$556 from the Sheng Siong supermarket at Block 622D Punggol Central, Singapore. She bagged them unscanned at self-checkout from 2 to 11 September 2025. On 10 September staff found stock discrepancies, reviewed closed-circuit television (CCTV) and uploaded her face to the chain's AI-driven facial recognition system. It automatically identified the earlier thefts. When she returned at noon on 11 September the retail manager got a phone alert. He stopped her at the self-checkout kiosk and found three unscanned bottles. Police arrested her. On 12 May 2026 District Judge Brenda Chua jailed her for eight days on an amalgamated theft charge after full restitution of about S$470.", "category": "benefit created", "unexpected": false, "purpose": "Sheng Siong rolled out its AI-driven facial recognition CCTV system in April 2024 as a measure against shoplifters. It worked with the Singapore Police Force under its Shop Theft for Retailers (STAR) program. In August 2025 it announced an extension to all 83 outlets. Staff upload the photograph of a suspect identified from footage. The system alerts store managers' phones when that person enters any branch. Sheng Siong said thefts per store rose from 4.91 in 2023 to 8 in 2024. Its CEO attributed the rise to catching cases the human eye had missed. Identifying a repeat shoplifter and flagging her return was exactly its designed purpose.", "system": "Sheng Siong's AI-driven facial recognition closed-circuit television system: a suspect's photograph, taken from CCTV footage after a theft is found, is uploaded, the system matches the face across footage and sends mobile alerts through a corporate app to staff when that person enters any Sheng Siong outlet", "model_version": null, "developer": null, "deployer": "Sheng Siong (supermarket chain, 83 outlets), Singapore", "location": "Punggol, Singapore", "domain": "Retail loss prevention and law enforcement", "segment": "Supermarket shop-theft detection: facial recognition on store CCTV with mobile alerts to managers", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-05-12-21c2" }, "ratings": [ { "group": "organizational", "depth": 1, "reason": "Sheng Siong's gain was small and specific. It kept three bottles on 11 September 2025. It got full restitution of about S$470 for the rest of the 19 bottles (about S$556 in all). A seven-visit theft series at one outlet ended. The amount is minor for an 83-outlet chain. The first detection was a human stock check and a manual review of CCTV, not the system." } ], "ai_role": { "status": "assessed", "statement": "The system's documented contribution came after a human detection. On 10 September 2025 a staff member found discrepancies in a stock check. Colleagues reviewed CCTV and saw Tan leaving without paying. They uploaded her face. The Straits Times reports that the system “then automatically identified the other instances” of theft. That turned one observed theft into an amalgamated charge covering seven visits. On 11 September the system's alert reached the retail manager's phone when she entered. He intercepted her at the self-checkout kiosk with three unscanned bottles. The interception, the bag check, the police call, the prosecution and the restitution were human and institutional. No source names the vendor or the model, gives an accuracy figure, or reports a false match in this case." }, "evidence": [ { "kind": "news report", "citation": "Lam L / CNA (2026). Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine. Channel NewsAsia, 12 May 2026 (updated 14 May 2026).", "title": "Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "date": "2026-05-12", "written_by": "independent", "quote": "The retail manager was at the premises when he received an alert on his mobile phone" }, { "kind": "news report", "citation": "Sun D / The Straits Times (2026). Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system. The Straits Times, 12 May 2026 (updated 13 May 2026).", "title": "Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system", "url": "https://www.straitstimes.com/singapore/courts-crime/woman-who-stole-19-bottles-of-wine-from-sheng-shiong-caught-using-facial-recognition-system", "date": "2026-05-12", "written_by": "independent", "quote": "They used the footage to flag her face in the facial recognition system, which then automatically identified the other instances" }, { "kind": "news report", "citation": "Khoo Y-H / AsiaOne (2026). Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail. AsiaOne, 13 May 2026.", "title": "Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail", "url": "https://www.asiaone.com/singapore/woman-caught-sheng-siongs-facial-recognition-system-stealing-19-bottles-wine-get-8-days", "date": "2026-05-13", "written_by": "independent", "quote": "they were able to prevent her from stealing another three bottles of wine" }, { "kind": "news report", "citation": "Lay B / Mothership (2025). Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises. Mothership, 26 August 2025.", "title": "Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises", "url": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html", "date": "2025-08-26", "written_by": "independent", "quote": "It is deployed in collaboration with the Singapore Police Force (SPF) under its Shop Theft for Retailers (STAR) Programme" } ], "replication": { "produced_by": [ "A chain-wide facial recognition layer on existing CCTV (rolled out from April 2024, extended to all 83 outlets after August 2025). Staff upload a suspect's face, and managers get app alerts when that person re-enters any branch.", "A human trigger: a stock check on 10 September 2025 that revealed missing bottles. Staff then reviewed the footage and uploaded the face the same day.", "A repeat offender who came back to the same outlet at noon the next day, which gave the alert something to fire on.", "A manager on the premises with his phone, able to intercept at the self-checkout kiosk. A police and court process (the STAR program, an amalgamated theft charge) that turned the identification into restitution and a sentence." ], "must_hold": [ "The system identifies, it does not detect. Stock counts or staff still have to notice the first theft. Sheng Siong's CEO implied as much when he said the human eye had missed cases.", "Someone must act on the alert within minutes. The gain here was an interception before she left the store, not the alert itself.", "The match must be right and lawful. A wrong face flagged at the door would produce a wrongful stop. No source reports a false match in this case. None names the vendor or any accuracy figure." ], "echoes": [ { "date": null, "setting": "Malay Mail, 9 October 2026 (AsiaOne report): Leong Seek Yuen, 45, jailed one week after Sheng Siong's facial recognition system alerted staff when she re-entered its Sin Ming Avenue outlet on 1 October 2025, following twelve thefts of meat and drinks worth about S$90 between 27 May and 12 August 2025; as here, an assistant supervisor had first found stock discrepancies and checked CCTV. https://www.malaymail.com/news/singapore/2026/10/09/singapore-supermarkets-facial-recognition-system-catches-woman-after-12-shoplifting-trips/238387", "model_version": null, "outcome": "unknown", "source": null } ] }, "paired_incident": null, "headline": "Sheng Siong's facial recognition catches a repeat wine thief", "metric": "matched a wine thief to six prior thefts and flagged her return", "comparator": { "against": "the chain's previous practice of staff manually reviewing CCTV footage to identify suspects", "before": "staff members had to manually review CCTV footage to identify suspects", "after": "staff receive mobile alerts through a corporate app once a flagged person enters any Sheng Siong branch again", "basis": "assumed", "quote": "Staff would receive mobile alerts through a corporate app once the same person enters any Sheng Siong branch again. Staff may approach the person and file a police report if they are sure the person has committed shop theft. In the past, staff members had to manually review CCTV footage to identify suspects.", "url": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html" }, "later": { "status": "litigated", "date": "2026-05-12", "note": "The shoplifter pleaded guilty to an amalgamated theft charge and was jailed for eight days on 12 May 2026 after making full restitution of about S$470.", "quote": "A shoplifter who was nabbed after being identified by supermarket chain Sheng Siong's facial recognition technology was sentenced to eight days' jail on Tuesday (May 12).", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" }, "value": { "amount": "about S$470", "what": "restitution paid by the shoplifter for the stolen bottles that were not recovered (three bottles were recovered at the interception)", "who": "Sheng Siong", "estimate": false, "quote": "The retail manager called the police, and the three bottles of wine were recovered. Tan also made full restitution of about S$470 for the remaining stolen bottles.", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" }, "developer_country": null, "deployer_country": "Singapore", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 11 September 2025, the day the system's alert led to the interception and arrest; the Monitor's 12 May 2026 is the sentencing and reporting date. Filed as benefit created or restored (identification, arrest and restitution for an identifiable organization); the 11 September interception with three unscanned bottles could be read as a small specific harm prevented, but the record's benefit is the identification of the series and the recovery of the loss. Values differ slightly by outlet: CNA about S$556, The Straits Times over $550, AsiaOne over $555; restitution is “about S$470” in CNA and “full restitution” in all three, consistent with the three bottles recovered on 11 September. No source names the vendor of the facial recognition system or a model, so developer and the model version are null; the system's design and the STAR program come from Mothership's 26 August 2025 report of CNA's rollout story, as CNA's own rollout article returned 404 at the URL found and the Yahoo syndication 404 as well. CNA and The Straits Times refused direct fetches and were read through the r.jina.ai text proxy; The Straits Times headline now reads “...caught by Sheng Siong's facial recognition system” while its URL slug and the Monitor's title read “...from Sheng Shiong caught using facial recognition system”. Malay Mail's 12 May 2026 syndication of David Sun's Straits Times piece was read once and returned 403 on re-fetch, so it is not cited; STOMP and The Independent Singapore returned 403. AsiaOne credits The Straits Times and CNA. No source discusses the PDPA or privacy aspects of the system; the record notes that the first detection was human. The October 2026 echo was found through Bing after WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged.", "source": "https://oecd.ai/en/incidents/2026-05-12-21c2", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Sheng_Siong", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0207", "catalog_version": "1.0.0", "source_fingerprint": "692dc2995f185a3da07c3ae1563b1e95acecd31c39a5109e2349ac66fb973a9b", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0207-source-0001", "AIBD-0207-source-0002", "AIBD-0207-source-0003", "AIBD-0207-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0207-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Sheng Siong (supermarket chain, 83 outlets), Singapore" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system's documented contribution came after a human detection. On 10 September 2025 a staff member found discrepancies in a stock check. Colleagues reviewed CCTV and saw Tan leaving without paying. They uploaded her face. The Straits Times reports that the system “then automatically identified the other instances” of theft. That turned one observed theft into an amalgamated charge covering seven visits. On 11 September the system's alert reached the retail manager's phone when she entered. He intercepted her at the self-checkout kiosk with three unscanned bottles. The interception, the bag check, the police call, the prosecution and the restitution were human and institutional. No source names the vendor or the model, gives an accuracy figure, or reports a false match in this case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "After staff identified a theft, facial-recognition software found footage of earlier visits and alerted a manager when the same person returned to the store.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The system's documented contribution came after a human detection. On 10 September 2025 a staff member found discrepancies in a stock check. Colleagues reviewed CCTV and saw Tan leaving without paying. They uploaded her face. The Straits Times reports that the system “then automatically identified the other instances” of theft. That turned one observed theft into an amalgamated charge covering seven visits. On 11 September the system's alert reached the retail manager's phone when she entered. He intercepted her at the self-checkout kiosk with three unscanned bottles. The interception, the bag check, the police call, the prosecution and the restitution were human and institutional. No source names the vendor or the model, gives an accuracy figure, or reports a false match in this case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Sheng Siong's gain was small and specific. It kept three bottles on 11 September 2025. It got full restitution of about S$470 for the rest of the 19 bottles (about S$556 in all). A seven-visit theft series at one outlet ended. The amount is minor for an 83-outlet chain. The first detection was a human stock check and a manual review of CCTV, not the system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Sheng Siong's gain was small and specific. It kept three bottles on 11 September 2025. It got full restitution of about S$470 for the rest of the 19 bottles (about S$556 in all). A seven-visit theft series at one outlet ended. The amount is minor for an 83-outlet chain. The first detection was a human stock check and a manual review of CCTV, not the system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The store stopped three bottles from being taken and received about S$470 in restitution for earlier thefts. The full series involved 19 bottles worth about S$556. Staff first noticed the missing stock and reviewed the footage, so the documented AI contribution was recognizing the suspect’s return." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Sheng Siong's gain was small and specific. It kept three bottles on 11 September 2025. It got full restitution of about S$470 for the rest of the 19 bottles (about S$556 in all). A seven-visit theft series at one outlet ended. The amount is minor for an 83-outlet chain. The first detection was a human stock check and a manual review of CCTV, not the system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "about S$470", "what": "restitution paid by the shoplifter for the stolen bottles that were not recovered (three bottles were recovered at the interception)", "who": "Sheng Siong", "estimate": false, "quote": "The retail manager called the police, and the three bottles of wine were recovered. Tan also made full restitution of about S$470 for the remaining stolen bottles.", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "about S$470", "what": "restitution paid by the shoplifter for the stolen bottles that were not recovered (three bottles were recovered at the interception)", "who": "Sheng Siong", "estimate": false, "quote": "The retail manager called the police, and the three bottles of wine were recovered. Tan also made full restitution of about S$470 for the remaining stolen bottles.", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "about S$470", "what": "restitution paid by the shoplifter for the stolen bottles that were not recovered (three bottles were recovered at the interception)", "who": "Sheng Siong", "estimate": false, "quote": "The retail manager called the police, and the three bottles of wine were recovered. Tan also made full restitution of about S$470 for the remaining stolen bottles.", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The alert reached the manager when the suspect returned the next day. Staff stopped three unscanned bottles, and the retailer later received about S$470 in restitution. No controlled comparison with manual surveillance is reported." } }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Bottles recovered, restitution paid, and the interval between identifying the suspect and the next alert." } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The sources do not identify the vendor or model and provide no accuracy measure. They do not report a false match in this instance.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-05-12-21c2" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "litigated", "date": "2026-05-12", "note": "The shoplifter pleaded guilty to an amalgamated theft charge and was jailed for eight days on 12 May 2026 after making full restitution of about S$470.", "quote": "A shoplifter who was nabbed after being identified by supermarket chain Sheng Siong's facial recognition technology was sentenced to eight days' jail on Tuesday (May 12).", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Staff found the initial discrepancy, reviewed the footage, and uploaded the photograph. A manager intercepted the customer and contacted the police." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0207" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Sheng Siong’s Punggol Central store." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Sheng Siong’s Punggol Central store." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Catherine Tan Li Eng, 51, took 19 Jacob's Creek wine bottles worth about S$556 from the Sheng Siong supermarket at Block 622D Punggol Central, Singapore. She bagged them unscanned at self-checkout from 2 to 11 September 2025. On 10 September staff found stock discrepancies, reviewed closed-circuit television (CCTV) and uploaded her face to the chain's AI-driven facial recognition system. It automatically identified the earlier thefts. When she returned at noon on 11 September the retail manager got a phone alert. He stopped her at the self-checkout kiosk and found three unscanned bottles. Police arrested her. On 12 May 2026 District Judge Brenda Chua jailed her for eight days on an amalgamated theft charge after full restitution of about S$470.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Sheng Siong recovered the value of stolen wine and stopped three further bottles from leaving the store." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Sheng Siong's gain was small and specific. It kept three bottles on 11 September 2025. It got full restitution of about S$470 for the rest of the 19 bottles (about S$556 in all). A seven-visit theft series at one outlet ended. The amount is minor for an 83-outlet chain. The first detection was a human stock check and a manual review of CCTV, not the system." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Sheng Siong rolled out its AI-driven facial recognition CCTV system in April 2024 as a measure against shoplifters. It worked with the Singapore Police Force under its Shop Theft for Retailers (STAR) program. In August 2025 it announced an extension to all 83 outlets. Staff upload the photograph of a suspect identified from footage. The system alerts store managers' phones when that person enters any branch. Sheng Siong said thefts per store rose from 4.91 in 2023 to 8 in 2024. Its CEO attributed the rise to catching cases the human eye had missed. Identifying a repeat shoplifter and flagging her return was exactly its designed purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Punggol, Singapore", "deployer_country": "Singapore" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Sheng Siong rolled out its AI-driven facial recognition CCTV system in April 2024 as a measure against shoplifters. It worked with the Singapore Police Force under its Shop Theft for Retailers (STAR) program. In August 2025 it announced an extension to all 83 outlets. Staff upload the photograph of a suspect identified from footage. The system alerts store managers' phones when that person enters any branch. Sheng Siong said thefts per store rose from 4.91 in 2023 to 8 in 2024. Its CEO attributed the rise to catching cases the human eye had missed. Identifying a repeat shoplifter and flagging her return was exactly its designed purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-09-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 11 September 2025, the day the system's alert led to the interception and arrest; the Monitor's 12 May 2026 is the sentencing and reporting date. Filed as benefit created or restored (identification, arrest and restitution for an identifiable organization); the 11 September interception with three unscanned bottles could be read as a small specific harm prevented, but the record's benefit is the identification of the series and the recovery of the loss. Values differ slightly by outlet: CNA about S$556, The Straits Times over $550, AsiaOne over $555; restitution is “about S$470” in CNA and “full restitution” in all three, consistent with the three bottles recovered on 11 September. No source names the vendor of the facial recognition system or a model, so developer and the model version are null; the system's design and the STAR program come from Mothership's 26 August 2025 report of CNA's rollout story, as CNA's own rollout article returned 404 at the URL found and the Yahoo syndication 404 as well. CNA and The Straits Times refused direct fetches and were read through the r.jina.ai text proxy; The Straits Times headline now reads “...caught by Sheng Siong's facial recognition system” while its URL slug and the Monitor's title read “...from Sheng Shiong caught using facial recognition system”. Malay Mail's 12 May 2026 syndication of David Sun's Straits Times piece was read once and returned 403 on re-fetch, so it is not cited; STOMP and The Independent Singapore returned 403. AsiaOne credits The Straits Times and CNA. No source discusses the PDPA or privacy aspects of the system; the record notes that the first detection was human. The October 2026 echo was found through Bing after WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged.", "source": "https://oecd.ai/en/incidents/2026-05-12-21c2", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Sheng_Siong", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A chain-wide facial recognition layer on existing CCTV (rolled out from April 2024, extended to all 83 outlets after August 2025). Staff upload a suspect's face, and managers get app alerts when that person re-enters any branch.", "A human trigger: a stock check on 10 September 2025 that revealed missing bottles. Staff then reviewed the footage and uploaded the face the same day.", "A repeat offender who came back to the same outlet at noon the next day, which gave the alert something to fire on.", "A manager on the premises with his phone, able to intercept at the self-checkout kiosk. A police and court process (the STAR program, an amalgamated theft charge) that turned the identification into restitution and a sentence." ], "must_hold": [ "The system identifies, it does not detect. Stock counts or staff still have to notice the first theft. Sheng Siong's CEO implied as much when he said the human eye had missed cases.", "Someone must act on the alert within minutes. The gain here was an interception before she left the store, not the alert itself.", "The match must be right and lawful. A wrong face flagged at the door would produce a wrongful stop. No source reports a false match in this case. None names the vendor or any accuracy figure." ], "echoes": [ { "setting": "Malay Mail, 9 October 2026 (AsiaOne report): Leong Seek Yuen, 45, jailed one week after Sheng Siong's facial recognition system alerted staff when she re-entered its Sin Ming Avenue outlet on 1 October 2025, following twelve thefts of meat and drinks worth about S$90 between 27 May and 12 August 2025; as here, an assistant supervisor had first found stock discrepancies and checked CCTV. https://www.malaymail.com/news/singapore/2026/10/09/singapore-supermarkets-facial-recognition-system-catches-woman-after-12-shoplifting-trips/238387", "outcome": "unknown" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A chain-wide facial recognition layer on existing CCTV (rolled out from April 2024, extended to all 83 outlets after August 2025). Staff upload a suspect's face, and managers get app alerts when that person re-enters any branch.", "A human trigger: a stock check on 10 September 2025 that revealed missing bottles. Staff then reviewed the footage and uploaded the face the same day.", "A repeat offender who came back to the same outlet at noon the next day, which gave the alert something to fire on.", "A manager on the premises with his phone, able to intercept at the self-checkout kiosk. A police and court process (the STAR program, an amalgamated theft charge) that turned the identification into restitution and a sentence." ], "must_hold": [ "The system identifies, it does not detect. Stock counts or staff still have to notice the first theft. Sheng Siong's CEO implied as much when he said the human eye had missed cases.", "Someone must act on the alert within minutes. The gain here was an interception before she left the store, not the alert itself.", "The match must be right and lawful. A wrong face flagged at the door would produce a wrongful stop. No source reports a false match in this case. None names the vendor or any accuracy figure." ], "echoes": [ { "setting": "Malay Mail, 9 October 2026 (AsiaOne report): Leong Seek Yuen, 45, jailed one week after Sheng Siong's facial recognition system alerted staff when she re-entered its Sin Ming Avenue outlet on 1 October 2025, following twelve thefts of meat and drinks worth about S$90 between 27 May and 12 August 2025; as here, an assistant supervisor had first found stock discrepancies and checked CCTV. https://www.malaymail.com/news/singapore/2026/10/09/singapore-supermarkets-facial-recognition-system-catches-woman-after-12-shoplifting-trips/238387", "outcome": "unknown" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Staff must first identify a suspected theft and supply the relevant image. A manager must then verify the match and act before the person leaves. The identification and stop need to be lawful." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. the date observed is 11 September 2025, the day the system's alert led to the interception and arrest; the Monitor's 12 May 2026 is the sentencing and reporting date. Filed as benefit created or restored (identification, arrest and restitution for an identifiable organization); the 11 September interception with three unscanned bottles could be read as a small specific harm prevented, but the record's benefit is the identification of the series and the recovery of the loss. Values differ slightly by outlet: CNA about S$556, The Straits Times over $550, AsiaOne over $555; restitution is “about S$470” in CNA and “full restitution” in all three, consistent with the three bottles recovered on 11 September. No source names the vendor of the facial recognition system or a model, so developer and the model version are null; the system's design and the STAR program come from Mothership's 26 August 2025 report of CNA's rollout story, as CNA's own rollout article returned 404 at the URL found and the Yahoo syndication 404 as well. CNA and The Straits Times refused direct fetches and were read through the r.jina.ai text proxy; The Straits Times headline now reads “...caught by Sheng Siong's facial recognition system” while its URL slug and the Monitor's title read “...from Sheng Shiong caught using facial recognition system”. Malay Mail's 12 May 2026 syndication of David Sun's Straits Times piece was read once and returned 403 on re-fetch, so it is not cited; STOMP and The Independent Singapore returned 403. AsiaOne credits The Straits Times and CNA. No source discusses the PDPA or privacy aspects of the system; the record notes that the first detection was human. The October 2026 echo was found through Bing after WebSearch was exhausted and Brave and DuckDuckGo rate-limited or challenged.", "source": "https://oecd.ai/en/incidents/2026-05-12-21c2", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421; https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Sheng_Siong", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Retail loss prevention and law enforcement", "segment": "Supermarket shop-theft detection: facial recognition on store CCTV with mobile alerts to managers" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Lam L / CNA (2026). Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine. Channel NewsAsia, 12 May 2026 (updated 14 May 2026).", "title": "Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "date": "2026-05-12", "written_by": "independent", "quote": "The retail manager was at the premises when he received an alert on his mobile phone" }, { "kind": "news report", "citation": "Sun D / The Straits Times (2026). Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system. The Straits Times, 12 May 2026 (updated 13 May 2026).", "title": "Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system", "url": "https://www.straitstimes.com/singapore/courts-crime/woman-who-stole-19-bottles-of-wine-from-sheng-shiong-caught-using-facial-recognition-system", "date": "2026-05-12", "written_by": "independent", "quote": "They used the footage to flag her face in the facial recognition system, which then automatically identified the other instances" }, { "kind": "news report", "citation": "Khoo Y-H / AsiaOne (2026). Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail. AsiaOne, 13 May 2026.", "title": "Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail", "url": "https://www.asiaone.com/singapore/woman-caught-sheng-siongs-facial-recognition-system-stealing-19-bottles-wine-get-8-days", "date": "2026-05-13", "written_by": "independent", "quote": "they were able to prevent her from stealing another three bottles of wine" }, { "kind": "news report", "citation": "Lay B / Mothership (2025). Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises. Mothership, 26 August 2025.", "title": "Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises", "url": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html", "date": "2025-08-26", "written_by": "independent", "quote": "It is deployed in collaboration with the Singapore Police Force (SPF) under its Shop Theft for Retailers (STAR) Programme" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0207-source-0001", "AIBD-0207-source-0002", "AIBD-0207-source-0003", "AIBD-0207-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Lam L / CNA (2026). Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine. Channel NewsAsia, 12 May 2026 (updated 14 May 2026).", "title": "Shoplifter identified by Sheng Siong's facial recognition technology gets jail for stealing wine", "url": "https://www.channelnewsasia.com/singapore/shoplifter-caught-sheng-siong-facial-recognition-tech-jail-6115421", "date": "2026-05-12", "written_by": "independent", "quote": "The retail manager was at the premises when he received an alert on his mobile phone" }, { "kind": "news report", "citation": "Sun D / The Straits Times (2026). Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system. The Straits Times, 12 May 2026 (updated 13 May 2026).", "title": "Woman who stole 19 bottles of wine caught by Sheng Siong's facial recognition system", "url": "https://www.straitstimes.com/singapore/courts-crime/woman-who-stole-19-bottles-of-wine-from-sheng-shiong-caught-using-facial-recognition-system", "date": "2026-05-12", "written_by": "independent", "quote": "They used the footage to flag her face in the facial recognition system, which then automatically identified the other instances" }, { "kind": "news report", "citation": "Khoo Y-H / AsiaOne (2026). Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail. AsiaOne, 13 May 2026.", "title": "Woman caught by Sheng Siong's facial recognition system stealing 19 bottles of wine gets 8 days' jail", "url": "https://www.asiaone.com/singapore/woman-caught-sheng-siongs-facial-recognition-system-stealing-19-bottles-wine-get-8-days", "date": "2026-05-13", "written_by": "independent", "quote": "they were able to prevent her from stealing another three bottles of wine" }, { "kind": "news report", "citation": "Lay B / Mothership (2025). Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises. Mothership, 26 August 2025.", "title": "Sheng Siong installs face recognition CCTV at 83 outlets to catch shoplifters who re-enter premises", "url": "https://mothership.sg/2025/08/sheng-siong-face-recognition-cctv/index.html", "date": "2025-08-26", "written_by": "independent", "quote": "It is deployed in collaboration with the Singapore Police Force (SPF) under its Shop Theft for Retailers (STAR) Programme" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Sheng Siong's facial recognition matched a wine thief to six prior thefts and flagged her return; arrest, restitution, eight days' jail.", "account": "Catherine Tan Li Eng, 51, took 19 Jacob's Creek wine bottles worth about S$556 from the Sheng Siong supermarket at Block 622D Punggol Central, Singapore. She bagged them unscanned at self-checkout from 2 to 11 September 2025. On 10 September staff found stock discrepancies, reviewed closed-circuit television (CCTV) and uploaded her face to the chain's AI-driven facial recognition system. It automatically identified the earlier thefts. When she returned at noon on 11 September the retail manager got a phone alert. He stopped her at the self-checkout kiosk and found three unscanned bottles. Police arrested her. On 12 May 2026 District Judge Brenda Chua jailed her for eight days on an amalgamated theft charge after full restitution of about S$470." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Sheng Siong's AI-driven facial recognition closed-circuit television system: a suspect's photograph, taken from CCTV footage after a theft is found, is uploaded, the system matches the face across footage and sends mobile alerts through a corporate app to staff when that person enters any Sheng Siong outlet" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Sheng Siong's facial-recognition CCTV, given a shoplifter's face after a 10 September 2025 stock check at its Punggol Central outlet, matches her to six earlier wine thefts and alerts the manager's phone when she returns at noon the next day; staff stop her with three unscanned bottles, police arrest her, she repays the chain and is jailed eight days on 12 May 2026", "headline": "Sheng Siong's facial recognition catches a repeat wine thief" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "692dc2995f185a3da07c3ae1563b1e95acecd31c39a5109e2349ac66fb973a9b" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Catherine Tan Li Eng, 51, took 19 Jacob's Creek wine bottles worth about S$556 from the Sheng Siong supermarket at Block 622D Punggol Central, Singapore. She bagged them unscanned at self-checkout from 2 to 11 September 2025. On 10 September staff found stock discrepancies, reviewed closed-circuit television (CCTV) and uploaded her face to the chain's AI-driven facial recognition system. It automatically identified the earlier thefts. When she returned at noon on 11 September the retail manager got a phone alert. He stopped her at the self-checkout kiosk and found three unscanned bottles. Police arrested her. On 12 May 2026 District Judge Brenda Chua jailed her for eight days on an amalgamated theft charge after full restitution of about S$470.", "replication": { "produced_by": [ "A chain-wide facial recognition layer on existing CCTV (rolled out from April 2024, extended to all 83 outlets after August 2025). Staff upload a suspect's face, and managers get app alerts when that person re-enters any branch.", "A human trigger: a stock check on 10 September 2025 that revealed missing bottles. Staff then reviewed the footage and uploaded the face the same day.", "A repeat offender who came back to the same outlet at noon the next day, which gave the alert something to fire on.", "A manager on the premises with his phone, able to intercept at the self-checkout kiosk. A police and court process (the STAR program, an amalgamated theft charge) that turned the identification into restitution and a sentence." ], "must_hold": [ "The system identifies, it does not detect. Stock counts or staff still have to notice the first theft. Sheng Siong's CEO implied as much when he said the human eye had missed cases.", "Someone must act on the alert within minutes. The gain here was an interception before she left the store, not the alert itself.", "The match must be right and lawful. A wrong face flagged at the door would produce a wrongful stop. No source reports a false match in this case. None names the vendor or any accuracy figure." ], "echoes": [ { "setting": "Malay Mail, 9 October 2026 (AsiaOne report): Leong Seek Yuen, 45, jailed one week after Sheng Siong's facial recognition system alerted staff when she re-entered its Sin Ming Avenue outlet on 1 October 2025, following twelve thefts of meat and drinks worth about S$90 between 27 May and 12 August 2025; as here, an assistant supervisor had first found stock discrepancies and checked CCTV. https://www.malaymail.com/news/singapore/2026/10/09/singapore-supermarkets-facial-recognition-system-catches-woman-after-12-shoplifting-trips/238387", "outcome": "unknown" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "A stock check that revealed missing wine led staff to review camera footage and upload the suspect’s face. The system sent an alert that reached the manager when she returned the next day. Staff stopped her at the checkout. Police and court proceedings that followed led to restitution and an eight-day sentence." } } }, "summary": { "total": 91, "addressed": 91, "known": 34, "reviewed": 0, "record_mapped": 34, "not_assessed": 57, "complete": false, "by_state": { "value_present": 34, "not_reported": 0, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0253", "case_url": "records/AIBD-0253.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The system is built to detect the first seismic waves and automatically slow or stop high-speed trains before stronger shaking arrives." }, { "label": "AI contribution", "value": "The software evaluated the seismic signals and triggered the stop protocol without human intervention, per euronews. The sources are two Italian news reports of the company's announcement. No RFI document is among the sources and the algorithm is not described." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0253", "title": "RFI's seismic early-warning system on the Rome-Naples high-speed line, operational since 3 May 2025, stops trains preventively during a magnitude 4.4 earthquake", "summary": "euronews (15 Jul 2025): about 20 accelerometric stations over 200 km feed software that analyzes P-waves and automatically slows or stops trains; in the weeks after activation a magnitude 4.4 quake...", "date_observed": "2025-07-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "ANSA reported on 14 Jul 2025 that Ferrovie dello Stato's infrastructure company Rete Ferroviaria Italiana had activated Italy's first seismic early-warning system. It is on the Rome-Naples high-speed line. It was developed with the Physics Department of the University of Naples Federico II. Technical support came from the Railway Technical Research Institute of the Japanese railways. A network of latest-generation sensors detects the earliest seismic waves. Within seconds it automatically activates safety protocols to slow or stop trains. euronews (15 Jul 2025) added that the system comprises about 20 seismic stations along 200 km, on the surface and in deep boreholes. They are linked by an SDH fiber network. An advanced software intelligence instantly analyzes the data and assesses the quake's danger, per euronews. The system had been operational since 3 May 2025. In the following weeks it was tested by a magnitude 4.4 earthquake. During it, trains on the Rome-Naples line were stopped preventively. Neither article gives the date of that earthquake. ANSA does not mention artificial intelligence. The OECD AI Incidents Monitor lists the system as AI-powered and a protective response.", "category": "protective response", "unexpected": false, "purpose": "The system is built to detect the first seismic waves and automatically slow or stop high-speed trains before stronger shaking arrives.", "system": "RFI seismic early-warning system (Early Warning Sismico) on the Rome-Naples high-speed line", "model_version": null, "developer": "Rete Ferroviaria Italiana with the Physics Department of the University of Naples Federico II and JR RTRI", "deployer": "Rete Ferroviaria Italiana (Gruppo FS)", "location": null, "domain": "Railway safety", "segment": "High-speed rail / earthquake early warning", "benefit_type": "Increased Product Safety", "taxonomy_note": "An automatic train stop on P-wave detection is a failsafe; no harm averted is documented.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "Precautionary stops were executed automatically during a magnitude 4.4 quake. No damage, derailment or delay figures are reported." }, { "group": "societal", "depth": null, "reason": "No passenger outcome is documented beyond the stops." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "The software evaluated the seismic signals and triggered the stop protocol without human intervention, per euronews. The sources are two Italian news reports of the company's announcement. No RFI document is among the sources and the algorithm is not described." }, "evidence": [ { "kind": "news report", "citation": "Euronews (15 Jul 2025). Treni che si fermano da soli: l'Italia inaugura l'allerta sismica automatica in ferrovia.", "title": "Treni che si fermano da soli: l'Italia inaugura l'allerta sismica automatica in ferrovia", "url": "https://it.euronews.com/viaggi/2025/07/15/treni-che-si-fermano-da-soli-litalia-inaugura-lallerta-sismica-automatica-in-ferrovia", "date": "2025-07-15", "written_by": "independent", "quote": "nelle settimane a seguire, è stato testato da un terremoto di magnitudo 4.4: i convogli sulla Roma–Napoli sono stati fermati preventivamente" }, { "kind": "news report", "citation": "ANSA (14 Jul 2025). Fs, su Av Roma-Napoli attivo primo sistema allerta sismica.", "title": "Fs, su Av Roma-Napoli attivo primo sistema allerta sismica", "url": "https://www.ansa.it/sito/notizie/economia/2025/07/14/-fs-su-av-roma-napoli-attivo-primo-sistema-allerta-sismica-_eebe792d-b886-4597-af5d-bd9ce79fc8e4.html", "date": "2025-07-14", "written_by": "independent", "quote": "attiva automaticamente protocolli di sicurezza per rallentare o fermare i treni" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2025-07-14-dd98: AI-Powered Seismic Early Warning System Protects Rome-Naples High-Speed Rail.", "title": "AI-Powered Seismic Early Warning System Protects Rome-Naples High-Speed Rail", "url": "https://oecd.ai/en/incidents/2025-07-14-dd98", "date": "2025-07-14", "written_by": "independent", "quote": "Using real-time sensor data and advanced software, the system automatically slows or stops trains during earthquakes, directly preventing potential harm to passengers and infrastructure." } ], "replication": { "produced_by": [ "Dense accelerometric stations along a high-speed line in a seismic region", "Low-latency fiber links to signaling", "Automatic P-wave analysis with thresholds tied to train control" ], "must_hold": [ "P-waves arrive early enough before damaging shaking to brake trains", "False triggers stay rare enough to keep the automatic stop enabled" ], "echoes": [] }, "paired_incident": null, "headline": "RFI seismic warning stops Rome-Naples trains in a 4.4 quake", "metric": "a magnitude 4.4 earthquake, during which trains on the Rome-Naples line were stopped", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The July 2025 coverage describes activation and RFI's intention to extend the system to other high-speed and ERTMS lines. No later development is established.", "quote": null, "url": null }, "value": null, "developer_country": "Italy", "deployer_country": "Italy", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rete_Ferroviaria_Italiana; https://en.wikipedia.org/wiki/Rete_Ferroviaria_Italiana", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0253", "catalog_version": "1.0.0", "source_fingerprint": "1e13f2aeb2cb54252a5f15abe4e23b6d06aafd6e66e4c39f7b6501212525e12b", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0253-source-0001", "AIBD-0253-source-0002", "AIBD-0253-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Rete Ferroviaria Italiana with the Physics Department of the University of Naples Federico II and JR RTRI", "deployer": "Rete Ferroviaria Italiana (Gruppo FS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The software evaluated the seismic signals and triggered the stop protocol without human intervention, per euronews. The sources are two Italian news reports of the company's announcement. No RFI document is among the sources and the algorithm is not described." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The software evaluated the seismic signals and triggered the stop protocol without human intervention, per euronews. The sources are two Italian news reports of the company's announcement. No RFI document is among the sources and the algorithm is not described." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Precautionary stops were executed automatically during a magnitude 4.4 quake. No damage, derailment or delay figures are reported." }, { "group": "societal", "reason": "No passenger outcome is documented beyond the stops." }, { "group": "personal", "reason": "No individual outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Precautionary stops were executed automatically during a magnitude 4.4 quake. No damage, derailment or delay figures are reported." }, { "group": "societal", "reason": "No passenger outcome is documented beyond the stops." }, { "group": "personal", "reason": "No individual outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Precautionary stops were executed automatically during a magnitude 4.4 quake. No damage, derailment or delay figures are reported." }, { "group": "societal", "reason": "No passenger outcome is documented beyond the stops." }, { "group": "personal", "reason": "No individual outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, 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It is on the Rome-Naples high-speed line. It was developed with the Physics Department of the University of Naples Federico II. Technical support came from the Railway Technical Research Institute of the Japanese railways. A network of latest-generation sensors detects the earliest seismic waves. Within seconds it automatically activates safety protocols to slow or stop trains. euronews (15 Jul 2025) added that the system comprises about 20 seismic stations along 200 km, on the surface and in deep boreholes. They are linked by an SDH fiber network. An advanced software intelligence instantly analyzes the data and assesses the quake's danger, per euronews. The system had been operational since 3 May 2025. In the following weeks it was tested by a magnitude 4.4 earthquake. During it, trains on the Rome-Naples line were stopped preventively. Neither article gives the date of that earthquake. ANSA does not mention artificial intelligence. The OECD AI Incidents Monitor lists the system as AI-powered and a protective response." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "RFI seismic early-warning system (Early Warning Sismico) on the Rome-Naples high-speed line" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rete Ferroviaria Italiana with the Physics Department of the University of Naples Federico II and JR RTRI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "RFI's seismic early-warning system on the Rome-Naples high-speed line, operational since 3 May 2025, stops trains preventively during a magnitude 4.4 earthquake", "headline": "RFI seismic warning stops Rome-Naples trains in a 4.4 quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "1e13f2aeb2cb54252a5f15abe4e23b6d06aafd6e66e4c39f7b6501212525e12b" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ANSA reported on 14 Jul 2025 that Ferrovie dello Stato's infrastructure company Rete Ferroviaria Italiana had activated Italy's first seismic early-warning system. It is on the Rome-Naples high-speed line. It was developed with the Physics Department of the University of Naples Federico II. Technical support came from the Railway Technical Research Institute of the Japanese railways. A network of latest-generation sensors detects the earliest seismic waves. Within seconds it automatically activates safety protocols to slow or stop trains. euronews (15 Jul 2025) added that the system comprises about 20 seismic stations along 200 km, on the surface and in deep boreholes. They are linked by an SDH fiber network. An advanced software intelligence instantly analyzes the data and assesses the quake's danger, per euronews. The system had been operational since 3 May 2025. In the following weeks it was tested by a magnitude 4.4 earthquake. During it, trains on the Rome-Naples line were stopped preventively. Neither article gives the date of that earthquake. ANSA does not mention artificial intelligence. The OECD AI Incidents Monitor lists the system as AI-powered and a protective response.", "replication": { "produced_by": [ "Dense accelerometric stations along a high-speed line in a seismic region", "Low-latency fiber links to signaling", "Automatic P-wave analysis with thresholds tied to train control" ], "must_hold": [ "P-waves arrive early enough before damaging shaking to brake trains", "False triggers stay rare enough to keep the automatic stop enabled" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0131", "case_url": "records/AIBD-0131.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "A student-built earthquake early-warning system meant to notify phones before shaking arrives. Warning the room before the Kulu quake was felt was that intended function, demonstrated live during the team's own presentation to MPs." }, { "label": "AI contribution", "value": "The system is called yapay zeka tabanlı (AI-based) in DHA's report, on the students' description. None of the read reports explains how it detects quakes, what data it uses, or whether the alert time was logged. The 30-second lead is the statement of student Birkan Yılmaz, a member of the team that built it. DHA carried it and Haberler, Yeni Akit and Yeni Çağ reproduced it. No MP, AFAD or Kandilli comment on the alert was found. Leaving the building was a human response to the notification and to the shaking." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0131", "title": "KTÜ students' AI early-warning app pings phones about 30 seconds before a Kulu quake is felt at the Turkish parliament, 15 May 2025", "summary": "KTÜ students' AI quake app alerted phones about 30 s before a Kulu quake (DHA 5.2, USGS 5.0) was felt at Turkey's parliament, 15 May 2025.", "date_observed": "2025-05-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 15 May 2025 software-engineering students from Karadeniz Technical University were presenting their Erken Uyarı Merkezi (Early Warning Center) to members of parliament. DHA's report describes it as an AI-based earthquake early-warning system. During the presentation, in the TBMM Public Relations building in Ankara, a quake centered in Kulu, Konya, was felt in the capital. DHA gave the magnitude as 5.2. USGS lists M5.0 at 15:46 local time (12:46:37 UTC), 14 km east-north-east of Kulu at 10 km depth. Student Birkan Yılmaz said notifications had reached their phones 30 seconds before the quake. Students, some MPs and building staff went outside. The read reports mention no damage or injuries.", "category": "protective response", "unexpected": false, "purpose": "A student-built earthquake early-warning system meant to notify phones before shaking arrives. Warning the room before the Kulu quake was felt was that intended function, demonstrated live during the team's own presentation to MPs.", "system": "Erken Uyarı Merkezi (Early Warning Center), AI-based earthquake early-warning system built by Karadeniz Technical University software-engineering students", "model_version": null, "developer": "Karadeniz Technical University (KTÜ) software-engineering students (DHA); no company or faculty lead named", "deployer": "Karadeniz Technical University (student team), demonstrating the system at the TBMM Public Relations building", "location": "Ankara, Turkey", "domain": "Disaster risk reduction / earthquake early warning", "segment": "Student prototype demonstrated to parliamentarians", "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented.", "registry_ids": { "oecd_aim": "2025-05-15-ad86" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "MPs, students and staff in the TBMM Public Relations building got about 30 seconds' notice, on the developer's own account. They went outside. No damage or injury is reported, so the benefit is the notice itself." } ], "ai_role": { "status": "assessed", "statement": "The system is called yapay zeka tabanlı (AI-based) in DHA's report, on the students' description. None of the read reports explains how it detects quakes, what data it uses, or whether the alert time was logged. The 30-second lead is the statement of student Birkan Yılmaz, a member of the team that built it. DHA carried it and Haberler, Yeni Akit and Yeni Çağ reproduced it. No MP, AFAD or Kandilli comment on the alert was found. Leaving the building was a human response to the notification and to the shaking." }, "evidence": [ { "kind": "news report", "citation": "DHA / Haberler.com (2025). Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti. Haberler.com, 15 May 2025, 18:48.", "title": "Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti", "url": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Akit (2025). Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği. Yeni Akit, 15 May 2025, 18:58.", "title": "Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği", "url": "https://www.yeniakit.com.tr/haber/milletvekilleri-deprem-konusurken-depreme-yakalandi-tbmmde-deprem-panigi-1933002.html", "date": "2025-05-15", "written_by": "independent", "quote": "yapay zeka tabanlı deprem erken uyarı sistemi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Erken uyarı sistemini vekillere anlatırken depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 21:33.", "title": "Erken uyarı sistemini vekillere anlatırken depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/erken-uyar%C4%B1-sistemini-vekillere-anlatirken-depreme-yakalandilar-915433h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 23:45.", "title": "Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/deprem-erken-uyar%C4%B1-sistemini-anlatirken-tbmmde-depreme-yakalandilar-915462h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Öğrenciler, bazı vekiller ve binada çalışanlarla birlikte dışarıya çıktı" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 5.0 - 14 km ENE of Kulu, Turkey (event us7000pzcl, 2025-05-15 12:46:37 UTC, depth 10 km). USGS Earthquake Hazards Program.", "title": "M 5.0 - 14 km ENE of Kulu, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pzcl", "date": "2025-05-15", "written_by": "independent", "quote": "M 5.0 - 14 km ENE of Kulu, Turkey" } ], "replication": { "produced_by": [ "A quake large enough to be felt in Ankara from an epicentre in Kulu, Konya (DHA 5.2; USGS M5.0 at 10 km depth). This left a gap between detection and the arrival of shaking in the capital.", "The team's own phones carrying the app, with connectivity inside the TBMM building, during a live demonstration to MPs", "An audience already primed by the presentation to treat the notification as a cue to leave" ], "must_hold": [ "The alert must arrive before the shaking. Here the lead time is the developer's statement, with no independent log or official timing.", "The detection source and false-alarm rate are undocumented in the read reports. Reliability beyond this one event is therefore unknown.", "Recipients must act on the notice. Here students, some MPs and staff went outside." ], "echoes": [] }, "paired_incident": null, "headline": "KTÜ students' app pings phones about 30 seconds before a quake", "metric": "notifications had reached their phones 30 seconds before the quake", "comparator": { "against": "feeling the quake with no warning", "before": null, "after": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi", "basis": "assumed", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi, yanımızdaki vekillere bunun bilgisini sağladık ve öncesinde çıktık.", "url": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi" }, "later": { "status": "unknown", "date": null, "note": "No later report on the KTÜ students' Erken Uyarı Merkezi was found.", "quote": null, "url": null }, "value": null, "developer_country": "Turkey", "deployer_country": "Turkey", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. All four readable sources carry the same DHA report; ensonhaber.com and gercekgundem.com returned HTTP 403 and were not read. USGS lists the Kulu event as M5.0 mww at 2025–05–15T12:46:37 UTC, 10 km depth, 14 km ENE of Kulu; DHA reports 5.2. The system name Erken Uyarı Merkezi appears in the Yeni Çağ texts. No KTÜ, AFAD or parliamentary statement was found; web search was unavailable in this session. Filed as protective response with prevention unproven as the classifier proposed: the warning is documented only by the developer's statement in a DHA report, and no harm was averted.", "source": "https://oecd.ai/en/incidents/2025-05-15-ad86", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Karadeniz_Technical_University; https://en.wikipedia.org/wiki/Karadeniz_Technical_University", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Karadeniz Technical University.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports and one independent dataset. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0131", "catalog_version": "1.0.0", "source_fingerprint": "047c86dedff83ae19f523631d37cf8f8a36e573639fab4ede8bc861ad3b6d92c", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0131-source-0001", "AIBD-0131-source-0002", "AIBD-0131-source-0003", "AIBD-0131-source-0004", "AIBD-0131-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Karadeniz Technical University (KTÜ) software-engineering students (DHA); no company or faculty lead named", "deployer": "Karadeniz Technical University (student team), demonstrating the system at the TBMM Public Relations building" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The system is called yapay zeka tabanlı (AI-based) in DHA's report, on the students' description. None of the read reports explains how it detects quakes, what data it uses, or whether the alert time was logged. The 30-second lead is the statement of student Birkan Yılmaz, a member of the team that built it. DHA carried it and Haberler, Yeni Akit and Yeni Çağ reproduced it. No MP, AFAD or Kandilli comment on the alert was found. Leaving the building was a human response to the notification and to the shaking." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The system is called yapay zeka tabanlı (AI-based) in DHA's report, on the students' description. None of the read reports explains how it detects quakes, what data it uses, or whether the alert time was logged. The 30-second lead is the statement of student Birkan Yılmaz, a member of the team that built it. DHA carried it and Haberler, Yeni Akit and Yeni Çağ reproduced it. No MP, AFAD or Kandilli comment on the alert was found. Leaving the building was a human response to the notification and to the shaking." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "MPs, students and staff in the TBMM Public Relations building got about 30 seconds' notice, on the developer's own account. They went outside. No damage or injury is reported, so the benefit is the notice itself." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports and one independent dataset. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "MPs, students and staff in the TBMM Public Relations building got about 30 seconds' notice, on the developer's own account. They went outside. No damage or injury is reported, so the benefit is the notice itself." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "MPs, students and staff in the TBMM Public Relations building got about 30 seconds' notice, on the developer's own account. They went outside. No damage or injury is reported, so the benefit is the notice itself." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "notifications had reached their phones 30 seconds before the quake", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-05-15-ad86" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No later report on the KTÜ students' Erken Uyarı Merkezi was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0131" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 15 May 2025 software-engineering students from Karadeniz Technical University were presenting their Erken Uyarı Merkezi (Early Warning Center) to members of parliament. DHA's report describes it as an AI-based earthquake early-warning system. During the presentation, in the TBMM Public Relations building in Ankara, a quake centered in Kulu, Konya, was felt in the capital. DHA gave the magnitude as 5.2. USGS lists M5.0 at 15:46 local time (12:46:37 UTC), 14 km east-north-east of Kulu at 10 km depth. Student Birkan Yılmaz said notifications had reached their phones 30 seconds before the quake. Students, some MPs and building staff went outside. The read reports mention no damage or injuries.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "MPs, students and staff in the TBMM Public Relations building got about 30 seconds' notice, on the developer's own account. They went outside. No damage or injury is reported, so the benefit is the notice itself." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "A student-built earthquake early-warning system meant to notify phones before shaking arrives. Warning the room before the Kulu quake was felt was that intended function, demonstrated live during the team's own presentation to MPs." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Ankara, Turkey", "developer_country": "Turkey", "deployer_country": "Turkey" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "A student-built earthquake early-warning system meant to notify phones before shaking arrives. Warning the room before the Kulu quake was felt was that intended function, demonstrated live during the team's own presentation to MPs." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-15", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. All four readable sources carry the same DHA report; ensonhaber.com and gercekgundem.com returned HTTP 403 and were not read. USGS lists the Kulu event as M5.0 mww at 2025–05–15T12:46:37 UTC, 10 km depth, 14 km ENE of Kulu; DHA reports 5.2. The system name Erken Uyarı Merkezi appears in the Yeni Çağ texts. No KTÜ, AFAD or parliamentary statement was found; web search was unavailable in this session. Filed as protective response with prevention unproven as the classifier proposed: the warning is documented only by the developer's statement in a DHA report, and no harm was averted.", "source": "https://oecd.ai/en/incidents/2025-05-15-ad86", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Karadeniz_Technical_University; https://en.wikipedia.org/wiki/Karadeniz_Technical_University", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Karadeniz Technical University.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A quake large enough to be felt in Ankara from an epicentre in Kulu, Konya (DHA 5.2; USGS M5.0 at 10 km depth). This left a gap between detection and the arrival of shaking in the capital.", "The team's own phones carrying the app, with connectivity inside the TBMM building, during a live demonstration to MPs", "An audience already primed by the presentation to treat the notification as a cue to leave" ], "must_hold": [ "The alert must arrive before the shaking. Here the lead time is the developer's statement, with no independent log or official timing.", "The detection source and false-alarm rate are undocumented in the read reports. Reliability beyond this one event is therefore unknown.", "Recipients must act on the notice. Here students, some MPs and staff went outside." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. All four readable sources carry the same DHA report; ensonhaber.com and gercekgundem.com returned HTTP 403 and were not read. USGS lists the Kulu event as M5.0 mww at 2025–05–15T12:46:37 UTC, 10 km depth, 14 km ENE of Kulu; DHA reports 5.2. The system name Erken Uyarı Merkezi appears in the Yeni Çağ texts. No KTÜ, AFAD or parliamentary statement was found; web search was unavailable in this session. Filed as protective response with prevention unproven as the classifier proposed: the warning is documented only by the developer's statement in a DHA report, and no harm was averted.", "source": "https://oecd.ai/en/incidents/2025-05-15-ad86", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Karadeniz_Technical_University; https://en.wikipedia.org/wiki/Karadeniz_Technical_University", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Karadeniz Technical University.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Disaster risk reduction / earthquake early warning", "segment": "Student prototype demonstrated to parliamentarians" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "DHA / Haberler.com (2025). Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti. Haberler.com, 15 May 2025, 18:48.", "title": "Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti", "url": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Akit (2025). Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği. Yeni Akit, 15 May 2025, 18:58.", "title": "Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği", "url": "https://www.yeniakit.com.tr/haber/milletvekilleri-deprem-konusurken-depreme-yakalandi-tbmmde-deprem-panigi-1933002.html", "date": "2025-05-15", "written_by": "independent", "quote": "yapay zeka tabanlı deprem erken uyarı sistemi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Erken uyarı sistemini vekillere anlatırken depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 21:33.", "title": "Erken uyarı sistemini vekillere anlatırken depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/erken-uyar%C4%B1-sistemini-vekillere-anlatirken-depreme-yakalandilar-915433h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 23:45.", "title": "Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/deprem-erken-uyar%C4%B1-sistemini-anlatirken-tbmmde-depreme-yakalandilar-915462h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Öğrenciler, bazı vekiller ve binada çalışanlarla birlikte dışarıya çıktı" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 5.0 - 14 km ENE of Kulu, Turkey (event us7000pzcl, 2025-05-15 12:46:37 UTC, depth 10 km). USGS Earthquake Hazards Program.", "title": "M 5.0 - 14 km ENE of Kulu, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pzcl", "date": "2025-05-15", "written_by": "independent", "quote": "M 5.0 - 14 km ENE of Kulu, Turkey" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0131-source-0001", "AIBD-0131-source-0002", "AIBD-0131-source-0003", "AIBD-0131-source-0004", "AIBD-0131-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "DHA / Haberler.com (2025). Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti. Haberler.com, 15 May 2025, 18:48.", "title": "Karadeniz Teknik Üniversitesi Öğrencileri TBMM'de Depremi Hissetti", "url": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Akit (2025). Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği. Yeni Akit, 15 May 2025, 18:58.", "title": "Milletvekilleri deprem konuşurken depreme yakalandı! TBMM'de deprem paniği", "url": "https://www.yeniakit.com.tr/haber/milletvekilleri-deprem-konusurken-depreme-yakalandi-tbmmde-deprem-panigi-1933002.html", "date": "2025-05-15", "written_by": "independent", "quote": "yapay zeka tabanlı deprem erken uyarı sistemi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Erken uyarı sistemini vekillere anlatırken depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 21:33.", "title": "Erken uyarı sistemini vekillere anlatırken depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/erken-uyar%C4%B1-sistemini-vekillere-anlatirken-depreme-yakalandilar-915433h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "kind": "news report", "citation": "DHA / Yeni Çağ (2025). Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar. Yeniçağ Gazetesi, 15 May 2025, 23:45.", "title": "Deprem erken uyarı sistemini anlatırken TBMM'de depreme yakalandılar", "url": "https://www.yenicaggazetesi.com/deprem-erken-uyar%C4%B1-sistemini-anlatirken-tbmmde-depreme-yakalandilar-915462h.htm", "date": "2025-05-15", "written_by": "independent", "quote": "Öğrenciler, bazı vekiller ve binada çalışanlarla birlikte dışarıya çıktı" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 5.0 - 14 km ENE of Kulu, Turkey (event us7000pzcl, 2025-05-15 12:46:37 UTC, depth 10 km). USGS Earthquake Hazards Program.", "title": "M 5.0 - 14 km ENE of Kulu, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pzcl", "date": "2025-05-15", "written_by": "independent", "quote": "M 5.0 - 14 km ENE of Kulu, Turkey" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "KTÜ students' AI quake app alerted phones about 30 s before a Kulu quake (DHA 5.2, USGS 5.0) was felt at Turkey's parliament, 15 May 2025.", "account": "On 15 May 2025 software-engineering students from Karadeniz Technical University were presenting their Erken Uyarı Merkezi (Early Warning Center) to members of parliament. DHA's report describes it as an AI-based earthquake early-warning system. During the presentation, in the TBMM Public Relations building in Ankara, a quake centered in Kulu, Konya, was felt in the capital. DHA gave the magnitude as 5.2. USGS lists M5.0 at 15:46 local time (12:46:37 UTC), 14 km east-north-east of Kulu at 10 km depth. Student Birkan Yılmaz said notifications had reached their phones 30 seconds before the quake. Students, some MPs and building staff went outside. The read reports mention no damage or injuries." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Erken Uyarı Merkezi (Early Warning Center), AI-based earthquake early-warning system built by Karadeniz Technical University software-engineering students" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Karadeniz Technical University (KTÜ) software-engineering students (DHA); no company or faculty lead named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "KTÜ students' AI early-warning app pings phones about 30 seconds before a Kulu quake is felt at the Turkish parliament, 15 May 2025", "headline": "KTÜ students' app pings phones about 30 seconds before a quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "047c86dedff83ae19f523631d37cf8f8a36e573639fab4ede8bc861ad3b6d92c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 15 May 2025 software-engineering students from Karadeniz Technical University were presenting their Erken Uyarı Merkezi (Early Warning Center) to members of parliament. DHA's report describes it as an AI-based earthquake early-warning system. During the presentation, in the TBMM Public Relations building in Ankara, a quake centered in Kulu, Konya, was felt in the capital. DHA gave the magnitude as 5.2. USGS lists M5.0 at 15:46 local time (12:46:37 UTC), 14 km east-north-east of Kulu at 10 km depth. Student Birkan Yılmaz said notifications had reached their phones 30 seconds before the quake. Students, some MPs and building staff went outside. The read reports mention no damage or injuries.", "replication": { "produced_by": [ "A quake large enough to be felt in Ankara from an epicentre in Kulu, Konya (DHA 5.2; USGS M5.0 at 10 km depth). This left a gap between detection and the arrival of shaking in the capital.", "The team's own phones carrying the app, with connectivity inside the TBMM building, during a live demonstration to MPs", "An audience already primed by the presentation to treat the notification as a cue to leave" ], "must_hold": [ "The alert must arrive before the shaking. Here the lead time is the developer's statement, with no independent log or official timing.", "The detection source and false-alarm rate are undocumented in the read reports. Reliability beyond this one event is therefore unknown.", "Recipients must act on the notice. Here students, some MPs and staff went outside." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0130", "case_url": "records/AIBD-0130.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function." }, { "label": "AI contribution", "value": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Greece" ] }, "record": { "id": "AIBD-0130", "title": "Google's Android Earthquake Alerts reached phones in Crete and the Dodecanese seconds before the 14 May 2025 M6.0 (Greek press 6.1) Kasos quake", "summary": "Android users in Crete and the Dodecanese got Google's quake alert seconds before the M6.0 (Greek press 6.1) Kasos quake of 14 May 2025.", "date_observed": "2025-05-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "category": "protective response", "unexpected": false, "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function.", "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)", "model_version": null, "developer": "Google", "deployer": "Google", "location": "Kasos, Greece", "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)", "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented.", "registry_ids": { "oecd_aim": "2025-05-14-779c" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ], "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." }, "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "date": null, "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ], "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] }, "paired_incident": null, "headline": "Google alerts Crete phones seconds before the M6.0 Kasos quake", "metric": null, "comparator": { "against": "the arrival of the seismic shaking (no warning)", "before": null, "after": "λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση", "basis": "assumed", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys" }, "later": { "status": "persisted", "date": "2025-07-17", "note": "Two months later Google's team published its Science evaluation of the Android system: over three years it delivered about 18 million alerts a month in 98 countries, and 36% of users who gave feedback received the alert before shaking (the paper does not discuss the Kasos event).", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month. User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively.", "url": "https://europepmc.org/article/MED/40674485" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports, one independent dataset, the makers' peer-reviewed study and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0130", "catalog_version": "1.0.0", "source_fingerprint": "c434c4947e2cacd4aa85a380b8afca94ec0bad41d6ad884073cdbbb28fd816a4", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0130-source-0001", "AIBD-0130-source-0002", "AIBD-0130-source-0003", "AIBD-0130-source-0004", "AIBD-0130-source-0005", "AIBD-0130-source-0006", "AIBD-0130-source-0007", "AIBD-0130-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Android Earthquake Alerts combined signals from phone accelerometers and sent warnings. Greek news reports said that some alerts arrived before the shaking.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "If the reports are right, the alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake. Alerts were then pushed ahead of the slower waves, as Google's 2020 description and the 2025 Science paper set out. Google's own pages do not use the words AI or machine learning. The Science abstract speaks of smartphone-based earthquake detection algorithms. The OECD Monitor classifies the system as AI doing event detection. That alerts arrived seconds before shaking rests on unattributed statements in four Greek outlets published within a day of the quake. None quotes a recipient, a seismologist or Google. No Google per-event confirmation was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: four independent news reports, one independent dataset, the makers' peer-reviewed study and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The reports quote no recipient or Google representative and provide no confirmation for this particular earthquake. Google’s cited descriptions establish an automated detection system but do not identify a learned model.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-05-14-779c" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-07-17", "note": "Two months later Google's team published its Science evaluation of the Android system: over three years it delivered about 18 million alerts a month in 98 countries, and 36% of users who gave feedback received the alert before shaking (the paper does not discuss the Kasos event).", "quote": "Alerts were delivered in 98 countries for earthquakes with M ≥4.5, corresponding to ~60 events and 18 million alerts per month. User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively.", "url": "https://europepmc.org/article/MED/40674485" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0130" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "An earthquake warning is read as a detection mechanism; no averted injury is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "Android users in Crete, the Dodecanese and farther away received a few seconds' message and audible notice of a deep M6.0 quake. Their number is unstated. The reports name no recipient and give no lead time in seconds. They report no damage, injuries or protective action." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kasos, Greece", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect earthquakes from the accelerometers of Android phones and combine the signals on Google's servers. It alerts users in the expected shaking zone ahead of the slower seismic waves. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-14", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Dated to 14 May 2025 local time: USGS gives the origin as 2025–05–13T22:51:15 UTC, M6.0 mww, 76 km depth, 20 km SSE of Fry, and in.gr places it in the early hours of Wednesday; Newsbomb's text says the early hours of Tuesday 13 May, which contradicts both and is not followed. Greek outlets report 6.1; USGS 6.0. Filed as protective response with prevention unproven as the classifier proposed. Of the Monitor's eight Greek sources, Newsbomb, GizChina Greece, in.gr and Alphafreepress state that users were alerted before the shaking (Newsbomb names Crete, the Dodecanese and farther away, by message and an audible signal like the 112 alert); Parapolitika is an explainer without that claim; eleftherostypos.gr and reader.gr returned HTTP 403 and ant1news.gr a TLS error, so they were not read. No Google statement about this event was found; web search was unavailable in this session, so sourcing is limited to the Monitor's list, USGS, Google's own pages and the Science abstract.", "source": "https://oecd.ai/en/incidents/2025-05-14-779c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys; https://europepmc.org/article/MED/40674485", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0130-source-0001", "AIBD-0130-source-0002", "AIBD-0130-source-0003", "AIBD-0130-source-0004", "AIBD-0130-source-0005", "AIBD-0130-source-0006", "AIBD-0130-source-0007", "AIBD-0130-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Newsbomb (2025). «Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς. Newsbomb.gr, 14 May 2025, 23:10.", "title": "«Σεισμός τώρα»: Πώς να ενεργοποιήσετε τη νέα εφαρμογή της Google που ενημερώνει για σεισμούς", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys", "date": "2025-05-14", "written_by": "independent", "quote": "Πολλοί κάτοικοι κοντινών περιοχών, όπως εκείνοι στην Κρήτη, στα Δωδεκάνησα αλλά και πιο μακριά ενημερώθηκαν έγκαιρα και έλαβαν ειδοποίηση στο κινητό τους λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "kind": "news report", "citation": "GizChina Greece (2025). Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό. GizChina.gr, 14 May 2025.", "title": "Σεισμός τώρα: πώς να ενεργοποιήσετε τις ειδοποιήσεις στο κινητό", "url": "https://www.gizchina.gr/?p=363710", "date": "2025-05-14", "written_by": "independent", "quote": "αρκετοί κάτοικοι των Δωδεκανήσων, αλλά και ευρύτερων περιοχών, έλαβαν ειδοποιήσεις στο κινητό τους, δευτερόλεπτα πριν το κύμα του σεισμού φτάσει σε αυτούς" }, { "kind": "news report", "citation": "in.gr (2025). Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό. in.gr, 15 May 2025, 11:02.", "title": "Πώς θα ενεργοποιήσετε την εφαρμογή της Google που προειδοποιεί για σεισμό", "url": "https://www.in.gr/2025/05/15/in-science/technology/pos-tha-energopoiisete-tin-efarmogi-tis-google-pou-proeidopoiei-gia-seismo", "date": "2025-05-15", "written_by": "independent", "quote": "Μια υπηρεσία έγκαιρης προειδοποίησης για σεισμούς που κρύβεται στο λειτουργικό σύστημα Android ήταν αυτή που ενημέρωσε πολλούς χρήστες για τον σεισμό των 6,1 βαθμών" }, { "kind": "news report", "citation": "Alphafreepress (2025). Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android. Alphafreepress.gr, 14 May 2025, 20:42.", "title": "Κάσος σεισμός 6,1 Ρίχτερ: Πώς λειτουργεί το σύστημα προειδοποίησης στα Android", "url": "https://www.alphafreepress.gr/2025/05/14/ellada/kasos-seismos-61-rixter-pos-leitourgei-to-systima-proeidopoiisis-sta-android", "date": "2025-05-14", "written_by": "independent", "quote": "το σύστημα σεισμικής ειδοποίησης της Google καταφέρνει να στείλει ενημέρωση στον πολίτη πριν φτάσει το κύμα της δόνησης στην περιοχή του" }, { "kind": "dataset", "citation": "U.S. Geological Survey (2025). M 6.0 - 20 km SSE of Fry, Greece (event us7000pz2p, 2025-05-13 22:51:15 UTC, depth 76 km). USGS Earthquake Hazards Program.", "title": "M 6.0 - 20 km SSE of Fry, Greece", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000pz2p", "date": "2025-05-13", "written_by": "independent", "quote": "M 6.0 - 20 km SSE of Fry, Greece" }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259. PMID 40674485.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "We show how smartphone-based earthquake detection algorithms can be implemented at scale and improved through postevent analysis." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Android users in Crete and the Dodecanese got Google's quake alert seconds before the M6.0 (Greek press 6.1) Kasos quake of 14 May 2025.", "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts reached phones in Crete and the Dodecanese seconds before the 14 May 2025 M6.0 (Greek press 6.1) Kasos quake", "headline": "Google alerts Crete phones seconds before the M6.0 Kasos quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c434c4947e2cacd4aa85a380b8afca94ec0bad41d6ad884073cdbbb28fd816a4" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 01:51 local time on Wednesday 14 May 2025 (22:51 UTC on 13 May) a magnitude 6.0 (USGS; Greek outlets 6.1) earthquake struck. It struck 20 km south-south-east of Fry on Kasos, between Kasos and Karpathos, at 76 km depth. USGS logged 931 felt reports and a green alert level. Newsbomb, GizChina Greece, in.gr and Alphafreepress reported on 14 and 15 May on Google's Android Earthquake Alerts System. In Europe it detects quakes from phone accelerometers rather than seismographs. It notified users in Crete, the Dodecanese and farther away seconds before the shaking. Notice came by message and an audible signal similar to the 112 alert. None of the reports names a recipient, gives a lead time in seconds, or mentions damage or injuries.", "replication": { "produced_by": [ "A magnitude 6.0 (USGS) quake above the system's 4.5 alert threshold, centered offshore south of Kasos at about 76 km depth. A push alert could therefore outrun the slower waves to Crete and the Dodecanese.", "Enough Android phones near the epicentre with the Earthquake Alerts setting on to trigger Google's server-side detection. In Europe the system uses the accelerometers in phones instead of seismographs (in.gr).", "Receiving phones with location (GPS) enabled and an internet connection, which Alphafreepress lists as requirements for the alerts" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports that only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. The Greek reports document no protective action by anyone who received the alert.", "The phone must have the Earthquake Alerts setting enabled (Alphafreepress: Settings > Safety & Emergency > Earthquake Alerts). The feature must be supported in the region." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0229", "case_url": "records/AIBD-0229.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Identify synthetic (deepfake) audio and attribute it to the generating system." }, { "label": "AI contribution", "value": "The attribution is the vendor's own analysis, reported by WIRED with named sources and independently replicated by Farid's team. ElevenLabs did not comment on the incident. The sources document no harm stopped and do not link the later charges against Kramer or the FCC fine to Pindrop's finding." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0229", "title": "Pindrop's deepfake detector attributes the Biden New Hampshire robocall to ElevenLabs technology, corroborated by UC Berkeley", "summary": "After a deepfake Biden robocall targeted New Hampshire's Jan 2024 primary, Pindrop's analysis against 120+ voice engines pointed to ElevenLabs; Hany Farid's Berkeley team reached the same conclusion.", "date_observed": "2024-01-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Pindrop's blog post appeared the day before. Voters in New Hampshire received an AI-generated robocall impersonating President Biden before the 23 January 2024 primary. Pindrop, a company that builds synthetic-audio detection tools, analyzed a 39-second clip, checking its patterns against more than 120 voice synthesis engines. Its CEO Vijay Balasubramaniyan told WIRED the result came back well north of 99 percent for ElevenLabs. Hany Farid's UC Berkeley team, initially skeptical, ran its own model on the same sample. It concluded with high confidence that it was AI-generated and likely ElevenLabs. ElevenLabs' own detector returned 84 percent, a result WIRED reproduced. In May 2024 the New Hampshire Attorney General announced 26 charges against Steven Kramer and the FCC proposed a $6 million fine (NHPR). The sources do not connect those actions to Pindrop's analysis.", "category": "benefit created", "unexpected": false, "purpose": "Identify synthetic (deepfake) audio and attribute it to the generating system.", "system": "Pindrop deepfake audio analysis (voice-engine attribution)", "model_version": null, "developer": "Pindrop", "deployer": "Pindrop (developer and deployer are the same company); independent corroboration by Hany Farid's UC Berkeley model", "location": null, "domain": "Election integrity / deepfake detection", "segment": "Technology", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The system attributed a harm after the fact rather than preventing it.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 1, "reason": "Public attribution of the robocall's toolchain, corroborated by an academic team. No enforcement outcome in the sources is traced to the analysis." } ], "ai_role": { "status": "assessed", "statement": "The attribution is the vendor's own analysis, reported by WIRED with named sources and independently replicated by Farid's team. ElevenLabs did not comment on the incident. The sources document no harm stopped and do not link the later charges against Kramer or the FCC fine to Pindrop's finding." }, "evidence": [ { "kind": "news report", "citation": "Kate Knibbs, WIRED, 26 Jan 2024", "title": "Researchers Say the Deepfake Biden Robocall Was Likely Made With Tools From AI Startup ElevenLabs", "url": "https://www.wired.com/story/biden-robocall-deepfake-elevenlabs/", "date": "2024-01-26", "written_by": "independent", "quote": "Our model says with high confidence that it is AI-generated and likely to be ElevenLabs," }, { "kind": "news report", "citation": "Todd Bookman, New Hampshire Public Radio, 23 May 2024", "title": "Political operative linked to AI Biden robocalls facing charges in NH, FCC fines", "url": "https://www.nhpr.org/politics/2024-05-23/political-operative-linked-to-ai-biden-robocalls-facing-charges-in-nh-fcc-fines", "date": "2024-05-23", "written_by": "independent", "quote": "In total, he’s facing 26 charges, including 13 felonies." } ], "replication": { "produced_by": [ "A clean audio sample of the deepfake", "A library of fingerprints for many commercial voice-synthesis engines", "A second, independent model run on the same sample" ], "must_hold": [ "The generating engine must be among those fingerprinted", "Sample length and quality sufficient for attribution" ], "echoes": [] }, "paired_incident": null, "headline": "Pindrop traces the Biden New Hampshire robocall to ElevenLabs", "metric": "the result came back well north of 99 percent for ElevenLabs", "comparator": null, "later": { "status": "litigated", "date": "2024-05-23", "note": "Four months later Steven Kramer was indicted in New Hampshire on 26 charges and the FCC proposed a $6 million fine; the cited sources do not tie those actions to Pindrop's analysis.", "quote": "They allege that 54-year-old Steven Kramer worked to suppress votes and impersonated a political candidate when he coordinated the robocalls. In total, he's facing 26 charges, including 13 felonies.", "url": "https://www.nhpr.org/politics/2024-05-23/political-operative-linked-to-ai-biden-robocalls-facing-charges-in-nh-fcc-fines" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhpr.org/politics/2024-05-23/political-operative-linked-to-ai-biden-robocalls-facing-charges-in-nh-fcc-fines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Pindrop_Security; https://en.wikipedia.org/wiki/Pindrop_Security", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Pindrop.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0229", "catalog_version": "1.0.0", "source_fingerprint": "8214b8556babe9cf20a70009bb6e001f80fe1f3ec1f88e30c4c95f12a2d2f31e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0229-source-0001", "AIBD-0229-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Pindrop", "deployer": "Pindrop (developer and deployer are the same company); independent corroboration by Hany Farid's UC Berkeley model" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The attribution is the vendor's own analysis, reported by WIRED with named sources and independently replicated by Farid's team. 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Voters in New Hampshire received an AI-generated robocall impersonating President Biden before the 23 January 2024 primary. Pindrop, a company that builds synthetic-audio detection tools, analyzed a 39-second clip, checking its patterns against more than 120 voice synthesis engines. Its CEO Vijay Balasubramaniyan told WIRED the result came back well north of 99 percent for ElevenLabs. Hany Farid's UC Berkeley team, initially skeptical, ran its own model on the same sample. It concluded with high confidence that it was AI-generated and likely ElevenLabs. ElevenLabs' own detector returned 84 percent, a result WIRED reproduced. In May 2024 the New Hampshire Attorney General announced 26 charges against Steven Kramer and the FCC proposed a $6 million fine (NHPR). The sources do not connect those actions to Pindrop's analysis." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Pindrop deepfake audio analysis (voice-engine attribution)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Pindrop" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Pindrop's deepfake detector attributes the Biden New Hampshire robocall to ElevenLabs technology, corroborated by UC Berkeley", "headline": "Pindrop traces the Biden New Hampshire robocall to ElevenLabs" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "8214b8556babe9cf20a70009bb6e001f80fe1f3ec1f88e30c4c95f12a2d2f31e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Pindrop's blog post appeared the day before. Voters in New Hampshire received an AI-generated robocall impersonating President Biden before the 23 January 2024 primary. Pindrop, a company that builds synthetic-audio detection tools, analyzed a 39-second clip, checking its patterns against more than 120 voice synthesis engines. Its CEO Vijay Balasubramaniyan told WIRED the result came back well north of 99 percent for ElevenLabs. Hany Farid's UC Berkeley team, initially skeptical, ran its own model on the same sample. It concluded with high confidence that it was AI-generated and likely ElevenLabs. ElevenLabs' own detector returned 84 percent, a result WIRED reproduced. In May 2024 the New Hampshire Attorney General announced 26 charges against Steven Kramer and the FCC proposed a $6 million fine (NHPR). The sources do not connect those actions to Pindrop's analysis.", "replication": { "produced_by": [ "A clean audio sample of the deepfake", "A library of fingerprints for many commercial voice-synthesis engines", "A second, independent model run on the same sample" ], "must_hold": [ "The generating engine must be among those fingerprinted", "Sample length and quality sufficient for attribution" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0318", "case_url": "records/AIBD-0318.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Built to detect tigers and people on trails with embedded AI and send real-time images to forest officials. Detecting and transmitting the poachers' images was that purpose." }, { "label": "AI contribution", "value": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0318", "title": "TrailGuard AI camera in Kishanpur Wildlife Sanctuary transmits images of armed poachers on two nights; Dudhwa Tiger Reserve officials identify them and investigate", "summary": "Dertien et al. (BioScience 2023): one TrailGuard AI unit captured poachers “carrying a gun and knives” on two dates and a poached chital; officials identified them. Arrests claimed only in releases.", "date_observed": "2023-09-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Dertien, Negi, Dinerstein and colleagues (BioScience 73(10):748–757, published online 14 September 2023) report deploying the TrailGuard AI camera-alert system. The paper does not date the detections. The system “runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images”. It was deployed in the Kanha-Pench corridor and in Dudhwa Tiger Reserve, Uttar Pradesh. All seven Dudhwa units transmitted images; three transmitted objects of interest to forest department officials. “One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates, and a presumably poached chital (Axis axis) on the second detection”. “Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter”. Tigers were detected at the same spot three days before the second poacher detection. The authors include RESOLVE and CVEDIA staff (the system's developers) alongside Dudhwa Tiger Reserve, Wildlife Institute of India and Global Tiger Forum co-authors. AFP (Taipei Times, 6 October 2023) reported the study's claim of “identifying a team of poachers”. A Clemson University release of 15 September 2023 carries Nightjar/RESOLVE boilerplate. It states that the alerts “led the authorities to the arrest of these individuals for poaching a few weeks later”. It quotes Sanjay Kumar Pathak, former field director of Dudhwa, that the system “led to the detection and arrest of poachers in Dudhwa”. The Hindu (16 September 2023) cited a person involved in the experiment. Poachers caught on camera had a year later helped forest authorities secure a conviction. It quoted Piyush Yadav of Nightjar, the manufacturer, that “A year down, these poachers were arrested with images from TrailGuard playing a key role”. The article's lead puts the capture in Kanha-Pench and the Yadav quote in Dudhwa. No forest-department statement or court record of an arrest was found.", "category": "protective response", "unexpected": false, "purpose": "Built to detect tigers and people on trails with embedded AI and send real-time images to forest officials. Detecting and transmitting the poachers' images was that purpose.", "system": "TrailGuard AI camera-alert system (embedded AI on an Intel computer-vision chip; cellular transmission)", "model_version": null, "developer": "Nightjar / RESOLVE, Washington DC (inventor Steve Gulick), with CVEDIA Ltd", "deployer": "Dudhwa Tiger Reserve, Uttar Pradesh Forest Department, with the Global Tiger Forum, National Tiger Conservation Authority and Clemson University", "location": "Kishanpur Wildlife Sanctuary, Dudhwa Tiger Reserve, Uttar Pradesh, India", "domain": "Conservation / anti-poaching law enforcement", "segment": "Anti-poaching enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The paper documents detection and identification of armed poachers for investigation, with arrests claimed only in releases, and the protected asset is wildlife.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. The paper documents an investigation, not an arrest or prosecution, so the recorded gain is an investigative lead." }, { "group": "societal", "depth": null, "reason": "Protection of the reserve's wildlife from this gang is claimed through arrests only in the Clemson release, developer material and The Hindu's second-hand report. The paper notes a chital presumably poached before the second detection." } ], "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Dertien JS, Negi H, Dinerstein E, Krishnamurthy R, Negi HS, Gopal R, Gulick S, Pathak SK, Kapoor M, Yadav P, Benitez M, Ferreira M, Wijnveen AJ, Lee ATL, Wright B, Baldwin RF (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10):748-757, doi:10.1093/biosci/biad076 (PMC10580963; authors include RESOLVE and CVEDIA developer staff and Dudhwa Tiger Reserve staff).", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates" }, { "kind": "peer-reviewed study", "citation": "Dertien et al. (2023), same paper, results on the Dudhwa deployment.", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter" }, { "kind": "news report", "citation": "AFP (2023). AI of the tiger: Tiny camera 'protects' predator - and people. Taipei Times, 6 October 2023.", "title": "AI of the tiger: Tiny camera 'protects' predator - and people", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "date": "2023-10-06", "written_by": "independent", "quote": "The research claims the cameras were immediately effective, picking up a tiger just 300 meters from a village, and on another occasion identifying a team of poachers." }, { "kind": "press release", "citation": "Clemson News (2023). Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans. 15 September 2023 (university release of the lead author's institution; carries Nightjar/RESOLVE boilerplate; quotes former Dudhwa field director Sanjay Kumar Pathak).", "title": "Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans", "url": "https://news.clemson.edu/clemson-students-deployment-of-novel-camera-alert-system-trailguard-ai-featured-in-bioscience-promotes-coexistence-of-tigers-humans/", "date": "2023-09-15", "written_by": "deployer", "quote": "TrailGuard AI offers park protection that led to the detection and arrest of poachers in Dudhwa," }, { "kind": "news report", "citation": "The Hindu (2023). AI cameras to curb poaching in Madhya Pradesh. 16 Sep 2023.", "title": "AI cameras to curb poaching in Madhya Pradesh", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "A year down, these poachers were arrested with images from TrailGuard playing a key role" }, { "kind": "news report", "citation": "The Hindu (2023). Same article, lead paragraph.", "title": "AI cameras to curb poaching in Madhya Pradesh (conviction claim)", "url": "https://web.archive.org/web/20240808102127/https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "caught poachers on camera that — a year down — helped forest authorities secure a conviction, according to a person involved in the experiment" } ], "replication": { "produced_by": [ "Camouflaged camera units on trails inside the reserve used by both tigers and poachers", "Cellular coverage allowing images to reach forest officials' phones in under a minute" ], "must_hold": [ "Officials act on the images while the poachers can still be found. The paper records identification and investigation only", "The embedded classifier suppresses non-target triggers so that officials keep trusting the alerts" ], "echoes": [ { "date": null, "setting": "Similipal Tiger Reserve, Odisha (recorded separately as AIBD-0102); The Better India (30 June 2025) reports “96 poacher arrests and 86 firearms seized within ten months” from TrailGuard AI deployments there", "model_version": null, "outcome": "recurred", "source": "https://thebetterindia.com/457142/ai-applications-that-tackle-poaching-india/" } ] }, "paired_incident": null, "headline": "TrailGuard AI transmits images of armed poachers in Kishanpur", "metric": null, "comparator": { "against": "camera traps without real-time notification", "before": "camera traps have lacked the real-time notification capabilities and practicality necessary for prevention of poaching", "after": "notifications of tiger presence via email or push notification between 30 and 42 seconds after detections", "basis": "assumed", "quote": "Until recently, camera traps have lacked the real-time notification capabilities and practicality necessary for prevention of poaching and human–wildlife conflict.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/" }, "later": { "status": "persisted", "date": "2023-09-16", "note": "Two days after the paper, The Hindu quoted Nightjar's Piyush Yadav that the Dudhwa poachers were arrested about a year after the images, with TrailGuard images playing a key role; Clemson's release of 15 September 2023 says the arrests came a few weeks later, and no forest department or court record was found.", "quote": "But in the Dudhwa [tiger reserve] we learnt that not only could we track [in this case] tigers approaching a village but we also caught a gang of poachers on camera who were walking with a hunted chital. A year down, these poachers were arrested with images from TrailGuard playing a key role,", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" }, "value": null, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged the Dudhwa poacher material from a second write-up: added The Hindu's report of the arrest and conviction claims; the BioScience paper is marked as written by the system's developers.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/; https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.3blmedia.com/news/trailguard-ai-named-times-list-best-inventions-2023; https://en.wikipedia.org/wiki/Dudhwa_Tiger_Reserve", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: three independent news reports, the makers' peer-reviewed study and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0318", "catalog_version": "1.0.0", "source_fingerprint": "3da3ea7fd080d05b95a11dd87baa78b9d68631cb63ea47cec85c691e5b783a51", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0318-source-0001", "AIBD-0318-source-0002", "AIBD-0318-source-0003", "AIBD-0318-source-0004", "AIBD-0318-source-0005", "AIBD-0318-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Nightjar / RESOLVE, Washington DC (inventor Steve Gulick), with CVEDIA Ltd", "deployer": "Dudhwa Tiger Reserve, Uttar Pradesh Forest Department, with the Global Tiger Forum, National Tiger Conservation Authority and Clemson University" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The camera's on-board classifier filtered motion triggers to people and tigers and transmitted the poachers' images to officials' phones within about a minute. Identification and investigation were done by officials. The peer-reviewed account is co-authored by the developers (RESOLVE, CVEDIA) with Dudhwa and Wildlife Institute of India staff and stops at “investigating”. The arrest claim rests on a Clemson release quoting the ex-field director, developer press releases and The Hindu quoting the manufacturer and an unnamed participant." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. The paper documents an investigation, not an arrest or prosecution, so the recorded gain is an investigative lead." }, { "group": "societal", "reason": "Protection of the reserve's wildlife from this gang is claimed through arrests only in the Clemson release, developer material and The Hindu's second-hand report. The paper notes a chital presumably poached before the second detection." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: three independent news reports, the makers' peer-reviewed study and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. The paper documents an investigation, not an arrest or prosecution, so the recorded gain is an investigative lead." }, { "group": "societal", "reason": "Protection of the reserve's wildlife from this gang is claimed through arrests only in the Clemson release, developer material and The Hindu's second-hand report. The paper notes a chital presumably poached before the second detection." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 1, "reason": "Forest officials received the images in near real time and identified the individuals. 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The paper notes a chital presumably poached before the second detection." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", 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The paper does not date the detections. The system “runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images”. It was deployed in the Kanha-Pench corridor and in Dudhwa Tiger Reserve, Uttar Pradesh. All seven Dudhwa units transmitted images; three transmitted objects of interest to forest department officials. “One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates, and a presumably poached chital (Axis axis) on the second detection”. “Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter”. Tigers were detected at the same spot three days before the second poacher detection. The authors include RESOLVE and CVEDIA staff (the system's developers) alongside Dudhwa Tiger Reserve, Wildlife Institute of India and Global Tiger Forum co-authors. 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Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10):748-757, doi:10.1093/biosci/biad076 (PMC10580963; authors include RESOLVE and CVEDIA developer staff and Dudhwa Tiger Reserve staff).", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates" }, { "kind": "peer-reviewed study", "citation": "Dertien et al. 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Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans. 15 September 2023 (university release of the lead author's institution; carries Nightjar/RESOLVE boilerplate; quotes former Dudhwa field director Sanjay Kumar Pathak).", "title": "Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans", "url": "https://news.clemson.edu/clemson-students-deployment-of-novel-camera-alert-system-trailguard-ai-featured-in-bioscience-promotes-coexistence-of-tigers-humans/", "date": "2023-09-15", "written_by": "deployer", "quote": "TrailGuard AI offers park protection that led to the detection and arrest of poachers in Dudhwa," }, { "kind": "news report", "citation": "The Hindu (2023). AI cameras to curb poaching in Madhya Pradesh. 16 Sep 2023.", "title": "AI cameras to curb poaching in Madhya Pradesh", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "A year down, these poachers were arrested with images from TrailGuard playing a key role" }, { "kind": "news report", "citation": "The Hindu (2023). Same article, lead paragraph.", "title": "AI cameras to curb poaching in Madhya Pradesh (conviction claim)", "url": "https://web.archive.org/web/20240808102127/https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "caught poachers on camera that — a year down — helped forest authorities secure a conviction, according to a person involved in the experiment" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0318-source-0001", "AIBD-0318-source-0002", "AIBD-0318-source-0003", "AIBD-0318-source-0004", "AIBD-0318-source-0005", "AIBD-0318-source-0006" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Dertien JS, Negi H, Dinerstein E, Krishnamurthy R, Negi HS, Gopal R, Gulick S, Pathak SK, Kapoor M, Yadav P, Benitez M, Ferreira M, Wijnveen AJ, Lee ATL, Wright B, Baldwin RF (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10):748-757, doi:10.1093/biosci/biad076 (PMC10580963; authors include RESOLVE and CVEDIA developer staff and Dudhwa Tiger Reserve staff).", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates" }, { "kind": "peer-reviewed study", "citation": "Dertien et al. (2023), same paper, results on the Dudhwa deployment.", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/", "date": "2023-09-14", "written_by": "makers", "quote": "Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter" }, { "kind": "news report", "citation": "AFP (2023). AI of the tiger: Tiny camera 'protects' predator - and people. Taipei Times, 6 October 2023.", "title": "AI of the tiger: Tiny camera 'protects' predator - and people", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "date": "2023-10-06", "written_by": "independent", "quote": "The research claims the cameras were immediately effective, picking up a tiger just 300 meters from a village, and on another occasion identifying a team of poachers." }, { "kind": "press release", "citation": "Clemson News (2023). Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans. 15 September 2023 (university release of the lead author's institution; carries Nightjar/RESOLVE boilerplate; quotes former Dudhwa field director Sanjay Kumar Pathak).", "title": "Clemson students' deployment of novel camera alert system, TrailGuard AI, featured in BioScience, promotes coexistence of tigers, humans", "url": "https://news.clemson.edu/clemson-students-deployment-of-novel-camera-alert-system-trailguard-ai-featured-in-bioscience-promotes-coexistence-of-tigers-humans/", "date": "2023-09-15", "written_by": "deployer", "quote": "TrailGuard AI offers park protection that led to the detection and arrest of poachers in Dudhwa," }, { "kind": "news report", "citation": "The Hindu (2023). AI cameras to curb poaching in Madhya Pradesh. 16 Sep 2023.", "title": "AI cameras to curb poaching in Madhya Pradesh", "url": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "A year down, these poachers were arrested with images from TrailGuard playing a key role" }, { "kind": "news report", "citation": "The Hindu (2023). Same article, lead paragraph.", "title": "AI cameras to curb poaching in Madhya Pradesh (conviction claim)", "url": "https://web.archive.org/web/20240808102127/https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece", "date": "2023-09-16", "written_by": "independent", "quote": "caught poachers on camera that — a year down — helped forest authorities secure a conviction, according to a person involved in the experiment" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Dertien et al. (BioScience 2023): one TrailGuard AI unit captured poachers “carrying a gun and knives” on two dates and a poached chital; officials identified them. Arrests claimed only in releases.", "account": "Dertien, Negi, Dinerstein and colleagues (BioScience 73(10):748–757, published online 14 September 2023) report deploying the TrailGuard AI camera-alert system. The paper does not date the detections. The system “runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images”. It was deployed in the Kanha-Pench corridor and in Dudhwa Tiger Reserve, Uttar Pradesh. All seven Dudhwa units transmitted images; three transmitted objects of interest to forest department officials. “One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates, and a presumably poached chital (Axis axis) on the second detection”. “Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter”. Tigers were detected at the same spot three days before the second poacher detection. The authors include RESOLVE and CVEDIA staff (the system's developers) alongside Dudhwa Tiger Reserve, Wildlife Institute of India and Global Tiger Forum co-authors. AFP (Taipei Times, 6 October 2023) reported the study's claim of “identifying a team of poachers”. A Clemson University release of 15 September 2023 carries Nightjar/RESOLVE boilerplate. It states that the alerts “led the authorities to the arrest of these individuals for poaching a few weeks later”. It quotes Sanjay Kumar Pathak, former field director of Dudhwa, that the system “led to the detection and arrest of poachers in Dudhwa”. The Hindu (16 September 2023) cited a person involved in the experiment. Poachers caught on camera had a year later helped forest authorities secure a conviction. It quoted Piyush Yadav of Nightjar, the manufacturer, that “A year down, these poachers were arrested with images from TrailGuard playing a key role”. The article's lead puts the capture in Kanha-Pench and the Yadav quote in Dudhwa. No forest-department statement or court record of an arrest was found." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "TrailGuard AI camera-alert system (embedded AI on an Intel computer-vision chip; cellular transmission)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Nightjar / RESOLVE, Washington DC (inventor Steve Gulick), with CVEDIA Ltd" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "TrailGuard AI camera in Kishanpur Wildlife Sanctuary transmits images of armed poachers on two nights; Dudhwa Tiger Reserve officials identify them and investigate", "headline": "TrailGuard AI transmits images of armed poachers in Kishanpur" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "3da3ea7fd080d05b95a11dd87baa78b9d68631cb63ea47cec85c691e5b783a51" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Dertien, Negi, Dinerstein and colleagues (BioScience 73(10):748–757, published online 14 September 2023) report deploying the TrailGuard AI camera-alert system. The paper does not date the detections. The system “runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images”. It was deployed in the Kanha-Pench corridor and in Dudhwa Tiger Reserve, Uttar Pradesh. All seven Dudhwa units transmitted images; three transmitted objects of interest to forest department officials. “One unit within Kishanpur Wildlife Sanctuary captured a group of poachers carrying a gun and knives on two separate dates, and a presumably poached chital (Axis axis) on the second detection”. “Authorities were able to quickly identify the individuals via the transmitted images and were investigating the matter”. Tigers were detected at the same spot three days before the second poacher detection. The authors include RESOLVE and CVEDIA staff (the system's developers) alongside Dudhwa Tiger Reserve, Wildlife Institute of India and Global Tiger Forum co-authors. AFP (Taipei Times, 6 October 2023) reported the study's claim of “identifying a team of poachers”. A Clemson University release of 15 September 2023 carries Nightjar/RESOLVE boilerplate. It states that the alerts “led the authorities to the arrest of these individuals for poaching a few weeks later”. It quotes Sanjay Kumar Pathak, former field director of Dudhwa, that the system “led to the detection and arrest of poachers in Dudhwa”. The Hindu (16 September 2023) cited a person involved in the experiment. Poachers caught on camera had a year later helped forest authorities secure a conviction. It quoted Piyush Yadav of Nightjar, the manufacturer, that “A year down, these poachers were arrested with images from TrailGuard playing a key role”. The article's lead puts the capture in Kanha-Pench and the Yadav quote in Dudhwa. No forest-department statement or court record of an arrest was found.", "replication": { "produced_by": [ "Camouflaged camera units on trails inside the reserve used by both tigers and poachers", "Cellular coverage allowing images to reach forest officials' phones in under a minute" ], "must_hold": [ "Officials act on the images while the poachers can still be found. The paper records identification and investigation only", "The embedded classifier suppresses non-target triggers so that officials keep trusting the alerts" ], "echoes": [ { "setting": "Similipal Tiger Reserve, Odisha (recorded separately as AIBD-0102); The Better India (30 June 2025) reports “96 poacher arrests and 86 firearms seized within ten months” from TrailGuard AI deployments there", "outcome": "recurred", "source": "https://thebetterindia.com/457142/ai-applications-that-tackle-poaching-india/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0113", "case_url": "records/AIBD-0113.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Meta's content-detection and law-enforcement referral process exists to find posts that threaten violence or self-harm. It passes credible threats to the authorities of the user's country. Flagging this post and alerting the police was that purpose." }, { "label": "AI contribution", "value": "Officials said the network detected the comment, classified it as dangerous and reported it. It went to the FBI and a 24/7 channel shared by prosecutors and the cybercrime police. Alarcón also spoke of corroborating “datos de analítica artificial” before the visit. No source says the flag was automated. Meta documents that its technology proactively detects most violating content before anyone reports it, but its referrals to law enforcement also pass through human review. Identifying the user, the home visit and the psychological support were the work of Paraguayan police, prosecutors and the child-protection office." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0113", "title": "Meta threat alert, relayed through the FBI, brings Paraguayan police and psychologists to a bullied 17-year-old who praised the Uvalde shooter", "summary": "Meta flagged a Paraguayan teen's post praising the Uvalde shooter; the FBI relayed it and police, prosecutors and psychologists intervened.", "date_observed": "2022-05-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Thursday 26 May 2022, two days after the Uvalde school shooting, Paraguay's Policía Nacional and Ministerio Público received an alert. It came from Meta's platform and the FBI office in Buenos Aires. A user in Paraguay had tagged the shooter, called himself his “fan” and said something similar could happen in Paraguay with more victims. Comisario Diosnel Alarcón's cybercrime department identified the Instagram profile as a 17-year-old in the “zona central”. He lived with a relative and was treated as a bullying victim. Police, prosecutors, the children's defender and psychologists visited the home and recommended urgent psychological support, and also visited a friend who knew of the posts. No weapon or plan was reported.", "category": "protective response", "unexpected": false, "purpose": "Meta's content-detection and law-enforcement referral process exists to find posts that threaten violence or self-harm. It passes credible threats to the authorities of the user's country. Flagging this post and alerting the police was that purpose.", "system": "Meta (Facebook/Instagram) violence-threat detection and law-enforcement referral process", "model_version": null, "developer": "Meta Platforms", "deployer": "Meta Platforms", "location": null, "domain": "Online safety and threat referral to law enforcement", "segment": "Media, social platforms, and marketing", "benefit_type": "Reduced Violence", "taxonomy_note": "A threat of a copycat shooting was checked and the minor given psychological support, but no weapon or plan was found and the detection is not shown to be automated.", "registry_ids": { "oecd_aim": "2022-05-27-1b09" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "The 17-year-old, a bullying victim living with a relative, was seen by prosecutors' psychologists, who recommended urgent support (“contención”). The sources report no coercive outcome beyond a planned review of his phone." }, { "group": "societal", "depth": 1, "reason": "A public statement that a school shooting could be repeated in Paraguay was checked within a day, after local media had reported threats in schools. Police said many such alerts prove to be jokes, so no prevented harm is shown." } ], "ai_role": { "status": "unassessed", "statement": "Officials said the network detected the comment, classified it as dangerous and reported it. It went to the FBI and a 24/7 channel shared by prosecutors and the cybercrime police. Alarcón also spoke of corroborating “datos de analítica artificial” before the visit. No source says the flag was automated. Meta documents that its technology proactively detects most violating content before anyone reports it, but its referrals to law enforcement also pass through human review. Identifying the user, the home visit and the psychological support were the work of Paraguayan police, prosecutors and the child-protection office." }, "evidence": [ { "kind": "news report", "citation": "La Nación, Paraguay (2022). Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor. La Nación.", "title": "Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor", "url": "https://www.lanacion.com.py/pais/2022/05/26/atentado-en-texas-fbi-alerta-sobre-intencion-de-replicar-el-hecho-en-paraguay-por-parte-de-un-menor/", "date": "2022-05-26", "written_by": "independent", "quote": "eso detecta la red social y reporta al FBI como también a una red 24/7 en el que está tanto el Ministerio Público como la Policía de Delitos Informáticos" }, { "kind": "news report", "citation": "Última Hora, Glezcu, M. (2022). FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio. Última Hora.", "title": "FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio", "url": "https://www.ultimahora.com/fbi-alerta-la-policia-paraguaya-una-amenaza-tiroteo-un-colegio-n3003665.html", "date": "2022-05-26", "written_by": "independent", "quote": "las alertas llegan constantemente cuando las redes sociales detectan conductas de personas que quieren atentar contra su vida o también de otros tipos de hechos punibles graves" }, { "kind": "news report", "citation": "Diario Hoy (2022). Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó. Hoy.", "title": "Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó", "url": "https://www.hoy.com.py/nacionales/joven-paraguayo-alento-por-redes-a-tiroteo-en-texas-delitos-informaticos-ya-lo-ubico", "date": "2022-05-27", "written_by": "independent", "quote": "Fuimos alertados a través de la red social involucrada y el FBI de Buenos Aires, luego hicimos lo propio en el Departamento de Cibercrimen para identificar al usuario" }, { "kind": "news report", "citation": "Última Hora (2022). Caso alertado por FBI involucra a una víctima del bullying, señalan. Última Hora.", "title": "Caso alertado por FBI involucra a una víctima del bullying, señalan", "url": "https://www.ultimahora.com/caso-alertado-fbi-involucra-una-victima-del-bullying-segun-fiscalia-n3003798.html", "date": "2022-05-27", "written_by": "independent", "quote": "El Ministerio Público también recibió la misma alerta que recibió la Policía Nacional el jueves por parte de la agencia federal de investigación e inteligencia FBI" }, { "kind": "news report", "citation": "Última Hora (2022). Policía Nacional señala que reciben alertas periódicas de redes sociales. Última Hora.", "title": "Policía Nacional señala que reciben alertas periódicas de redes sociales", "url": "https://www.ultimahora.com/delitos-informaticos-senala-que-reciben-alertas-periodicas-redes-sociales-n3003985.html", "date": "2022-05-28", "written_by": "independent", "quote": "una vez corroborados los datos de analítica artificial, nos constituimos en la casa del adolescente" }, { "kind": "news report", "citation": "EFE, via Vanguardia (2022). Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas. Vanguardia.", "title": "Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas", "url": "https://www.vanguardia.com/tecnologia/menor-paraguayo-activa-alerta-de-facebook-tras-mensaje-sobre-masacre-de-texas-XA5244671", "date": "2022-05-27", "written_by": "independent", "quote": "publicó un comentario que la red social catalogó como peligroso" }, { "kind": "press release", "citation": "Meta (n.d.). Detecting violations. Meta Transparency Center.", "title": "Detecting violations", "url": "https://transparency.meta.com/enforcement/detecting-violations/", "date": null, "written_by": "makers", "quote": "Our technology proactively detects and removes the vast majority of violating content before anyone reports it" } ], "replication": { "produced_by": [ "A platform practice of reporting threats of violence to law enforcement in the user's country. Here, via the FBI's Buenos Aires office and a 24/7 police-prosecutor channel.", "A cybercrime unit able to identify an account holder from the platform's primary data within hours", "A welfare rather than punitive response, with prosecutors, the children's defender and psychologists attending" ], "must_hold": [ "The referral must carry enough account data to identify a real person; police said many flagged profiles are fake or jokes", "The response must stay proportionate for a minor; the Monitor flags rights, psychological and reputational risks to the child", "The platform's detection and referral thresholds, which are not public for this case, must stay in place for the alert to recur" ], "echoes": [] }, "paired_incident": null, "headline": "Meta alert brings police and psychologists to a Paraguayan teen", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "Follow-up reports on 27 and 28 May 2022 described the police visit with psychologists and said the cybercrime unit receives such platform alerts routinely, but no later development in this case was found.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-05-27-1b09", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0113", "catalog_version": "1.0.0", "source_fingerprint": "976c61ad62f482e970abb49b78777dfcbf43fa9ab0c98c75a6d47b93f4d3989c", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0113-source-0001", "AIBD-0113-source-0002", "AIBD-0113-source-0003", "AIBD-0113-source-0004", "AIBD-0113-source-0005", "AIBD-0113-source-0006", "AIBD-0113-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Meta Platforms", "deployer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "Officials said the network detected the comment, classified it as dangerous and reported it. It went to the FBI and a 24/7 channel shared by prosecutors and the cybercrime police. Alarcón also spoke of corroborating “datos de analítica artificial” before the visit. No source says the flag was automated. Meta documents that its technology proactively detects most violating content before anyone reports it, but its referrals to law enforcement also pass through human review. Identifying the user, the home visit and the psychological support were the work of Paraguayan police, prosecutors and the child-protection office." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "The 17-year-old, a bullying victim living with a relative, was seen by prosecutors' psychologists, who recommended urgent support (“contención”). The sources report no coercive outcome beyond a planned review of his phone." }, { "group": "societal", "depth": 1, "reason": "A public statement that a school shooting could be repeated in Paraguay was checked within a day, after local media had reported threats in schools. Police said many such alerts prove to be jokes, so no prevented harm is shown." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: six independent news reports and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "The 17-year-old, a bullying victim living with a relative, was seen by prosecutors' psychologists, who recommended urgent support (“contención”). The sources report no coercive outcome beyond a planned review of his phone." }, { "group": "societal", "depth": 1, "reason": "A public statement that a school shooting could be repeated in Paraguay was checked within a day, after local media had reported threats in schools. Police said many such alerts prove to be jokes, so no prevented harm is shown." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "The 17-year-old, a bullying victim living with a relative, was seen by prosecutors' psychologists, who recommended urgent support (“contención”). 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}, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2022-05-27-1b09" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "Follow-up reports on 27 and 28 May 2022 described the police visit with psychologists and said the cybercrime unit receives such platform alerts routinely, but no later development in this case was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0113" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "A threat of a copycat shooting was checked and the minor given psychological support, but no weapon or plan was found and the detection is not shown to be automated." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Thursday 26 May 2022, two days after the Uvalde school shooting, Paraguay's Policía Nacional and Ministerio Público received an alert. It came from Meta's platform and the FBI office in Buenos Aires. A user in Paraguay had tagged the shooter, called himself his “fan” and said something similar could happen in Paraguay with more victims. Comisario Diosnel Alarcón's cybercrime department identified the Instagram profile as a 17-year-old in the “zona central”. He lived with a relative and was treated as a bullying victim. Police, prosecutors, the children's defender and psychologists visited the home and recommended urgent psychological support, and also visited a friend who knew of the posts. No weapon or plan was reported.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "The 17-year-old, a bullying victim living with a relative, was seen by prosecutors' psychologists, who recommended urgent support (“contención”). The sources report no coercive outcome beyond a planned review of his phone." }, { "group": "societal", "depth": 1, "reason": "A public statement that a school shooting could be repeated in Paraguay was checked within a day, after local media had reported threats in schools. Police said many such alerts prove to be jokes, so no prevented harm is shown." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Meta's content-detection and law-enforcement referral process exists to find posts that threaten violence or self-harm. It passes credible threats to the authorities of the user's country. Flagging this post and alerting the police was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Meta's content-detection and law-enforcement referral process exists to find posts that threaten violence or self-harm. It passes credible threats to the authorities of the user's country. Flagging this post and alerting the police was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-05-26", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-05-27-1b09", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A platform practice of reporting threats of violence to law enforcement in the user's country. Here, via the FBI's Buenos Aires office and a 24/7 police-prosecutor channel.", "A cybercrime unit able to identify an account holder from the platform's primary data within hours", "A welfare rather than punitive response, with prosecutors, the children's defender and psychologists attending" ], "must_hold": [ "The referral must carry enough account data to identify a real person; police said many flagged profiles are fake or jokes", "The response must stay proportionate for a minor; the Monitor flags rights, psychological and reputational risks to the child", "The platform's detection and referral thresholds, which are not public for this case, must stay in place for the alert to recur" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-05-27-1b09", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Meta_Platforms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Online safety and threat referral to law enforcement", "segment": "Media, social platforms, and marketing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "La Nación, Paraguay (2022). Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor. La Nación.", "title": "Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor", "url": "https://www.lanacion.com.py/pais/2022/05/26/atentado-en-texas-fbi-alerta-sobre-intencion-de-replicar-el-hecho-en-paraguay-por-parte-de-un-menor/", "date": "2022-05-26", "written_by": "independent", "quote": "eso detecta la red social y reporta al FBI como también a una red 24/7 en el que está tanto el Ministerio Público como la Policía de Delitos Informáticos" }, { "kind": "news report", "citation": "Última Hora, Glezcu, M. (2022). FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio. Última Hora.", "title": "FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio", "url": "https://www.ultimahora.com/fbi-alerta-la-policia-paraguaya-una-amenaza-tiroteo-un-colegio-n3003665.html", "date": "2022-05-26", "written_by": "independent", "quote": "las alertas llegan constantemente cuando las redes sociales detectan conductas de personas que quieren atentar contra su vida o también de otros tipos de hechos punibles graves" }, { "kind": "news report", "citation": "Diario Hoy (2022). Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó. Hoy.", "title": "Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó", "url": "https://www.hoy.com.py/nacionales/joven-paraguayo-alento-por-redes-a-tiroteo-en-texas-delitos-informaticos-ya-lo-ubico", "date": "2022-05-27", "written_by": "independent", "quote": "Fuimos alertados a través de la red social involucrada y el FBI de Buenos Aires, luego hicimos lo propio en el Departamento de Cibercrimen para identificar al usuario" }, { "kind": "news report", "citation": "Última Hora (2022). Caso alertado por FBI involucra a una víctima del bullying, señalan. Última Hora.", "title": "Caso alertado por FBI involucra a una víctima del bullying, señalan", "url": "https://www.ultimahora.com/caso-alertado-fbi-involucra-una-victima-del-bullying-segun-fiscalia-n3003798.html", "date": "2022-05-27", "written_by": "independent", "quote": "El Ministerio Público también recibió la misma alerta que recibió la Policía Nacional el jueves por parte de la agencia federal de investigación e inteligencia FBI" }, { "kind": "news report", "citation": "Última Hora (2022). Policía Nacional señala que reciben alertas periódicas de redes sociales. Última Hora.", "title": "Policía Nacional señala que reciben alertas periódicas de redes sociales", "url": "https://www.ultimahora.com/delitos-informaticos-senala-que-reciben-alertas-periodicas-redes-sociales-n3003985.html", "date": "2022-05-28", "written_by": "independent", "quote": "una vez corroborados los datos de analítica artificial, nos constituimos en la casa del adolescente" }, { "kind": "news report", "citation": "EFE, via Vanguardia (2022). Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas. Vanguardia.", "title": "Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas", "url": "https://www.vanguardia.com/tecnologia/menor-paraguayo-activa-alerta-de-facebook-tras-mensaje-sobre-masacre-de-texas-XA5244671", "date": "2022-05-27", "written_by": "independent", "quote": "publicó un comentario que la red social catalogó como peligroso" }, { "kind": "press release", "citation": "Meta (n.d.). Detecting violations. Meta Transparency Center.", "title": "Detecting violations", "url": "https://transparency.meta.com/enforcement/detecting-violations/", "written_by": "makers", "quote": "Our technology proactively detects and removes the vast majority of violating content before anyone reports it" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0113-source-0001", "AIBD-0113-source-0002", "AIBD-0113-source-0003", "AIBD-0113-source-0004", "AIBD-0113-source-0005", "AIBD-0113-source-0006", "AIBD-0113-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "La Nación, Paraguay (2022). Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor. La Nación.", "title": "Atentado en Texas: FBI alerta sobre intención de replicar el hecho en Paraguay por parte de un menor", "url": "https://www.lanacion.com.py/pais/2022/05/26/atentado-en-texas-fbi-alerta-sobre-intencion-de-replicar-el-hecho-en-paraguay-por-parte-de-un-menor/", "date": "2022-05-26", "written_by": "independent", "quote": "eso detecta la red social y reporta al FBI como también a una red 24/7 en el que está tanto el Ministerio Público como la Policía de Delitos Informáticos" }, { "kind": "news report", "citation": "Última Hora, Glezcu, M. (2022). FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio. Última Hora.", "title": "FBI alerta a la Policía paraguaya de una amenaza de tiroteo en un colegio", "url": "https://www.ultimahora.com/fbi-alerta-la-policia-paraguaya-una-amenaza-tiroteo-un-colegio-n3003665.html", "date": "2022-05-26", "written_by": "independent", "quote": "las alertas llegan constantemente cuando las redes sociales detectan conductas de personas que quieren atentar contra su vida o también de otros tipos de hechos punibles graves" }, { "kind": "news report", "citation": "Diario Hoy (2022). Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó. Hoy.", "title": "Joven paraguayo alentó por redes a tiroteo en Texas: FBI alertó y la Policía ya lo ubicó", "url": "https://www.hoy.com.py/nacionales/joven-paraguayo-alento-por-redes-a-tiroteo-en-texas-delitos-informaticos-ya-lo-ubico", "date": "2022-05-27", "written_by": "independent", "quote": "Fuimos alertados a través de la red social involucrada y el FBI de Buenos Aires, luego hicimos lo propio en el Departamento de Cibercrimen para identificar al usuario" }, { "kind": "news report", "citation": "Última Hora (2022). Caso alertado por FBI involucra a una víctima del bullying, señalan. Última Hora.", "title": "Caso alertado por FBI involucra a una víctima del bullying, señalan", "url": "https://www.ultimahora.com/caso-alertado-fbi-involucra-una-victima-del-bullying-segun-fiscalia-n3003798.html", "date": "2022-05-27", "written_by": "independent", "quote": "El Ministerio Público también recibió la misma alerta que recibió la Policía Nacional el jueves por parte de la agencia federal de investigación e inteligencia FBI" }, { "kind": "news report", "citation": "Última Hora (2022). Policía Nacional señala que reciben alertas periódicas de redes sociales. Última Hora.", "title": "Policía Nacional señala que reciben alertas periódicas de redes sociales", "url": "https://www.ultimahora.com/delitos-informaticos-senala-que-reciben-alertas-periodicas-redes-sociales-n3003985.html", "date": "2022-05-28", "written_by": "independent", "quote": "una vez corroborados los datos de analítica artificial, nos constituimos en la casa del adolescente" }, { "kind": "news report", "citation": "EFE, via Vanguardia (2022). Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas. Vanguardia.", "title": "Menor paraguayo activa alerta de Facebook tras mensaje sobre masacre de Texas", "url": "https://www.vanguardia.com/tecnologia/menor-paraguayo-activa-alerta-de-facebook-tras-mensaje-sobre-masacre-de-texas-XA5244671", "date": "2022-05-27", "written_by": "independent", "quote": "publicó un comentario que la red social catalogó como peligroso" }, { "kind": "press release", "citation": "Meta (n.d.). Detecting violations. Meta Transparency Center.", "title": "Detecting violations", "url": "https://transparency.meta.com/enforcement/detecting-violations/", "written_by": "makers", "quote": "Our technology proactively detects and removes the vast majority of violating content before anyone reports it" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Meta flagged a Paraguayan teen's post praising the Uvalde shooter; the FBI relayed it and police, prosecutors and psychologists intervened.", "account": "On Thursday 26 May 2022, two days after the Uvalde school shooting, Paraguay's Policía Nacional and Ministerio Público received an alert. It came from Meta's platform and the FBI office in Buenos Aires. A user in Paraguay had tagged the shooter, called himself his “fan” and said something similar could happen in Paraguay with more victims. Comisario Diosnel Alarcón's cybercrime department identified the Instagram profile as a 17-year-old in the “zona central”. He lived with a relative and was treated as a bullying victim. Police, prosecutors, the children's defender and psychologists visited the home and recommended urgent psychological support, and also visited a friend who knew of the posts. No weapon or plan was reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Meta (Facebook/Instagram) violence-threat detection and law-enforcement referral process" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Meta threat alert, relayed through the FBI, brings Paraguayan police and psychologists to a bullied 17-year-old who praised the Uvalde shooter", "headline": "Meta alert brings police and psychologists to a Paraguayan teen" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "976c61ad62f482e970abb49b78777dfcbf43fa9ab0c98c75a6d47b93f4d3989c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On Thursday 26 May 2022, two days after the Uvalde school shooting, Paraguay's Policía Nacional and Ministerio Público received an alert. It came from Meta's platform and the FBI office in Buenos Aires. A user in Paraguay had tagged the shooter, called himself his “fan” and said something similar could happen in Paraguay with more victims. Comisario Diosnel Alarcón's cybercrime department identified the Instagram profile as a 17-year-old in the “zona central”. He lived with a relative and was treated as a bullying victim. Police, prosecutors, the children's defender and psychologists visited the home and recommended urgent psychological support, and also visited a friend who knew of the posts. No weapon or plan was reported.", "replication": { "produced_by": [ "A platform practice of reporting threats of violence to law enforcement in the user's country. Here, via the FBI's Buenos Aires office and a 24/7 police-prosecutor channel.", "A cybercrime unit able to identify an account holder from the platform's primary data within hours", "A welfare rather than punitive response, with prosecutors, the children's defender and psychologists attending" ], "must_hold": [ "The referral must carry enough account data to identify a real person; police said many flagged profiles are fake or jokes", "The response must stay proportionate for a minor; the Monitor flags rights, psychological and reputational risks to the child", "The platform's detection and referral thresholds, which are not public for this case, must stay in place for the alert to recur" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0111", "case_url": "records/AIBD-0111.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function." }, { "label": "AI contribution", "value": "The alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake and estimate its size. Alerts were pushed to phones in the expected shaking zone ahead of the slower S-waves. Google's own descriptions of this process do not use the words AI or machine learning. The OECD Monitor classifies it as an AI system doing event detection and forecasting, and the 2025 Science paper calls it smartphone-based earthquake detection algorithms. The reported lead time varies by outlet (seconds, about 10 seconds, about one minute); Google promises seconds." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Turkey" ] }, "record": { "id": "AIBD-0111", "title": "Google's Android Earthquake Alerts reached Alanya phones seconds before the 5 January 2022 M5.1 (AFAD 5.3) Mediterranean quake", "summary": "Android users near Alanya, Turkey, got Google's earthquake alert seconds before a M5.1 (AFAD 5.3) offshore quake on 5 January 2022.", "date_observed": "2022-01-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "category": "protective response", "unexpected": false, "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function.", "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)", "model_version": null, "developer": "Google", "deployer": "Google", "location": "Alanya, Turkey", "domain": "Disaster risk reduction / earthquake early warning", "segment": "Consumer smartphones (Android)", "benefit_type": "Increased Product Safety", "taxonomy_note": "The alert arrived seconds before shaking, but no protective action by a recipient and no harm are documented.", "registry_ids": { "oecd_aim": "2022-01-18-136b" }, "ratings": [ { "group": "personal", "depth": 1, "reason": "An unstated number of Android users near Alanya received a few seconds' audible and visual notice of the quake. AFAD and the district governor said it caused no damage or injuries. No protective action by a recipient is documented. Hürriyet reported on 7 January 2022 that most users who posted about the alert said they panicked." } ], "ai_role": { "status": "assessed", "statement": "The alert was produced end to end by Google's system. Accelerometer triggers from many phones were combined on Google's servers to detect the quake and estimate its size. Alerts were pushed to phones in the expected shaking zone ahead of the slower S-waves. Google's own descriptions of this process do not use the words AI or machine learning. The OECD Monitor classifies it as an AI system doing event detection and forecasting, and the 2025 Science paper calls it smartphone-based earthquake detection algorithms. The reported lead time varies by outlet (seconds, about 10 seconds, about one minute); Google promises seconds." }, "evidence": [ { "kind": "news report", "citation": "Yeni Şafak (2022). Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "date": null, "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively." } ], "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. Hürriyet reported on 7 January 2022 that most users who posted about the Alanya alert said they panicked", "The phone must have location and the Earthquake Alerts setting enabled" ], "echoes": [] }, "paired_incident": null, "headline": "Google alerts Alanya phones seconds before a M5.1 quake", "metric": "Hürriyet put the lead at about 10 seconds", "comparator": null, "later": { "status": "persisted", "date": "2022-02-03", "note": "At a 3 February 2022 press event Google said the Android system had alerted users to the two January quakes centered on Alanya and Balıkesir.", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi hakkında detaylı bilgi vermek ve depremde saniyelerin önemini tekrar vurgulamak üzere Google bir basın toplantısı düzenledi.", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-01-18-136b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: nine independent news reports, one independent dataset, the makers' peer-reviewed study and three press releases from the makers. 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"At a 3 February 2022 press event Google said the Android system had alerted users to the two January quakes centered on Alanya and Balıkesir.", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi hakkında detaylı bilgi vermek ve depremde saniyelerin önemini tekrar vurgulamak üzere Google bir basın toplantısı düzenledi.", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": 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rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 1, "reason": "An unstated number of Android users near Alanya received a few seconds' audible and visual notice of the quake. AFAD and the district governor said it caused no damage or injuries. No protective action by a recipient is documented. Hürriyet reported on 7 January 2022 that most users who posted about the alert said they panicked." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Alanya, Turkey", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect earthquakes from Android phones' accelerometers and alert users in the expected shaking zone seconds before the slower destructive waves arrive. It covers quakes of magnitude 4.5 and above. This alert was that intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-01-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2022-01-18-136b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://en.wikipedia.org/wiki/Google", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. 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Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. 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Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı. Yeni Şafak, 5 January 2022.", "title": "Google depremi önceden bildi: Antalya depremi hissedilmeden kullanıcılarını uyardı", "url": "https://www.yenisafak.com/teknoloji/google-android-deprem-uyari-sistemi-nasil-acilir-deprem-uyari-sistemi-nedir-deprem-uyari-sistemi-hangi-telefonlarda-acilir-3728848", "date": "2022-01-05", "written_by": "independent", "quote": "Depremden saniyeler önce bazı Android kullanıcıları, Google Android’in deprem erken uyarı sisteminden depreme dair telefonlarına 5.3 büyüklüğünde bir depremin olacağına dair sesli ve görsel bir uyarı mesajı gönderildi." }, { "kind": "news report", "citation": "DonanımHaber (2022). Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi. DonanımHaber, 5 January 2022.", "title": "Google erken uyarı sistemi, Alanya depremini saniyeler öncesinden bildi", "url": "https://www.donanimhaber.com/google-erken-uyari-sistemi-alanya-depremini-onceden-bildi--143452", "date": "2022-01-05", "written_by": "independent", "quote": "Google deprem uyarı sistemi, erken saatlerde meydana gelen depremden saniyeler önce, o bölgede oturan kullanıcılara deprem uyarı sistemi aracılığıyla bir uyarı mesajı gönderdi." }, { "kind": "news report", "citation": "Öztürk T (2022). Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi? Hürriyet, 7 January 2022.", "title": "Erken uyarı sistemi tartışması! Depremin olacağını 10 saniye önceden bilmek güvenli mi?", "url": "https://www.hurriyet.com.tr/gundem/erken-uyari-sistemi-tartismasi-depremin-olacagini-10-saniye-onceden-bilmek-guvenli-mi-41976168", "date": "2022-01-07", "written_by": "independent", "quote": "Depremden yaklaşık 10 saniye önce, cep telefonlarına 'deprem olacak' bildirimi gitti." }, { "kind": "news report", "citation": "Hürriyet (2022). Google, Android Deprem Uyarı Sistemi’ni tanıttı. Hürriyet, 3 February 2022.", "title": "Google, Android Deprem Uyarı Sistemi’ni tanıttı", "url": "https://www.hurriyet.com.tr/teknoloji/google-android-deprem-uyari-sistemini-tanitti-41995996", "date": "2022-02-03", "written_by": "independent", "quote": "Ocak ayında ülkemizde merkezi Alanya ve Balıkesir olmak üzere 4.5 büyüklüğü üzeri iki depreme dair Android kullanıcılarına uyarı bildirisi gönderen Android Deprem Uyarı Sistemi" }, { "kind": "news report", "citation": "İndigo Dergisi (2022). Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi? İndigo Dergisi, 5 January 2022.", "title": "Google Deprem Uyarı Sistemi nasıl açılır? Alanya depremini 1 dakika önceden nasıl haber verdi?", "url": "https://indigodergisi.com/2022/01/google-deprem-uyari-sistemi-nasil-ailir/", "date": "2022-01-05", "written_by": "independent", "quote": "AFAD’dan yapılan açıklamada “Akdeniz’de Antalya’nın Alanya ilçesi açıklarında saat 06.21’de 5,3 büyüklüğünde bir deprem meydana geldi. Gelişmeleri takip ediyoruz” denildi." }, { "kind": "news report", "citation": "NTV (2022). Deprem uyarı sistemi nedir, nasıl çalışır? NTV, 18 January 2022.", "title": "Deprem uyarı sistemi nedir, nasıl çalışır?", "url": "https://www.ntv.com.tr/turkiye/deprem-uyari-sistemi-nedir-nasil-calisir,PtR3VF38DUOdDzApupgnQQ", "date": "2022-01-18", "written_by": "independent", "quote": "Geçtiğimiz haftalarda Google'ın sunduğu deprem uyarı sistemi, Alanya açıklarında meydana gelen 5.3'lük depremi 1 dakika öncesinden tahmin ederek kullanıcılarına sesli uyarı göndermişti." }, { "kind": "news report", "citation": "Yeni Akit (2022). Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır? Yeni Akit, 18 January 2022.", "title": "Android deprem uyarı sistemi nedir, nasıl yapılır, nasıl kullanılır?", "url": "https://www.yeniakit.com.tr/haber/android-deprem-uyari-sistemi-nedir-nasil-yapilir-nasil-kullanilir-1618101.html", "date": "2022-01-18", "written_by": "independent", "quote": "Android deprem uyarı sistemi sayesinde 5 Ocak 2022 sabahı Antalya’da meydana gelen depremin kısa süre öncesinde vatandaşlara uyarı yollandı." }, { "kind": "news report", "citation": "Hürriyet (2022). Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları. Hürriyet, 18 January 2022.", "title": "Deprem uyarı sistemi nasıl yapılır, nedir? Android erken deprem uyarı sistemi kullanım detayları", "url": "https://www.hurriyet.com.tr/galeri-deprem-uyari-sistemi-nasil-yapilir-nedir-android-erken-deprem-uyari-sistemi-kullanim-detaylari-41984292", "date": "2022-01-18", "written_by": "independent", "quote": "Olası bir depremi 10 saniye önceden haber verebilen deprem uyarı sistemi Alanya'da yaşanan depremi gerçekleşmeden 1 dakika kadar önce bölgede oturanlara Google tarafından bildirilmişti." }, { "kind": "dataset", "citation": "U.S. Geological Survey (2022). M 5.1 - 67 km SSW of Okurcalar, Turkey (event us7000g94d). USGS Earthquake Hazards Program.", "title": "M 5.1 - 67 km SSW of Okurcalar, Turkey", "url": "https://earthquake.usgs.gov/earthquakes/eventpage/us7000g94d", "date": "2022-01-05", "written_by": "independent", "quote": "M 5.1 - 67 km SSW of Okurcalar, Turkey" }, { "kind": "news report", "citation": "Duckett C (2021). Android opens up earthquake alerts and end-to-end encrypted messages. ZDNet, 16 June 2021.", "title": "Android opens up earthquake alerts and end-to-end encrypted messages", "url": "https://www.zdnet.com/article/android-opens-up-earthquake-alerts-and-end-to-end-encrypted-messages/", "date": "2021-06-16", "written_by": "independent", "quote": "The system is already live in New Zealand and Greece, with Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan coming on board." }, { "kind": "press release", "citation": "Lee F (2021). 6 new features on Android this summer. Google, The Keyword, 15 June 2021.", "title": "6 new features on Android this summer", "url": "https://blog.google/products-and-platforms/platforms/android/new-features-summer-2021/", "date": "2021-06-15", "written_by": "makers", "quote": "Today, we're introducing the Android Earthquake Alerts System in Turkey, the Philippines, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan." }, { "kind": "press release", "citation": "Stogaitis M (2020). Earthquake detection and early alerts, now on your Android phone. Google, The Keyword, 11 August 2020.", "title": "Earthquake detection and early alerts, now on your Android phone", "url": "https://blog.google/products/android/earthquake-detection-and-alerts/", "date": "2020-08-11", "written_by": "makers", "quote": "The server then combines information from many phones to figure out if an earthquake is happening." }, { "kind": "press release", "citation": "Google (n.d.). Android early earthquake warnings. Google Crisis Resilience.", "title": "Android early earthquake warnings", "url": "https://crisisresilience.google/android-early-earthquake-warnings", "written_by": "makers", "quote": "These alerts are only sent for earthquakes of magnitude 4.5 or greater." }, { "kind": "peer-reviewed study", "citation": "Allen RM, Barski A, Berman M, et al. (2025). Global earthquake detection and warning using Android phones. Science 389(6757):254-259.", "title": "Global earthquake detection and warning using Android phones", "url": "https://doi.org/10.1126/science.ads4779", "date": "2025-07-17", "written_by": "makers", "quote": "User feedback shows that 85% of people receiving an alert felt shaking, and 36, 28, and 23% received the alert before, during, and after shaking, respectively." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Android users near Alanya, Turkey, got Google's earthquake alert seconds before a M5.1 (AFAD 5.3) offshore quake on 5 January 2022.", "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Android Earthquake Alerts System (crowdsourced phone-accelerometer detection; alerts for magnitude 4.5 and above)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Google" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Google's Android Earthquake Alerts reached Alanya phones seconds before the 5 January 2022 M5.1 (AFAD 5.3) Mediterranean quake", "headline": "Google alerts Alanya phones seconds before a M5.1 quake" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "65b16e850751b1199c57592eb6bfa2df4ce99dc8579627ff3f0d2be754b77e98" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At 06:21 local time on 5 January 2022 a magnitude 5.1 (USGS; AFAD 5.3) earthquake struck the Mediterranean about 57 km off Alanya, Antalya, Turkey. Depth: roughly 40 km. Yeni Şafak and DonanımHaber reported the same day on Google's Android Earthquake Alerts System, introduced in Turkey in June 2021. It sent an audible and visual alert for a magnitude 5.3 quake to Android users seconds before the shaking. Hürriyet put the lead at about 10 seconds, while NTV and İndigo Dergisi claimed about a minute. AFAD and the Alanya district governor reported no damage or injuries. On 3 February 2022 Google said the system had alerted users to two January quakes centered on Alanya and Balıkesir.", "replication": { "produced_by": [ "A magnitude 5.1 (AFAD 5.3) quake above the 4.5 alert threshold, centered about 57 km offshore and roughly 40 km deep. So the push alert could outrun the slower S-waves to the coast.", "Enough Android phones near the epicenter with Earthquake Alerts on to trigger Google's server-side detection. Per Google's 3 February 2022 presentation it runs on Android 5.0 and later and users can switch it off.", "Crowdsourced phone detection was the only input: Google partners with the ShakeAlert seismic network only in California, Oregon and Washington" ], "must_hold": [ "The server's magnitude and location estimate must be right and early. The 2025 Science paper reports only 36% of alert recipients got the alert before shaking.", "Recipients must know what to do with seconds of notice. Hürriyet reported on 7 January 2022 that most users who posted about the Alanya alert said they panicked", "The phone must have location and the Earthquake Alerts setting enabled" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0345", "case_url": "records/AIBD-0345.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Satellite-based detection and notification of large methane plumes so that governments and operators mitigate them. The reported repairs are that purpose." }, { "label": "AI contribution", "value": "UNEP identifies AI as part of the satellite-analysis system that generates notifications and supports subsequent monitoring. Its public index names eight Turkmenistan mitigation cases. It does not isolate the AI contribution to each repair." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Turkmenistan" ] }, "record": { "id": "AIBD-0345", "title": "UNEP records eight methane mitigations in Turkmenistan after MARS notifications", "summary": "UNEP lists eight mitigation cases, with site identifiers and dates from November 2024 to March 2026. The contribution of AI is described at system level, rather than isolated for each repair.", "date_observed": "2026-08-31", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNEP’s MARS mitigation index lists eight Turkmenistan cases: two in Yelguyi in November 2024, Babarap in June 2025, Gugurtli field in July 2025, Balguyy in August 2025, two in Dauletabad in February 2026, and Lebap Province in March 2026. Each entry has a source identifier and a link to an individual case document. UNEP describes these as mitigations that its notification and monitoring system helped prompt and verify. UNEP states that MARS combines satellite observations, scientific review, and AI to identify emissions for government and company action. The available aggregate evidence does not isolate AI’s contribution from analysts, notifications, and physical repairs or quantify their combined benefit.", "category": "general benefit", "unexpected": false, "purpose": "Satellite-based detection and notification of large methane plumes so that governments and operators mitigate them. The reported repairs are that purpose.", "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); Government of Turkmenistan and its oil and gas operators (repairs; entities not named in cited sources)", "location": "Turkmenistan: Yelguyi, Babarap, Gugurtli field, Balguyy, Dauletabad, and Lebap Province", "domain": "Climate change mitigation / methane emissions monitoring", "segment": "Oil and gas sector; intergovernmental observatory", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual benefit is established." }, { "group": "organizational", "depth": null, "reason": "UNEP identifies the mitigation sites, but the contribution to a named operator’s capability or finances has not been assessed." }, { "group": "societal", "depth": null, "reason": "UNEP reports eight mitigations. This record does not establish their combined avoided emissions or assess the depth of the societal benefit." } ], "ai_role": { "status": "assessed", "statement": "UNEP identifies AI as part of the satellite-analysis system that generates notifications and supports subsequent monitoring. Its public index names eight Turkmenistan mitigation cases. It does not isolate the AI contribution to each repair." }, "evidence": [ { "kind": "report", "citation": "UNEP International Methane Emissions Observatory. MARS Mitigation Cases. n.d. Central Asia section lists eight Turkmenistan mitigation cases dated November 2024–March 2026, with individual case links.", "title": "MARS Mitigation Cases", "url": "https://www.unep.org/en/what-we-do/energy/methane/mars-case-studies", "date": null, "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "Damian Carrington, The Guardian. ‘Breakthrough’ as Turkmenistan starts fixing ‘mindboggling’ methane mega-leaks. 2026-08-31. Original interviews and contemporaneous alert-response totals.", "title": "‘Breakthrough’ as Turkmenistan starts fixing ‘mindboggling’ methane mega-leaks", "url": "https://www.theguardian.com/environment/2026/aug/31/turkmenistan-methane-leaks-un-alerts-mars", "date": "2026-08-31", "written_by": "independent", "quote": null }, { "kind": "report", "citation": "UNEP International Methane Emissions Observatory. Methane Alert and Response System (MARS). n.d. Original description of satellite analysis, AI, and notifications.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/en/what-we-do/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Satellite detection and analysis, notification to government or operators, and physical mitigation that UNEP reports as verified" ], "must_hold": [ "Operators can identify and address the notified emissions source", "Continued monitoring distinguishes durable mitigation from temporary non-detection", "Site-level evidence is needed to establish each repair and its effects" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: a single documented MARS-enabled pipeline mitigation in a neighboring country (UNEP case document)", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" } ] }, "paired_incident": null, "headline": "UNEP records eight methane mitigations in Turkmenistan", "metric": "eight listed mitigation cases; 192 alerts and about 20% answered in the August 2026 report", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The UNEP index names and dates eight mitigation cases. Their continued performance is not established by the aggregate account.", "quote": null, "url": "https://www.unep.org/en/what-we-do/energy/methane/mars-case-studies" }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced derivative summaries with UNEP’s original case index and the original Guardian reporting. Corrected the assertion that site-level documents do not exist.", "source": "https://www.unep.org/en/what-we-do/energy/methane/mars-case-studies", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the makers' report and the deployer's report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0345", "catalog_version": "1.0.0", "source_fingerprint": "99d559a555e9dd72aff880cf95666fa1cd18d56f5ab7dc5148de450fe943f190", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); Government of Turkmenistan and its oil and gas operators (repairs; entities not named in cited sources)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "UNEP identifies AI as part of the satellite-analysis system that generates notifications and supports subsequent monitoring. 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Original description of satellite analysis, AI, and notifications." } ], "excluded_republications": [ { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem", "published_on": "2026-09-03", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0001" }, { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem (repairs)", "published_on": "2026-09-03", "relationship": "Repeated quotation or alternate version of a retained original work", "previous_source_ref": "AIBD-0345-source-0002" }, { "url": "https://eurasianet.org/turkmenistan-tackling-methane-leak-problem", "title": "Turkmenistan tackling methane leak problem (cases addressed)", "published_on": "2026-09-03", "relationship": "Repeated quotation or alternate version of a retained original work", "previous_source_ref": "AIBD-0345-source-0003" }, { "url": "https://www.thechemicalengineer.com/news/un-satellite-programme-curbs-more-than-40-methane-super-emitters-since-2023/", "title": "UN satellite programme curbs more than 40 methane 'super-emitters' since 2023", "published_on": "2026-05-05", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0004" }, { "url": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/", "title": "Canada plugs 'major source' of methane emissions from oil and gas field leak", "published_on": "2026-09-04", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0005" }, { "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "title": "Methane Alert and Response System (MARS)", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0345-source-0006" } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0345-source-0001", "retained_source_order": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "ordering_basis": "The source that directly supports the central case leads. Additional sources contribute the specific evidence or limitations described in claim_lineage.", "claim_lineage": [ { "claim": "Eight named mitigation cases and dates", "source_chain": [ { "url": "https://www.unep.org/en/what-we-do/energy/methane/mars-case-studies", "locator": "Central Asia, Turkmenistan entries TKM_S_406, 451, 452, 118, 053, 071, 080, and 081", "role": "Original UNEP mitigation index", "access_status": "Full relevant text reviewed" }, { "url": "https://wedocs.unep.org/items/5c86c2bd-db0e-49ab-98ed-f9d4730b1af4", "locator": "TKM_S_406, Lebap Province, March 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/ab7219e7-8192-4ad3-91f5-1c93b440ec48", "locator": "TKM_S_451, Dauletabad, February 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/24553364-60bb-4cd9-841f-3e8ca35dfc3a", "locator": "TKM_S_452, Dauletabad, February 2026", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/fa3e52ea-97bd-4009-b5f2-7dafb2a5a4e9", "locator": "TKM_S_118, Balguyy, August 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/1bc9fee8-eb37-4f2a-b690-0f7ee470c8c5", "locator": "TKM_S_053, Gugurtli field, July 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/490eba84-19e5-41eb-93fe-e885f74cb68e", "locator": "TKM_S_071, Babarap, June 2025", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/12065ac0-2bee-4097-a140-7c94924603af", "locator": "TKM_S_080, Yelguyi, November 2024", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" }, { "url": "https://wedocs.unep.org/items/ba302a6c-8b00-404e-9e5a-ec6912f65250", "locator": "TKM_S_081, Yelguyi, November 2024", "role": "Linked original mitigation case document", "access_status": "Link identified in original index; document request timed out or failed" } ], "limitations": [ "The index supports named cases and dates; underlying sheets were inaccessible, so repair procedures and emissions quantities were not independently checked" ], "source_ref": "AIBD-0345-source-0001" }, { "claim": "192 alerts and roughly 20% response rate", "source_chain": [ { "url": "https://www.theguardian.com/environment/2026/aug/31/turkmenistan-methane-leaks-un-alerts-mars", "locator": "Contemporaneous MARS totals and direct interview", "role": "Original reporting", "access_status": "Full relevant text reviewed", "published_on": "2026-08-31" }, { "url": "https://methanedata.unep.org/", "locator": "August 2026 MARS snapshot underlying the reported totals", "role": "UNEP original data platform", "access_status": "Exact archived August snapshot not retrieved" } ], "limitations": [ "Historical totals could not be recomputed from the underlying snapshot" ], "source_ref": "AIBD-0345-source-0002" }, { "claim": "AI contribution to MARS", "source_chain": [ { "url": "https://www.unep.org/en/what-we-do/energy/methane/methane-alert-and-response-system-mars", "locator": "What is MARS?", "role": "Original system description", "access_status": "Full relevant text reviewed" } ], "limitations": [ "System-level AI contribution is not an isolated per-repair causal estimate" ], "source_ref": "AIBD-0345-source-0003" } ], "limitations": [ "Eight linked case sheets and the historical snapshot remain uninspected" ], "chronology_source": { "url": "https://www.theguardian.com/environment/2026/aug/31/turkmenistan-methane-leaks-un-alerts-mars", "locator": "Original aggregate report predating Eurasianet and The Chemical Engineer", "role": "First verified publication of the aggregate case", "access_status": "Full relevant text reviewed", "published_on": "2026-08-31", "date": "2026-08-31", "title": "‘Breakthrough’ as Turkmenistan starts fixing ‘mindboggling’ methane mega-leaks" } } }, "provenance": { "kind": "source_review", "basis": "Replaced derivative summaries with UNEP’s original case index and the original Guardian reporting. Corrected the assertion that site-level documents do not exist.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Eight linked case sheets and the historical website snapshot remain uninspected.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "UNEP lists eight mitigation cases, with site identifiers and dates from November 2024 to March 2026. The contribution of AI is described at system level, rather than isolated for each repair.", "account": "UNEP’s MARS mitigation index lists eight Turkmenistan cases: two in Yelguyi in November 2024, Babarap in June 2025, Gugurtli field in July 2025, Balguyy in August 2025, two in Dauletabad in February 2026, and Lebap Province in March 2026. Each entry has a source identifier and a link to an individual case document. UNEP describes these as mitigations that its notification and monitoring system helped prompt and verify. UNEP states that MARS combines satellite observations, scientific review, and AI to identify emissions for government and company action. The available aggregate evidence does not isolate AI’s contribution from analysts, notifications, and physical repairs or quantify their combined benefit." } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP IMEO Methane Alert and Response System (MARS): satellite plume detection and analysis with AI models, notification and satellite confirmation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP records eight methane mitigations in Turkmenistan after MARS notifications", "headline": "UNEP records eight methane mitigations in Turkmenistan" } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "99d559a555e9dd72aff880cf95666fa1cd18d56f5ab7dc5148de450fe943f190" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version after the October 24, 2026 primary-source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP’s MARS mitigation index lists eight Turkmenistan cases: two in Yelguyi in November 2024, Babarap in June 2025, Gugurtli field in July 2025, Balguyy in August 2025, two in Dauletabad in February 2026, and Lebap Province in March 2026. Each entry has a source identifier and a link to an individual case document. UNEP describes these as mitigations that its notification and monitoring system helped prompt and verify. UNEP states that MARS combines satellite observations, scientific review, and AI to identify emissions for government and company action. The available aggregate evidence does not isolate AI’s contribution from analysts, notifications, and physical repairs or quantify their combined benefit.", "replication": { "produced_by": [ "Satellite detection and analysis, notification to government or operators, and physical mitigation that UNEP reports as verified" ], "must_hold": [ "Operators can identify and address the notified emissions source", "Continued monitoring distinguishes durable mitigation from temporary non-detection", "Site-level evidence is needed to establish each repair and its effects" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan KAZ_S_136: a single documented MARS-enabled pipeline mitigation in a neighboring country (UNEP case document)", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" } ] } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0345-source-0001", "AIBD-0345-source-0002", "AIBD-0345-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 4, "record_mapped": 25, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0246", "case_url": "records/AIBD-0246.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The system is built to detect people left inside a closed crossing and stop approaching trains automatically." }, { "label": "AI contribution", "value": "The sources describe the intended detection-to-brake chain and the trial period; no detections, false alarms or accuracy figures are reported." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0246", "title": "Odakyu Electric Railway starts an AI crossing image-analysis system at four Odawara Line crossings that signals approaching trains to stop when people are left inside", "summary": "ITmedia (22 Jun 2026): from 24 Jun 2026 AI analyzes existing crossing cameras, tracks pedestrians, wheelchair users, cyclists and motorcyclists and sends a brake signal to approaching trains; devel...", "date_observed": "2026-06-24", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "ITmedia's 岡田有花 reported on 22 Jun 2026 that Odakyu Electric Railway would introduce an “AI踏切画像解析システム” from 24 June 2026. It covers four crossings on its Odawara Line. They are Minami-Shinjuku Nos. 2, 4 and 5 between Minami-Shinjuku and Sangubashi stations, and Mukogaoka-Yuen No. 9 between Mukogaoka-Yuen and Ikuta stations. The AI analyzes footage from the existing safety-confirmation cameras in real time. It identifies and tracks pedestrians, wheelchair users, bicycles and motorcycles. When it detects someone left inside after the barriers come down, it sends a signal through the signaling equipment. That applies the brakes of approaching trains and lights the crossing's special signal lamps to alert crews. Both are released automatically once the crossing is clear. Images are used only for hazard detection, not to identify individuals, and analysis data are destroyed within a year. The system was developed by Meitetsu EI Engineer, Toyota Systems and Toho Electric Industries. It was trialed from January 2023, with work on detection accuracy at night and in rain. railf.jp carried the same announcement on 25 Jun 2026. No detection events or outcomes are reported.", "category": "general benefit", "unexpected": false, "purpose": "The system is built to detect people left inside a closed crossing and stop approaching trains automatically.", "system": "AI踏切画像解析システム (AI crossing image-analysis system) using existing crossing cameras linked to signaling equipment", "model_version": null, "developer": "Meitetsu EI Engineer, Toyota Systems and Toho Electric Industries", "deployer": "Odakyu Electric Railway", "location": null, "domain": "Railway safety", "segment": "Level-crossing obstacle detection", "benefit_type": "Increased Product Safety", "taxonomy_note": "Launch only; the automatic brake signal is a failsafe with no stop event documented.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "No outcome is documented; the sources describe the start of operation." }, { "group": "organizational", "depth": null, "reason": "No operational outcome is documented." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "unassessed", "statement": "The sources describe the intended detection-to-brake chain and the trial period; no detections, false alarms or accuracy figures are reported." }, "evidence": [ { "kind": "news report", "citation": "岡田有花 (22 Jun 2026). 踏切に取り残された人をAIで検知→列車を自動停止 小田急が実運用. ITmedia NEWS.", "title": "踏切に取り残された人をAIで検知→列車を自動停止 小田急が実運用", "url": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html", "date": "2026-06-22", "written_by": "independent", "quote": "名鉄EIエンジニア、トヨタシステムズ、東邦電機工業が開発し、2023年1月からの実証を行ってきた。" }, { "kind": "news report", "citation": "railf.jp (25 Jun 2026). 小田急，AIを活用した踏切画像解析システムを導入.", "title": "小田急，AIを活用した踏切画像解析システムを導入", "url": "https://railf.jp/news/2026/06/25/041500.html", "date": "2026-06-25", "written_by": "independent", "quote": "踏切内の取り残しが解消された場合は、これらの措置は自動で解除される" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-06-22-b4f8: AI System Deployed to Prevent Railway Crossing Accidents in Japan.", "title": "AI System Deployed to Prevent Railway Crossing Accidents in Japan", "url": "https://oecd.ai/en/incidents/2026-06-22-b4f8", "date": "2026-06-22", "written_by": "independent", "quote": "The system analyzes real-time camera footage to detect pedestrians, cyclists, and others left inside crossings, automatically triggering train stops to prevent accidents." } ], "replication": { "produced_by": [ "Existing crossing cameras with sufficient coverage of the enclosed area", "Integration with signaling equipment able to command train braking", "Three years of trials covering night and rain" ], "must_hold": [ "Detection accuracy holds in poor weather and lighting", "False detections do not stop trains so often that the function is disabled", "Signaling interlocks remain in place" ], "echoes": [ { "date": "2022-11-14", "setting": "Seibu Railway's AI and 3D-image crossing anomaly detection in full operation at three crossings, alerting drivers by special signal lamps (Response.jp)", "model_version": null, "outcome": "recurred", "source": "https://response.jp/article/2022/11/11/363968.html" }, { "date": "2020-08-12", "setting": "Optage and Sanyo Electric Railway trial of AI crossing-camera analysis at three crossings (Mynavi News)", "model_version": null, "outcome": "unknown", "source": "https://news.mynavi.jp/article/20200812-1216717" } ] }, "paired_incident": null, "headline": "Odakyu to brake trains when cameras see someone inside a crossing", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "No development after the 24 June 2026 start was found; railf.jp says only that wider operation will be considered.", "quote": null, "url": null }, "value": null, "developer_country": "Japan", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://ja.wikipedia.org/wiki/%E5%90%8D%E9%89%84EI%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Odakyu_Electric_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the replication conditions rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. 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ITmedia NEWS.", "title": "踏切に取り残された人をAIで検知→列車を自動停止 小田急が実運用", "url": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html", "date": "2026-06-22", "written_by": "independent", "quote": "名鉄EIエンジニア、トヨタシステムズ、東邦電機工業が開発し、2023年1月からの実証を行ってきた。" }, { "kind": "news report", "citation": "railf.jp (25 Jun 2026). 小田急，AIを活用した踏切画像解析システムを導入.", "title": "小田急，AIを活用した踏切画像解析システムを導入", "url": "https://railf.jp/news/2026/06/25/041500.html", "date": "2026-06-25", "written_by": "independent", "quote": "踏切内の取り残しが解消された場合は、これらの措置は自動で解除される" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-06-22-b4f8: AI System Deployed to Prevent Railway Crossing Accidents in Japan.", "title": "AI System Deployed to Prevent Railway Crossing Accidents in Japan", "url": "https://oecd.ai/en/incidents/2026-06-22-b4f8", "date": "2026-06-22", "written_by": "independent", "quote": "The system analyzes real-time camera footage to detect pedestrians, cyclists, and others left inside crossings, automatically triggering train stops to prevent accidents." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0246-source-0001", "AIBD-0246-source-0002", "AIBD-0246-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "岡田有花 (22 Jun 2026). 踏切に取り残された人をAIで検知→列車を自動停止 小田急が実運用. ITmedia NEWS.", "title": "踏切に取り残された人をAIで検知→列車を自動停止 小田急が実運用", "url": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html", "date": "2026-06-22", "written_by": "independent", "quote": "名鉄EIエンジニア、トヨタシステムズ、東邦電機工業が開発し、2023年1月からの実証を行ってきた。" }, { "kind": "news report", "citation": "railf.jp (25 Jun 2026). 小田急，AIを活用した踏切画像解析システムを導入.", "title": "小田急，AIを活用した踏切画像解析システムを導入", "url": "https://railf.jp/news/2026/06/25/041500.html", "date": "2026-06-25", "written_by": "independent", "quote": "踏切内の取り残しが解消された場合は、これらの措置は自動で解除される" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-06-22-b4f8: AI System Deployed to Prevent Railway Crossing Accidents in Japan.", "title": "AI System Deployed to Prevent Railway Crossing Accidents in Japan", "url": "https://oecd.ai/en/incidents/2026-06-22-b4f8", "date": "2026-06-22", "written_by": "independent", "quote": "The system analyzes real-time camera footage to detect pedestrians, cyclists, and others left inside crossings, automatically triggering train stops to prevent accidents." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "ITmedia (22 Jun 2026): from 24 Jun 2026 AI analyzes existing crossing cameras, tracks pedestrians, wheelchair users, cyclists and motorcyclists and sends a brake signal to approaching trains; devel...", "account": "ITmedia's 岡田有花 reported on 22 Jun 2026 that Odakyu Electric Railway would introduce an “AI踏切画像解析システム” from 24 June 2026. It covers four crossings on its Odawara Line. They are Minami-Shinjuku Nos. 2, 4 and 5 between Minami-Shinjuku and Sangubashi stations, and Mukogaoka-Yuen No. 9 between Mukogaoka-Yuen and Ikuta stations. The AI analyzes footage from the existing safety-confirmation cameras in real time. It identifies and tracks pedestrians, wheelchair users, bicycles and motorcycles. When it detects someone left inside after the barriers come down, it sends a signal through the signaling equipment. That applies the brakes of approaching trains and lights the crossing's special signal lamps to alert crews. Both are released automatically once the crossing is clear. Images are used only for hazard detection, not to identify individuals, and analysis data are destroyed within a year. The system was developed by Meitetsu EI Engineer, Toyota Systems and Toho Electric Industries. It was trialed from January 2023, with work on detection accuracy at night and in rain. railf.jp carried the same announcement on 25 Jun 2026. No detection events or outcomes are reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI踏切画像解析システム (AI crossing image-analysis system) using existing crossing cameras linked to signaling equipment" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meitetsu EI Engineer, Toyota Systems and Toho Electric Industries" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Odakyu Electric Railway starts an AI crossing image-analysis system at four Odawara Line crossings that signals approaching trains to stop when people are left inside", "headline": "Odakyu to brake trains when cameras see someone inside a crossing" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "cb230cae9b818512df19a1fff82ec3f6fd48700e059c91150c001b519ac9f745" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ITmedia's 岡田有花 reported on 22 Jun 2026 that Odakyu Electric Railway would introduce an “AI踏切画像解析システム” from 24 June 2026. It covers four crossings on its Odawara Line. They are Minami-Shinjuku Nos. 2, 4 and 5 between Minami-Shinjuku and Sangubashi stations, and Mukogaoka-Yuen No. 9 between Mukogaoka-Yuen and Ikuta stations. The AI analyzes footage from the existing safety-confirmation cameras in real time. It identifies and tracks pedestrians, wheelchair users, bicycles and motorcycles. When it detects someone left inside after the barriers come down, it sends a signal through the signaling equipment. That applies the brakes of approaching trains and lights the crossing's special signal lamps to alert crews. Both are released automatically once the crossing is clear. Images are used only for hazard detection, not to identify individuals, and analysis data are destroyed within a year. The system was developed by Meitetsu EI Engineer, Toyota Systems and Toho Electric Industries. It was trialed from January 2023, with work on detection accuracy at night and in rain. railf.jp carried the same announcement on 25 Jun 2026. No detection events or outcomes are reported.", "replication": { "produced_by": [ "Existing crossing cameras with sufficient coverage of the enclosed area", "Integration with signaling equipment able to command train braking", "Three years of trials covering night and rain" ], "must_hold": [ "Detection accuracy holds in poor weather and lighting", "False detections do not stop trains so often that the function is disabled", "Signaling interlocks remain in place" ], "echoes": [ { "date": "2022-11-14", "setting": "Seibu Railway's AI and 3D-image crossing anomaly detection in full operation at three crossings, alerting drivers by special signal lamps (Response.jp)", "outcome": "recurred", "source": "https://response.jp/article/2022/11/11/363968.html" }, { "date": "2020-08-12", "setting": "Optage and Sanyo Electric Railway trial of AI crossing-camera analysis at three crossings (Mynavi News)", "outcome": "unknown", "source": "https://news.mynavi.jp/article/20200812-1216717" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0257", "case_url": "records/AIBD-0257.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The system is intended to monitor operational radio communications and flag departures from protocol for the control center." }, { "label": "AI contribution", "value": "Kyranakis described speech transcription and checks against communication protocols. The original interview does not establish that the system was operating routinely or that an alert prevented an incident." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0257", "title": "Greece's transport ministry presents an AI system to transcribe stationmaster-driver GSM-R dialogues in real time and check them against protocols", "summary": "In a SKAI interview, Greece’s transport minister described AI transcription of GSM-R conversations and checks against railway communication protocols; no measured safety result was given.", "date_observed": "2026-05-18", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In a SKAI interview reported on May 18, 2026, Deputy Minister of Transport Konstantinos Kyranakis described an AI system that transcribes GSM-R communications between stationmasters and train drivers. He said that it would identify departures from railway communication protocols so that the control center could intervene. He said that the system had been presented the previous Saturday. 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Removed the unsupported inference that no incidents had occurred.", "source": "https://www.skai.gr/news/politics/kyranakis-ston-skai-systima-ai-tha-elegxei-tis-synomilies-sta-trena", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report. 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He gave no measured safety result.", "editor_note": "" } }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Speech-to-text and AI protocol-compliance monitoring of GSM-R stationmaster-driver communications (supplier not named)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Not named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Greece's transport ministry presents an AI system to transcribe stationmaster-driver GSM-R dialogues in real time and check them against protocols", "headline": "Greece presents AI to transcribe and check GSM-R rail dialogues" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a90659c74483ef2fa285bdc66717ad436ec1d49aa03f6f713763d8e548e15543" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In a SKAI interview reported on May 18, 2026, Deputy Minister of Transport Konstantinos Kyranakis described an AI system that transcribes GSM-R communications between stationmasters and train drivers. He said that it would identify departures from railway communication protocols so that the control center could intervene. He said that the system had been presented the previous Saturday. The interview supplies no developer, operational start date, transcription accuracy, alert counts, or measured safety result.", "replication": { "produced_by": [ "Railway GSM-R communications available for transcription", "Speech-to-text and a method for checking communications against railway protocols", "A control center able to review departures from those protocols" ], "must_hold": [ "Transcription accuracy in Greek operational speech", "A control center staffed to act on flagged dialogues" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced the CNN.gr retelling and OECD label with SKAI’s original interview report. Removed the unsupported inference that no incidents had occurred.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0257-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The proposed system transcribes conversations over GSM-R railway radio and checks them against communication protocols. Control-center staff review any departures from those protocols.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 13, "record_mapped": 15, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0251", "case_url": "records/AIBD-0251.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Orca AI's platform is built to give bridge teams AI-assisted situational awareness and earlier detection of risky targets." }, { "label": "AI contribution", "value": "The platform's detections feed bridge decisions. The reported reduction compares the same vessels early and late in their first year. Crew familiarization, seasonality and route changes are not separated from the system's effect." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0251", "title": "Orca AI's watchkeeping may have cut high-severity close encounters 52% across 139 container ships in its first year", "summary": "Riviera and Hellenic Shipping News (16 Apr 2026): the P&I club and Orca AI analyzed 10.8 million nautical miles; high-severity close encounters fell from 2.7 to 1.3 per vessel-month between months ...", "date_observed": "2026-04-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Trade reports published on 15 and 16 Apr 2026 describe a study by the protection and indemnity insurer NorthStandard with Orca AI. The reports are MarineLink, Riviera Maritime Media's Martyn Wingrove, and Hellenic Shipping News reproducing Orca AI's release. The study covered a globally operating cohort of 139 container vessels fitted with Orca AI's navigation platform. Across more than 10.8 million nautical miles, it compared the first three months after installation with months 10 to 12. It reported a 52% reduction in high-severity close encounters, with 22% within the first six months. The reduction was 36% in the North and Baltic Seas and 18% in the seas around China and Japan. Encounters were defined by closest point of approach, time to closest point of approach, COLREGs interactions and traffic density. Orca AI's report page states that such encounters declined from 2.7 per vessel per month early after implementation to 1.3 by months 10 to 12. NorthStandard's head of loss prevention Colin Gillespie and Orca AI's chief executive Yarden Gross are quoted. The design is a before-and-after comparison on the same vessels with no control fleet, and the report is hosted by the vendor.", "category": "general benefit", "unexpected": false, "purpose": "Orca AI's platform is built to give bridge teams AI-assisted situational awareness and earlier detection of risky targets.", "system": "Orca AI navigation platform (digital watchkeeper modules and software)", "model_version": "Orca AI SeaPod digital watchkeeper", "developer": "Orca AI", "deployer": "Operators of 139 container vessels insured with NorthStandard", "location": null, "domain": "Maritime safety", "segment": "Commercial shipping / navigational decision support", "benefit_type": "Increased Product Safety", "taxonomy_note": "The metric is close encounters rather than collisions.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Aggregate change in a proxy metric; no collision, claim or cost outcome is documented." }, { "group": "societal", "depth": null, "reason": "No outcome beyond the proxy metric is documented." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "The platform's detections feed bridge decisions. The reported reduction compares the same vessels early and late in their first year. Crew familiarization, seasonality and route changes are not separated from the system's effect." }, "evidence": [ { "kind": "news report", "citation": "Wingrove M (16 Apr 2026). NorthStandard study demonstrates benefits of AI navigation support. Riviera Maritime Media.", "title": "NorthStandard study demonstrates benefits of AI navigation support", "url": "https://www.rivieramm.com/news-content-hub/news-content-hub/northstandard-study-demonstrates-benefits-of-ai-navigation-support-88473", "date": "2026-04-16", "written_by": "independent", "quote": "NorthStandard P&I club undertook a 12-month study with Orca AI, using its AI navigation services for a cohort of 139 container vessels." }, { "kind": "press release", "citation": "Hellenic Shipping News (16 Apr 2026). Joint Orca AI-NorthStandard study highlights strong impact of AI on navigational safety (source credited as Orca AI).", "title": "Joint Orca AI-NorthStandard study highlights strong impact of AI on navigational safety", "url": "https://www.hellenicshippingnews.com/joint-orca-ai-northstandard-study-highlights-strong-impact-of-ai-on-navigational-safety/", "date": "2026-04-16", "written_by": "makers", "quote": "What we’re seeing through our work with Orca AI, and now reinforced by this study, is that improved situational awareness and earlier risk detection can materially reduce close-quarters situations." }, { "kind": "news report", "citation": "MarineLink / Maritime Activity Reports (15 Apr 2026). Study From Orca AI, NorthStandard Highlights Role of AI on Navigational Safety.", "title": "Study From Orca AI, NorthStandard Highlights Role of AI on Navigational Safety", "url": "https://www.marinelink.com/news/study-orca-ai-northstandard-highlights-538079", "date": "2026-04-15", "written_by": "independent", "quote": "The study examined more than 10.8 million nautical miles of voyages, comparing the initial system adaptation period (first three months after installation) with the stabilized usage phase (months 10–12)." }, { "kind": "press release", "citation": "Orca AI (2026). Orca AI and NorthStandard Safety Report (report landing page).", "title": "Orca AI and NorthStandard Safety Report", "url": "https://www.orca-ai.io/reports/safetyreport/", "date": null, "written_by": "makers", "quote": "High-severity close encounters declined from 2.7 per vessel per month in the early post-implementation phase to 1.3 by months 10–12 - a 52% reduction." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-04-15-b7a4: AI Navigation System Reduces Maritime Near Misses by 52%.", "title": "AI Navigation System Reduces Maritime Near Misses by 52%", "url": "https://oecd.ai/en/incidents/2026-04-15-b7a4", "date": "2026-04-15", "written_by": "independent", "quote": "deploying Orca AI's navigation platform on 139 container vessels led to a 52% reduction in high-severity close encounters over 12 months" }, { "kind": "report", "citation": "Orca AI and NorthStandard (Mar 2026). Orca AI and NorthStandard Safety Report: Orca AI's impact on navigational safety.", "title": "Orca AI and NorthStandard Safety Report", "url": "https://d3cpegos94401u.cloudfront.net/publications/documents/orca-ai-&-northstandard-safety-report---final.pdf", "date": null, "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Fleet-wide installation of a sensor-and-software watchkeeping platform", "Twelve months of anonymized operational data per vessel", "An insurer willing to analyze member-fleet data" ], "must_hold": [ "Crews keep using the system after the adaptation phase", "The close-encounter definition stays constant across the comparison", "Routes and traffic density are comparable between periods" ], "echoes": [ { "date": "2020-04-15", "setting": "Shore-side AI collision-risk prediction at the Tokyo Wan VTS Center: warnings about two minutes earlier in simulated tasks (Maritime Executive)", "model_version": null, "outcome": "unknown", "source": "https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers" } ] }, "paired_incident": null, "headline": "Orca AI may have cut high-severity close encounters by 52%", "metric": "high-severity close encounters fell from 2.7 to 1.3 per vessel-month", "comparator": { "against": "the same vessels' first three months after installation (early post-implementation phase), no control fleet", "before": "2.7 per vessel per month in the early post-implementation phase", "after": "1.3 by months 10–12 - a 52% reduction", "basis": "measured", "quote": "High-severity close encounters declined from 2.7 per vessel per month in the early post-implementation phase to 1.3 by months 10–12 - a 52% reduction.", "url": "https://www.orca-ai.io/reports/safetyreport/" }, "later": { "status": "unknown", "date": null, "note": "No development after the report of 15 April 2026 is established. The vendor's October 2025 post predates it.", "quote": null, "url": null }, "value": null, "developer_country": "United Kingdom", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.orca-ai.io/reports/safetyreport/; https://d3cpegos94401u.cloudfront.net/publications/documents/orca-ai-&-northstandard-safety-report---final.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.orca-ai.io/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, a registry entry, the makers' report and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0251", "catalog_version": "1.0.0", "source_fingerprint": "3e2ae9be39315bbb7ce89e6cf4892315109aaf1fac6e2d44cfac2c982c9674f6", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0251-source-0001", "AIBD-0251-source-0002", "AIBD-0251-source-0003", "AIBD-0251-source-0004", "AIBD-0251-source-0005", "AIBD-0251-source-0006" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Orca AI", "deployer": "Operators of 139 container vessels insured with NorthStandard" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The platform's detections feed bridge decisions. The reported reduction compares the same vessels early and late in their first year. Crew familiarization, seasonality and route changes are not separated from the system's effect." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The platform's detections feed bridge decisions. The reported reduction compares the same vessels early and late in their first year. 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The reports are MarineLink, Riviera Maritime Media's Martyn Wingrove, and Hellenic Shipping News reproducing Orca AI's release. The study covered a globally operating cohort of 139 container vessels fitted with Orca AI's navigation platform. Across more than 10.8 million nautical miles, it compared the first three months after installation with months 10 to 12. It reported a 52% reduction in high-severity close encounters, with 22% within the first six months. The reduction was 36% in the North and Baltic Seas and 18% in the seas around China and Japan. Encounters were defined by closest point of approach, time to closest point of approach, COLREGs interactions and traffic density. Orca AI's report page states that such encounters declined from 2.7 per vessel per month early after implementation to 1.3 by months 10 to 12. NorthStandard's head of loss prevention Colin Gillespie and Orca AI's chief executive Yarden Gross are quoted. The design is a before-and-after comparison on the same vessels with no control fleet, and the report is hosted by the vendor.", "replication": { "produced_by": [ "Fleet-wide installation of a sensor-and-software watchkeeping platform", "Twelve months of anonymized operational data per vessel", "An insurer willing to analyze member-fleet data" ], "must_hold": [ "Crews keep using the system after the adaptation phase", "The close-encounter definition stays constant across the comparison", "Routes and traffic density are comparable between periods" ], "echoes": [ { "date": "2020-04-15", "setting": "Shore-side AI collision-risk prediction at the Tokyo Wan VTS Center: warnings about two minutes earlier in simulated tasks (Maritime Executive)", "outcome": "unknown", "source": "https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0374", "case_url": "records/AIBD-0374.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Ukrainian unmanned ground vehicles and drones are built to carry out front-line missions in place of soldiers. Those missions include assaults, logistics and evacuation. Taking a position without exposing infantry is that purpose." }, { "label": "AI contribution", "value": "No source describes an AI function in this operation. Politico says the ground robotic systems are “currently piloted by humans from a safe distance”. Jacob Parakilas of RAND Europe writes in The Conversation that they are “mostly still remotely operated”. He says autonomy is limited to tasks such as highlighting identified enemy positions. The Debrief says it is unclear how much remote human control was involved at each stage. Politico separately notes a January 2026 case in which a DevDroid robot “equipped with artificial intelligence elements” took three prisoners, a different event." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Ukraine" ] }, "record": { "id": "AIBD-0374", "title": "Ukrainian ground robots and drones may have taken a Russian position with no infantry and no Ukrainian losses; the location, date, unit and prisoner count are undisclosed", "summary": "Zelenskyy (13 April 2026): an enemy position was taken “exclusively by unmanned platforms” with no infantry and no losses, per Politico and Business Insider; no location, date or verification.", "date_observed": "2026-04-13", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "President Volodymyr Zelenskyy gave an Arms Makers' Day address on Monday 13 April 2026. “For the first time in the history of this war, an enemy position was taken exclusively by unmanned platforms — ground systems and drones,” he said. He added that “the occupiers surrendered, and the operation was carried out without infantry and without losses on our side” (Politico, Business Insider, Censor.NET). He said ground robotic systems performed more than 22,000 missions on Ukraine's front line in the first three months of 2026. He named Ratel, TerMIT, Ardal, Rys, Zmiy, Protector and Volia. Newsweek says he did not say where the advance took place. No outlet gives a date, unit or prisoner count. Ars Technica says the claim is not independently verified and may refer to a 3rd Separate Assault Brigade action in Kharkiv Oblast last year. Politico calls the robots “currently piloted by humans from a safe distance”.", "category": "benefit created", "unexpected": false, "purpose": "Ukrainian unmanned ground vehicles and drones are built to carry out front-line missions in place of soldiers. Those missions include assaults, logistics and evacuation. Taking a position without exposing infantry is that purpose.", "system": "Ukrainian unmanned ground vehicles (Ratel, TerMIT, Ardal, Rys, Zmiy, Protector and Volia, as named by Zelenskyy) operating with aerial drones", "model_version": null, "developer": "Ukrainian makers of the named systems (Ratel, TerMIT, Ardal, Rys, Zmiy, Protector, Volia)", "deployer": "Ukrainian Defense Forces", "location": "Ukraine", "domain": "Defense: ground robotic systems in assault operations", "segment": "Unmanned ground vehicles and drones in combat", "benefit_type": "Reduced Risk", "taxonomy_note": "A combat action with no friendly losses is read as reduced potential for harm; no defense type exists.", "registry_ids": { "oecd_aim": "2026-04-13-ec31" }, "ratings": [ { "group": "organizational", "depth": null, "reason": "The capture, the surrender and the absence of losses rest solely on the President's statement. Newsweek says no location was given. Ars Technica and The Debrief say the claim is not independently verified, so the outcome is not established." } ], "ai_role": { "status": "unassessed", "statement": "No source describes an AI function in this operation. Politico says the ground robotic systems are “currently piloted by humans from a safe distance”. Jacob Parakilas of RAND Europe writes in The Conversation that they are “mostly still remotely operated”. He says autonomy is limited to tasks such as highlighting identified enemy positions. The Debrief says it is unclear how much remote human control was involved at each stage. Politico separately notes a January 2026 case in which a DevDroid robot “equipped with artificial intelligence elements” took three prisoners, a different event." }, "evidence": [ { "kind": "news report", "citation": "Melkozerova V (2026). Robots captured Russian army positions for first time in history, Zelenskyy says. POLITICO Europe, 14 April 2026.", "title": "Robots captured Russian army positions for first time in history, Zelenskyy says", "url": "https://www.politico.eu/article/volodymyr-zelenskyy-robotic-systems-russia-army-positions-ukraine/", "date": "2026-04-14", "written_by": "independent", "quote": "The Ukrainian army has been scaling up the use of ground robotic systems, currently piloted by humans from a safe distance, to perform different assault operations" }, { "kind": "news report", "citation": "Business Insider (2026). Ukraine said it captured a Russian position using only ground robots and drones, no infantry, for the first time. Business Insider, 14 April 2026.", "title": "Ukraine said it captured a Russian position using only ground robots and drones, no infantry, for the first time", "url": "https://www.businessinsider.com/ukraine-russia-position-taken-without-using-troops-just-robots-drones-2026-4", "date": "2026-04-14", "written_by": "independent", "quote": "Ukraine said it forced Russian soldiers to surrender and captured their position using only aerial drones and ground robots, no infantry, in a first for the war." }, { "kind": "news report", "citation": "Censor.NET (2026). Defense Forces capture enemy position for first time without infantry, using UGVs and drones, Zelenskyy says. Censor.NET, 13 April 2026.", "title": "Defense Forces capture enemy position for first time without infantry, using UGVs and drones, Zelenskyy says", "url": "https://censor.net/en/news/3610314/ukraine-s-defense-forces-seize-position-using-only-robots-and-drones", "date": "2026-04-13", "written_by": "independent", "quote": "The occupiers surrendered, and this operation was carried out without the involvement of infantry and without any losses on our side." }, { "kind": "news report", "citation": "Ars Technica (2026). Ukraine's military robot surge aims to offset drone risks to humans. Ars Technica, 14 April 2026.", "title": "Ukraine's military robot surge aims to offset drone risks to humans", "url": "https://arstechnica.com/ai/2026/04/ukraines-military-robot-surge-aims-to-offset-drone-risks-to-humans/", "date": "2026-04-14", "written_by": "independent", "quote": "The claim by Zelenskyy has not been independently verified but was accompanied by a promotional video" }, { "kind": "news report", "citation": "Robinson M, Cook E (2026). Putin's Troops Surrender Position to Robots for First Time: Zelensky. Newsweek, 14 April 2026.", "title": "Putin's Troops Surrender Position to Robots for First Time: Zelensky", "url": "https://www.newsweek.com/russia-ukraine-war-latest-putin-troops-surrender-military-robot-first-time-11825010", "date": "2026-04-14", "written_by": "independent", "quote": "The Ukrainian leader did not say where the advance took place." }, { "kind": "news report", "citation": "McMillan T (2026). Ukraine Says It Captured a Russian Position Using Only Unmanned Systems—A Glimpse of Future Warfare. The Debrief, 14 April 2026.", "title": "Ukraine Says It Captured a Russian Position Using Only Unmanned Systems—A Glimpse of Future Warfare", "url": "https://thedebrief.org/ukraine-says-it-captured-a-russian-position-using-only-unmanned-systems-a-glimpse-of-future-warfare/", "date": "2026-04-14", "written_by": "independent", "quote": "The claim has not yet been fully independently verified." }, { "kind": "other", "citation": "Parakilas J (2026). Robots just captured a Russian position in Ukraine – but don't worry about real-life Terminators just yet. The Conversation (analysis by a RAND Europe research leader), 17 April 2026.", "title": "Robots just captured a Russian position in Ukraine – but don't worry about real-life Terminators just yet", "url": "https://theconversation.com/robots-just-captured-a-russian-position-in-ukraine-but-dont-worry-about-real-life-terminators-just-yet-280959", "date": "2026-04-17", "written_by": "independent", "quote": "they are mostly still remotely operated, which means the operator must maintain a constant control link with the vehicle" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). Ukrainian AI Robots Capture Enemy Position Without Infantry. OECD.AI, 13 April 2026.", "title": "Ukrainian AI Robots Capture Enemy Position Without Infantry", "url": "https://oecd.ai/en/incidents/2026-04-13-ec31", "date": "2026-04-13", "written_by": "independent", "quote": "Ukrainian defense forces used unmanned ground robotic complexes and drones, powered by AI, to autonomously capture a Russian enemy position." } ], "replication": { "produced_by": [ "Ground robots and aerial drones available in numbers after a rapid rise in missions in early 2026", "Operators controlling the vehicles from a distance over radio, fiber or satellite links", "A Russian position whose defenders chose to surrender rather than fight the machines" ], "must_hold": [ "Control links survive electronic warfare and terrain", "Robots reach the position. Ars Technica cites a 38th Marine Brigade officer saying evacuation robots failed to reach positions in four of five cases", "Defenders are willing to surrender to unmanned systems" ], "echoes": [] }, "paired_incident": null, "headline": "Ukrainian robots and drones may have taken a Russian position alone", "metric": "more than 22,000 missions on Ukraine's front line", "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "Ukraine", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-04-13-ec31", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Armed_Forces_of_Ukraine", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0374", "catalog_version": "1.0.0", "source_fingerprint": "f398651d3ee73cd9026c0ecbecc0d5f8d8b0481aced3c989bff304ddc13305e7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0374-source-0001", "AIBD-0374-source-0002", "AIBD-0374-source-0003", "AIBD-0374-source-0004", "AIBD-0374-source-0005", "AIBD-0374-source-0006", "AIBD-0374-source-0007", "AIBD-0374-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Ukrainian makers of the named systems (Ratel, TerMIT, Ardal, Rys, Zmiy, Protector, Volia)", "deployer": "Ukrainian Defense Forces" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "unassessed", "statement": "No source describes an AI function in this operation. 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He added that “the occupiers surrendered, and the operation was carried out without infantry and without losses on our side” (Politico, Business Insider, Censor.NET). He said ground robotic systems performed more than 22,000 missions on Ukraine's front line in the first three months of 2026. He named Ratel, TerMIT, Ardal, Rys, Zmiy, Protector and Volia. Newsweek says he did not say where the advance took place. No outlet gives a date, unit or prisoner count. Ars Technica says the claim is not independently verified and may refer to a 3rd Separate Assault Brigade action in Kharkiv Oblast last year. Politico calls the robots “currently piloted by humans from a safe distance”." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Ukrainian unmanned ground vehicles (Ratel, TerMIT, Ardal, Rys, Zmiy, Protector and Volia, as named by Zelenskyy) operating with aerial drones" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Ukrainian makers of the named systems (Ratel, TerMIT, Ardal, Rys, Zmiy, Protector, Volia)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Ukrainian ground robots and drones may have taken a Russian position with no infantry and no Ukrainian losses; the location, date, unit and prisoner count are undisclosed", "headline": "Ukrainian robots and drones may have taken a Russian position alone" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "f398651d3ee73cd9026c0ecbecc0d5f8d8b0481aced3c989bff304ddc13305e7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "President Volodymyr Zelenskyy gave an Arms Makers' Day address on Monday 13 April 2026. “For the first time in the history of this war, an enemy position was taken exclusively by unmanned platforms — ground systems and drones,” he said. He added that “the occupiers surrendered, and the operation was carried out without infantry and without losses on our side” (Politico, Business Insider, Censor.NET). He said ground robotic systems performed more than 22,000 missions on Ukraine's front line in the first three months of 2026. He named Ratel, TerMIT, Ardal, Rys, Zmiy, Protector and Volia. Newsweek says he did not say where the advance took place. No outlet gives a date, unit or prisoner count. Ars Technica says the claim is not independently verified and may refer to a 3rd Separate Assault Brigade action in Kharkiv Oblast last year. Politico calls the robots “currently piloted by humans from a safe distance”.", "replication": { "produced_by": [ "Ground robots and aerial drones available in numbers after a rapid rise in missions in early 2026", "Operators controlling the vehicles from a distance over radio, fiber or satellite links", "A Russian position whose defenders chose to surrender rather than fight the machines" ], "must_hold": [ "Control links survive electronic warfare and terrain", "Robots reach the position. Ars Technica cites a 38th Marine Brigade officer saying evacuation robots failed to reach positions in four of five cases", "Defenders are willing to surrender to unmanned systems" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 26, "reviewed": 0, "record_mapped": 26, "not_assessed": 65, "complete": false, "by_state": { "value_present": 26, "not_reported": 0, "unresolved": 0, "not_assessed": 65, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0327", "case_url": "records/AIBD-0327.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Iron Beam is a high-energy laser built to intercept rockets, mortars and drones at short range. It is a low-cost complement to Iron Dome, David's Sling and Arrow. Interception of Hezbollah projectiles is its stated purpose." }, { "label": "AI contribution", "value": "No cited source describes Iron Beam as an AI or machine-learning system. Those sources are Ynet, JPost, Army Recognition, TimesNow, The Conversation and Moneycontrol. They also include Times of Israel 28 December 2025, 16 Mar, 27 Mar and 30 June 2026, and Wikipedia. They describe a high-power laser with a beam director and electro-optical targeting. The Monitor's “AI-enabled” label is its own. Rafael's CEO described machine learning as a future capability (Breaking Defense, 22 October 2025). The IDF has not confirmed the engagement itself, so neither an AI role nor a contribution can be assessed." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Israel" ] }, "record": { "id": "AIBD-0327", "title": "Israel's Iron Beam laser may have made its first combat interception, of a Hezbollah drone, in footage of 2 March 2026", "summary": "Ynet and JPost (2 March 2026) call the video unconfirmed; Times of Israel (27 Mar, 30 June 2026) quotes officials that Iron Beam “has not yet been declared operational”. No cited source calls it AI.", "date_observed": "2026-03-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry says that Israel “deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages”. It separately bundles a Hankyoreh item (dated 10 October 2023) on Hamas rockets penetrating Iron Dome. The Iron Beam claim rests on social-media footage from Monday 2 March 2026, not 28 February. Ynet (Yair Kraus) reports that the footage “appeared to show the first operational use” of the system. It was delivered to the Israeli Air Force in late December 2025. Hezbollah fired rockets and drones from Lebanon after the killing of Iran's Supreme Leader, with one drone intercepted near Shlomi. The Jerusalem Post (Seth Frantzman, 2 March 2026) says that the circulated video “did not show lasers intercepting rockets”. TimesNow (2 March 2026) says that whether this was Iron Beam's first combat use “awaits official confirmation”. Army Recognition (3 March 2026) says that officials “have not formally confirmed the specific engagements” and that no authoritative combat log names the system. The Conversation (4 March 2026) calls the reports unconfirmed. Moneycontrol (3 March 2026) sources its “first-ever use” to an “Israel War Room” social-media video. The Times of Israel reports the Defense Ministry's handover of the first operational system on 28 December 2025, after tests against rockets, mortars and drones. Times of Israel/AFP (16 March 2026) reports that the IDF “has so far refused to comment on its use during the ongoing war”. Times of Israel (Stav Levaton, 27 March 2026) reports that “military officials said it has not yet been declared operational within the IDF”. Times of Israel (Emanuel Fabian, 30 June 2026) reports that the one deployed system “has seen limited use and has not yet been declared operational within the IDF”. A senior officer called its coverage partial. The only confirmed laser combat use is aggregate and earlier. A lower-powered, shorter-range variant shot down some 35 drones from Lebanon in 2024 (Times of Israel/AFP). The Ministry of Defense announced it in May 2025 (JPost). The MoD said that no Iron Beam components were involved (Wikipedia). No cited source describes Iron Beam as an AI system. The only AI mention found is Rafael's CEO framing machine learning as a future capability (Breaking Defense, 22 October 2025). That piece is not among the sources. Metro, Economic Times, MoneyControl's second item, Hankyoreh and others among the Monitor's 20 listed sources are syndications or explainers.", "category": "harm prevented", "unexpected": false, "purpose": "Iron Beam is a high-energy laser built to intercept rockets, mortars and drones at short range. It is a low-cost complement to Iron Dome, David's Sling and Arrow. Interception of Hezbollah projectiles is its stated purpose.", "system": "Iron Beam ('Or Eitan') 100 kW-class high-energy laser air-defense system by Rafael Advanced Defense Systems, with beam director and electro-optical targeting; the Monitor calls it 'AI-enabled', which no cited source supports", "model_version": null, "developer": "Rafael Advanced Defense Systems with the Israel Ministry of Defense (Lockheed Martin collaboration for the US market, per Wikipedia)", "deployer": "Israeli Air Force air-defense array; first operational system delivered 28 December 2025", "location": "Northern Israel near the Lebanese border (Shlomi area reported), Israel", "domain": "Defense: air defense", "segment": "Directed-energy interception of rockets, mortars and drones", "benefit_type": "Reduced Violence", "taxonomy_note": "An intercepted drone or rockets are read as a physically harmful incident averted; no defense type exists.", "registry_ids": { "oecd_aim": "2026-02-28-d62f" }, "ratings": [ { "group": "societal", "depth": null, "reason": "Ynet reports that one drone was intercepted and no injuries occurred in the 2 March 2026 launches. No official source attributes any interception to Iron Beam." }, { "group": "organizational", "depth": null, "reason": "The IDF has not confirmed use. Officials told Times of Israel that the one deployed system had limited use and partial coverage. It had no operational declaration as of June 2026." } ], "ai_role": { "status": "unassessed", "statement": "No cited source describes Iron Beam as an AI or machine-learning system. Those sources are Ynet, JPost, Army Recognition, TimesNow, The Conversation and Moneycontrol. They also include Times of Israel 28 December 2025, 16 Mar, 27 Mar and 30 June 2026, and Wikipedia. They describe a high-power laser with a beam director and electro-optical targeting. The Monitor's “AI-enabled” label is its own. Rafael's CEO described machine learning as a future capability (Breaking Defense, 22 October 2025). The IDF has not confirmed the engagement itself, so neither an AI role nor a contribution can be assessed." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2026-02-28-d62f, 'AI-Enabled Iron Beam and Iron Dome Systems Deployed in Israeli Defense' (AI-generated summary).", "title": "AI-Enabled Iron Beam and Iron Dome Systems Deployed in Israeli Defense", "url": "https://oecd.ai/en/incidents/2026-02-28-d62f", "date": "2026-02-28", "written_by": "independent", "quote": "Israel deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages" }, { "kind": "news report", "citation": "Ynetnews (Yair Kraus), 2 March 2026, 'Video appears to show first combat use of Israel's Iron Beam laser defense system'.", "title": "Video appears to show first combat use of Israel’s Iron Beam laser defense system", "url": "https://www.ynetnews.com/article/h13axdmfwx", "date": "2026-03-02", "written_by": "independent", "quote": "Footage circulating on social media early Monday appeared to show the first operational use of the new Iron Beam laser air defense system" }, { "kind": "news report", "citation": "The Jerusalem Post (Seth J. Frantzman), 2 March 2026, 'Israel's Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis'.", "title": "Israel’s Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis", "url": "https://www.jpost.com/israel-news/defense-news/article-888515", "date": "2026-03-02", "written_by": "independent", "quote": "The video did not show lasers intercepting rockets, but the mass interest in the new technology is clear." }, { "kind": "news report", "citation": "Army Recognition, 3 March 2026, 'Israel's Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon'.", "title": "Israel’s Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon", "url": "https://www.armyrecognition.com/news/army-news/2026/israels-iron-beam-laser-linked-to-first-combat-intercepts-of-hezbollah-rockets-in-lebanon", "date": "2026-03-03", "written_by": "independent", "quote": "Israeli authorities have not released an authoritative combat log explicitly naming the system." }, { "kind": "news report", "citation": "Times Now, 2 March 2026, 'Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far'.", "title": "Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far", "url": "https://www.timesnownews.com/world/middle-east/did-israel-debut-their-iron-beam-defence-system-to-intercept-missiles-fired-by-hezbollah-what-we-know-so-far-article-153731169", "date": "2026-03-02", "written_by": "independent", "quote": "Whether this constitutes Iron Beam’s definitive first combat use awaits official confirmation." }, { "kind": "news report", "citation": "The Conversation, 4 March 2026, 'Israel's 'Iron Beam': why laser weapons are no longer science fiction'.", "title": "Israel’s ‘Iron Beam’: why laser weapons are no longer science fiction", "url": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390", "date": "2026-03-04", "written_by": "independent", "quote": "While the reports are unconfirmed, video circulating on social media appears to show rockets being destroyed within moments of launching without visible intervention" }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton and Emanuel Fabian), 28 December 2025, 'Defense Ministry hands IDF first combat-ready Iron Beam laser interception system'.", "title": "Defense Ministry hands IDF first combat-ready Iron Beam laser interception system", "url": "https://www.timesofisrael.com/defense-ministry-hands-idf-first-combat-ready-iron-beam-laser-interception-system/", "date": "2025-12-28", "written_by": "independent", "quote": "has successfully intercepted rockets, mortar shells and drones during extensive testing" }, { "kind": "news report", "citation": "The Times of Israel / AFP, 16 March 2026, 'Surge in drone attacks sharpens focus on laser defense'.", "title": "Surge in drone attacks sharpens focus on laser defense", "url": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/", "date": "2026-03-16", "written_by": "independent", "quote": "The IDF received the system in December, but has so far refused to comment on its use during the ongoing war with Iran and its Lebanese terror proxy Hezbollah." }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton), 27 March 2026, 'Cheap and elusive drones put incessant pressure on Israel's evolving air defenses'.", "title": "Cheap and elusive drones put incessant pressure on Israel’s evolving air defenses", "url": "https://www.timesofisrael.com/cheap-and-elusive-drones-put-incessant-pressure-on-israels-evolving-air-defenses/", "date": "2026-03-27", "written_by": "independent", "quote": "While the Defense Ministry said the system became operational in late 2025, military officials said it has not yet been declared operational within the IDF." }, { "kind": "news report", "citation": "The Times of Israel (Emanuel Fabian), 30 June 2026, 'Iron Dome air defenses upgraded, 'incorporating lessons learned' from Iran war'.", "title": "Iron Dome air defenses upgraded, ‘incorporating lessons learned’ from Iran war", "url": "https://www.timesofisrael.com/iron-dome-air-defenses-upgraded-incorporating-lessons-learned-from-iran-war/", "date": "2026-06-30", "written_by": "independent", "quote": "But while the military has deployed one Iron Beam system, it has seen limited use and has not yet been declared operational within the IDF." }, { "kind": "other", "citation": "Wikipedia, 'Iron Beam', accessed 20 October 2026 (on Rafael's 28 May 2025 announcement of 2024 laser interceptions).", "title": "Iron Beam", "url": "https://en.wikipedia.org/wiki/Iron_Beam", "date": null, "written_by": "independent", "quote": "MOD said that no Iron Beam components were involved" }, { "kind": "news report", "citation": "Hankyoreh (English edition), 10 October 2023, '$600 rockets shatter Israel's Iron Dome - will Seoul's skies be safe?' (the Monitor's bundled Iron Dome item; it predates and does not concern Iron Beam).", "title": "$600 rockets shatter Israel’s Iron Dome – will Seoul’s skies be safe?", "url": "https://english.hani.co.kr/arti/english_edition/e_international/1111552.html", "date": "2023-10-10", "written_by": "independent", "quote": "Many of the rockets fired by Hamas penetrated Israel’s low-altitude air defense system, known as the Iron Dome, and struck Israel." } ], "replication": { "produced_by": [ "A ground-based high-energy laser that holds its beam on a short-range target within line of sight until it is destroyed. JPost describes the terrain, weather and dwell-time limits", "Integration into the Israeli Air Force's layered air-defense array as a complement to Iron Dome" ], "must_hold": [ "IDF confirmation that Iron Beam, not Iron Dome, other interceptors or counter-UAS effects, made the 2 March 2026 interception", "Line of sight and clear weather. A senior officer said in May 2026 that the system's coverage is partial and that at least 14 batteries are sought" ], "echoes": [ { "date": null, "setting": "2024 conflict with Hezbollah, northern Israel: a lower-powered, shorter-range laser variant shot down some 35 drones (Times of Israel/AFP), announced by the Ministry of Defense in May 2025 (JPost); the MoD said no Iron Beam components were involved (Wikipedia)", "model_version": "lower-powered variant (Lite Beam or Iron Beam-M, per Wikipedia's account of Israeli media)", "outcome": "recurred", "source": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/" } ] }, "paired_incident": null, "headline": "Iron Beam may have made its first combat interception", "metric": "one drone intercepted near Shlomi", "comparator": { "against": "Iron Dome interceptor missiles", "before": "A single Iron Dome interceptor missile costs about US$50,000", "after": "each shot cost around $US3.50, and more recent estimates suggest the cost may now have fallen as low as US$2.50 per shot", "basis": "assumed", "quote": "A single Iron Dome interceptor missile costs about US$50,000 – which means the costs add up quickly when defending against large or frequent attacks. Firing the Iron Beam laser costs a lot less. In 2022, Israel’s then prime minister Naftali Bennett said each shot cost around $US3.50, and more recent estimates suggest the cost may now have fallen as low as US$2.50 per shot.", "url": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390" }, "later": null, "value": null, "developer_country": "Israel", "deployer_country": "Israel", "unrated_because": "the IDF has not confirmed use, and no source calls it AI", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the IDF has not confirmed use, and no source calls it AI.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Israeli_Air_Defense_Command", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: ten independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0327", "catalog_version": "1.0.0", "source_fingerprint": "cd4d27d77be41530edf2699ac8a2ccef212aaef7026277623e8af020144da5b5", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0327-source-0001", "AIBD-0327-source-0002", "AIBD-0327-source-0003", "AIBD-0327-source-0004", "AIBD-0327-source-0005", "AIBD-0327-source-0006", "AIBD-0327-source-0007", "AIBD-0327-source-0008", "AIBD-0327-source-0009", "AIBD-0327-source-0010", "AIBD-0327-source-0011", "AIBD-0327-source-0012" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Rafael Advanced Defense Systems with the Israel Ministry of Defense (Lockheed Martin collaboration for the US market, per Wikipedia)", "deployer": "Israeli Air Force air-defense array; first operational system delivered 28 December 2025" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "No cited source describes Iron Beam as an AI or machine-learning system. Those sources are Ynet, JPost, Army Recognition, TimesNow, The Conversation and Moneycontrol. They also include Times of Israel 28 December 2025, 16 Mar, 27 Mar and 30 June 2026, and Wikipedia. They describe a high-power laser with a beam director and electro-optical targeting. The Monitor's “AI-enabled” label is its own. Rafael's CEO described machine learning as a future capability (Breaking Defense, 22 October 2025). The IDF has not confirmed the engagement itself, so neither an AI role nor a contribution can be assessed." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: ten independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "Ynet reports that one drone was intercepted and no injuries occurred in the 2 March 2026 launches. No official source attributes any interception to Iron Beam." }, { "group": "organizational", "reason": "The IDF has not confirmed use. Officials told Times of Israel that the one deployed system had limited use and partial coverage. It had no operational declaration as of June 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "Ynet reports that one drone was intercepted and no injuries occurred in the 2 March 2026 launches. No official source attributes any interception to Iron Beam." }, { "group": "organizational", "reason": "The IDF has not confirmed use. Officials told Times of Israel that the one deployed system had limited use and partial coverage. 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{ "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0327" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "An intercepted drone or rockets are read as a physically harmful incident averted; no defense type exists." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry says that Israel “deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages”. It separately bundles a Hankyoreh item (dated 10 October 2023) on Hamas rockets penetrating Iron Dome. The Iron Beam claim rests on social-media footage from Monday 2 March 2026, not 28 February. Ynet (Yair Kraus) reports that the footage “appeared to show the first operational use” of the system. It was delivered to the Israeli Air Force in late December 2025. Hezbollah fired rockets and drones from Lebanon after the killing of Iran's Supreme Leader, with one drone intercepted near Shlomi. The Jerusalem Post (Seth Frantzman, 2 March 2026) says that the circulated video “did not show lasers intercepting rockets”. TimesNow (2 March 2026) says that whether this was Iron Beam's first combat use “awaits official confirmation”. Army Recognition (3 March 2026) says that officials “have not formally confirmed the specific engagements” and that no authoritative combat log names the system. The Conversation (4 March 2026) calls the reports unconfirmed. Moneycontrol (3 March 2026) sources its “first-ever use” to an “Israel War Room” social-media video. The Times of Israel reports the Defense Ministry's handover of the first operational system on 28 December 2025, after tests against rockets, mortars and drones. Times of Israel/AFP (16 March 2026) reports that the IDF “has so far refused to comment on its use during the ongoing war”. Times of Israel (Stav Levaton, 27 March 2026) reports that “military officials said it has not yet been declared operational within the IDF”. Times of Israel (Emanuel Fabian, 30 June 2026) reports that the one deployed system “has seen limited use and has not yet been declared operational within the IDF”. A senior officer called its coverage partial. The only confirmed laser combat use is aggregate and earlier. A lower-powered, shorter-range variant shot down some 35 drones from Lebanon in 2024 (Times of Israel/AFP). The Ministry of Defense announced it in May 2025 (JPost). The MoD said that no Iron Beam components were involved (Wikipedia). No cited source describes Iron Beam as an AI system. The only AI mention found is Rafael's CEO framing machine learning as a future capability (Breaking Defense, 22 October 2025). That piece is not among the sources. Metro, Economic Times, MoneyControl's second item, Hankyoreh and others among the Monitor's 20 listed sources are syndications or explainers.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "Ynet reports that one drone was intercepted and no injuries occurred in the 2 March 2026 launches. No official source attributes any interception to Iron Beam." }, { "group": "organizational", "reason": "The IDF has not confirmed use. Officials told Times of Israel that the one deployed system had limited use and partial coverage. It had no operational declaration as of June 2026." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Iron Beam is a high-energy laser built to intercept rockets, mortars and drones at short range. It is a low-cost complement to Iron Dome, David's Sling and Arrow. Interception of Hezbollah projectiles is its stated purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Northern Israel near the Lebanese border (Shlomi area reported), Israel", "developer_country": "Israel", "deployer_country": "Israel" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Iron Beam is a high-energy laser built to intercept rockets, mortars and drones at short range. It is a low-cost complement to Iron Dome, David's Sling and Arrow. Interception of Hezbollah projectiles is its stated purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-03-02", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the IDF has not confirmed use, and no source calls it AI.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Israeli_Air_Defense_Command", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A ground-based high-energy laser that holds its beam on a short-range target within line of sight until it is destroyed. JPost describes the terrain, weather and dwell-time limits", "Integration into the Israeli Air Force's layered air-defense array as a complement to Iron Dome" ], "must_hold": [ "IDF confirmation that Iron Beam, not Iron Dome, other interceptors or counter-UAS effects, made the 2 March 2026 interception", "Line of sight and clear weather. A senior officer said in May 2026 that the system's coverage is partial and that at least 14 batteries are sought" ], "echoes": [ { "setting": "2024 conflict with Hezbollah, northern Israel: a lower-powered, shorter-range laser variant shot down some 35 drones (Times of Israel/AFP), announced by the Ministry of Defense in May 2025 (JPost); the MoD said no Iron Beam components were involved (Wikipedia)", "model_version": "lower-powered variant (Lite Beam or Iron Beam-M, per Wikipedia's account of Israeli media)", "outcome": "recurred", "source": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A ground-based high-energy laser that holds its beam on a short-range target within line of sight until it is destroyed. 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Frantzman), 2 March 2026, 'Israel's Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis'.", "title": "Israel’s Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis", "url": "https://www.jpost.com/israel-news/defense-news/article-888515", "date": "2026-03-02", "written_by": "independent", "quote": "The video did not show lasers intercepting rockets, but the mass interest in the new technology is clear." }, { "kind": "news report", "citation": "Army Recognition, 3 March 2026, 'Israel's Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon'.", "title": "Israel’s Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon", "url": "https://www.armyrecognition.com/news/army-news/2026/israels-iron-beam-laser-linked-to-first-combat-intercepts-of-hezbollah-rockets-in-lebanon", "date": "2026-03-03", "written_by": "independent", "quote": "Israeli authorities have not released an authoritative combat log explicitly naming the system." }, { "kind": "news report", "citation": "Times Now, 2 March 2026, 'Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far'.", "title": "Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far", "url": "https://www.timesnownews.com/world/middle-east/did-israel-debut-their-iron-beam-defence-system-to-intercept-missiles-fired-by-hezbollah-what-we-know-so-far-article-153731169", "date": "2026-03-02", "written_by": "independent", "quote": "Whether this constitutes Iron Beam’s definitive first combat use awaits official confirmation." }, { "kind": "news report", "citation": "The Conversation, 4 March 2026, 'Israel's 'Iron Beam': why laser weapons are no longer science fiction'.", "title": "Israel’s ‘Iron Beam’: why laser weapons are no longer science fiction", "url": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390", "date": "2026-03-04", "written_by": "independent", "quote": "While the reports are unconfirmed, video circulating on social media appears to show rockets being destroyed within moments of launching without visible intervention" }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton and Emanuel Fabian), 28 December 2025, 'Defense Ministry hands IDF first combat-ready Iron Beam laser interception system'.", "title": "Defense Ministry hands IDF first combat-ready Iron Beam laser interception system", "url": "https://www.timesofisrael.com/defense-ministry-hands-idf-first-combat-ready-iron-beam-laser-interception-system/", "date": "2025-12-28", "written_by": "independent", "quote": "has successfully intercepted rockets, mortar shells and drones during extensive testing" }, { "kind": "news report", "citation": "The Times of Israel / AFP, 16 March 2026, 'Surge in drone attacks sharpens focus on laser defense'.", "title": "Surge in drone attacks sharpens focus on laser defense", "url": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/", "date": "2026-03-16", "written_by": "independent", "quote": "The IDF received the system in December, but has so far refused to comment on its use during the ongoing war with Iran and its Lebanese terror proxy Hezbollah." }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton), 27 March 2026, 'Cheap and elusive drones put incessant pressure on Israel's evolving air defenses'.", "title": "Cheap and elusive drones put incessant pressure on Israel’s evolving air defenses", "url": "https://www.timesofisrael.com/cheap-and-elusive-drones-put-incessant-pressure-on-israels-evolving-air-defenses/", "date": "2026-03-27", "written_by": "independent", "quote": "While the Defense Ministry said the system became operational in late 2025, military officials said it has not yet been declared operational within the IDF." }, { "kind": "news report", "citation": "The Times of Israel (Emanuel Fabian), 30 June 2026, 'Iron Dome air defenses upgraded, 'incorporating lessons learned' from Iran war'.", "title": "Iron Dome air defenses upgraded, ‘incorporating lessons learned’ from Iran war", "url": "https://www.timesofisrael.com/iron-dome-air-defenses-upgraded-incorporating-lessons-learned-from-iran-war/", "date": "2026-06-30", "written_by": "independent", "quote": "But while the military has deployed one Iron Beam system, it has seen limited use and has not yet been declared operational within the IDF." }, { "kind": "other", "citation": "Wikipedia, 'Iron Beam', accessed 20 October 2026 (on Rafael's 28 May 2025 announcement of 2024 laser interceptions).", "title": "Iron Beam", "url": "https://en.wikipedia.org/wiki/Iron_Beam", "written_by": "independent", "quote": "MOD said that no Iron Beam components were involved" }, { "kind": "news report", "citation": "Hankyoreh (English edition), 10 October 2023, '$600 rockets shatter Israel's Iron Dome - will Seoul's skies be safe?' (the Monitor's bundled Iron Dome item; it predates and does not concern Iron Beam).", "title": "$600 rockets shatter Israel’s Iron Dome – will Seoul’s skies be safe?", "url": "https://english.hani.co.kr/arti/english_edition/e_international/1111552.html", "date": "2023-10-10", "written_by": "independent", "quote": "Many of the rockets fired by Hamas penetrated Israel’s low-altitude air defense system, known as the Iron Dome, and struck Israel." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0327-source-0001", "AIBD-0327-source-0002", "AIBD-0327-source-0003", "AIBD-0327-source-0004", "AIBD-0327-source-0005", "AIBD-0327-source-0006", "AIBD-0327-source-0007", "AIBD-0327-source-0008", "AIBD-0327-source-0009", "AIBD-0327-source-0010", "AIBD-0327-source-0011", "AIBD-0327-source-0012" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2026-02-28-d62f, 'AI-Enabled Iron Beam and Iron Dome Systems Deployed in Israeli Defense' (AI-generated summary).", "title": "AI-Enabled Iron Beam and Iron Dome Systems Deployed in Israeli Defense", "url": "https://oecd.ai/en/incidents/2026-02-28-d62f", "date": "2026-02-28", "written_by": "independent", "quote": "Israel deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages" }, { "kind": "news report", "citation": "Ynetnews (Yair Kraus), 2 March 2026, 'Video appears to show first combat use of Israel's Iron Beam laser defense system'.", "title": "Video appears to show first combat use of Israel’s Iron Beam laser defense system", "url": "https://www.ynetnews.com/article/h13axdmfwx", "date": "2026-03-02", "written_by": "independent", "quote": "Footage circulating on social media early Monday appeared to show the first operational use of the new Iron Beam laser air defense system" }, { "kind": "news report", "citation": "The Jerusalem Post (Seth J. Frantzman), 2 March 2026, 'Israel's Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis'.", "title": "Israel’s Iron Beam laser: What can it do, and was it used against missiles from Lebanon? - analysis", "url": "https://www.jpost.com/israel-news/defense-news/article-888515", "date": "2026-03-02", "written_by": "independent", "quote": "The video did not show lasers intercepting rockets, but the mass interest in the new technology is clear." }, { "kind": "news report", "citation": "Army Recognition, 3 March 2026, 'Israel's Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon'.", "title": "Israel’s Iron Beam Laser Linked to First Combat Intercepts of Hezbollah Rockets in Lebanon", "url": "https://www.armyrecognition.com/news/army-news/2026/israels-iron-beam-laser-linked-to-first-combat-intercepts-of-hezbollah-rockets-in-lebanon", "date": "2026-03-03", "written_by": "independent", "quote": "Israeli authorities have not released an authoritative combat log explicitly naming the system." }, { "kind": "news report", "citation": "Times Now, 2 March 2026, 'Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far'.", "title": "Did Israel Debut Their Iron Beam Defence System to Intercept Missiles Fired by Hezbollah? What We Know So Far", "url": "https://www.timesnownews.com/world/middle-east/did-israel-debut-their-iron-beam-defence-system-to-intercept-missiles-fired-by-hezbollah-what-we-know-so-far-article-153731169", "date": "2026-03-02", "written_by": "independent", "quote": "Whether this constitutes Iron Beam’s definitive first combat use awaits official confirmation." }, { "kind": "news report", "citation": "The Conversation, 4 March 2026, 'Israel's 'Iron Beam': why laser weapons are no longer science fiction'.", "title": "Israel’s ‘Iron Beam’: why laser weapons are no longer science fiction", "url": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390", "date": "2026-03-04", "written_by": "independent", "quote": "While the reports are unconfirmed, video circulating on social media appears to show rockets being destroyed within moments of launching without visible intervention" }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton and Emanuel Fabian), 28 December 2025, 'Defense Ministry hands IDF first combat-ready Iron Beam laser interception system'.", "title": "Defense Ministry hands IDF first combat-ready Iron Beam laser interception system", "url": "https://www.timesofisrael.com/defense-ministry-hands-idf-first-combat-ready-iron-beam-laser-interception-system/", "date": "2025-12-28", "written_by": "independent", "quote": "has successfully intercepted rockets, mortar shells and drones during extensive testing" }, { "kind": "news report", "citation": "The Times of Israel / AFP, 16 March 2026, 'Surge in drone attacks sharpens focus on laser defense'.", "title": "Surge in drone attacks sharpens focus on laser defense", "url": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/", "date": "2026-03-16", "written_by": "independent", "quote": "The IDF received the system in December, but has so far refused to comment on its use during the ongoing war with Iran and its Lebanese terror proxy Hezbollah." }, { "kind": "news report", "citation": "The Times of Israel (Stav Levaton), 27 March 2026, 'Cheap and elusive drones put incessant pressure on Israel's evolving air defenses'.", "title": "Cheap and elusive drones put incessant pressure on Israel’s evolving air defenses", "url": "https://www.timesofisrael.com/cheap-and-elusive-drones-put-incessant-pressure-on-israels-evolving-air-defenses/", "date": "2026-03-27", "written_by": "independent", "quote": "While the Defense Ministry said the system became operational in late 2025, military officials said it has not yet been declared operational within the IDF." }, { "kind": "news report", "citation": "The Times of Israel (Emanuel Fabian), 30 June 2026, 'Iron Dome air defenses upgraded, 'incorporating lessons learned' from Iran war'.", "title": "Iron Dome air defenses upgraded, ‘incorporating lessons learned’ from Iran war", "url": "https://www.timesofisrael.com/iron-dome-air-defenses-upgraded-incorporating-lessons-learned-from-iran-war/", "date": "2026-06-30", "written_by": "independent", "quote": "But while the military has deployed one Iron Beam system, it has seen limited use and has not yet been declared operational within the IDF." }, { "kind": "other", "citation": "Wikipedia, 'Iron Beam', accessed 20 October 2026 (on Rafael's 28 May 2025 announcement of 2024 laser interceptions).", "title": "Iron Beam", "url": "https://en.wikipedia.org/wiki/Iron_Beam", "written_by": "independent", "quote": "MOD said that no Iron Beam components were involved" }, { "kind": "news report", "citation": "Hankyoreh (English edition), 10 October 2023, '$600 rockets shatter Israel's Iron Dome - will Seoul's skies be safe?' (the Monitor's bundled Iron Dome item; it predates and does not concern Iron Beam).", "title": "$600 rockets shatter Israel’s Iron Dome – will Seoul’s skies be safe?", "url": "https://english.hani.co.kr/arti/english_edition/e_international/1111552.html", "date": "2023-10-10", "written_by": "independent", "quote": "Many of the rockets fired by Hamas penetrated Israel’s low-altitude air defense system, known as the Iron Dome, and struck Israel." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Ynet and JPost (2 March 2026) call the video unconfirmed; Times of Israel (27 Mar, 30 June 2026) quotes officials that Iron Beam “has not yet been declared operational”. No cited source calls it AI.", "account": "The OECD AI Incidents entry says that Israel “deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages”. It separately bundles a Hankyoreh item (dated 10 October 2023) on Hamas rockets penetrating Iron Dome. The Iron Beam claim rests on social-media footage from Monday 2 March 2026, not 28 February. Ynet (Yair Kraus) reports that the footage “appeared to show the first operational use” of the system. It was delivered to the Israeli Air Force in late December 2025. Hezbollah fired rockets and drones from Lebanon after the killing of Iran's Supreme Leader, with one drone intercepted near Shlomi. The Jerusalem Post (Seth Frantzman, 2 March 2026) says that the circulated video “did not show lasers intercepting rockets”. TimesNow (2 March 2026) says that whether this was Iron Beam's first combat use “awaits official confirmation”. Army Recognition (3 March 2026) says that officials “have not formally confirmed the specific engagements” and that no authoritative combat log names the system. The Conversation (4 March 2026) calls the reports unconfirmed. Moneycontrol (3 March 2026) sources its “first-ever use” to an “Israel War Room” social-media video. The Times of Israel reports the Defense Ministry's handover of the first operational system on 28 December 2025, after tests against rockets, mortars and drones. Times of Israel/AFP (16 March 2026) reports that the IDF “has so far refused to comment on its use during the ongoing war”. Times of Israel (Stav Levaton, 27 March 2026) reports that “military officials said it has not yet been declared operational within the IDF”. Times of Israel (Emanuel Fabian, 30 June 2026) reports that the one deployed system “has seen limited use and has not yet been declared operational within the IDF”. A senior officer called its coverage partial. The only confirmed laser combat use is aggregate and earlier. A lower-powered, shorter-range variant shot down some 35 drones from Lebanon in 2024 (Times of Israel/AFP). The Ministry of Defense announced it in May 2025 (JPost). The MoD said that no Iron Beam components were involved (Wikipedia). No cited source describes Iron Beam as an AI system. The only AI mention found is Rafael's CEO framing machine learning as a future capability (Breaking Defense, 22 October 2025). That piece is not among the sources. Metro, Economic Times, MoneyControl's second item, Hankyoreh and others among the Monitor's 20 listed sources are syndications or explainers." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Iron Beam ('Or Eitan') 100 kW-class high-energy laser air-defense system by Rafael Advanced Defense Systems, with beam director and electro-optical targeting; the Monitor calls it 'AI-enabled', which no cited source supports" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rafael Advanced Defense Systems with the Israel Ministry of Defense (Lockheed Martin collaboration for the US market, per Wikipedia)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Israel's Iron Beam laser may have made its first combat interception, of a Hezbollah drone, in footage of 2 March 2026", "headline": "Iron Beam may have made its first combat interception" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "cd4d27d77be41530edf2699ac8a2ccef212aaef7026277623e8af020144da5b5" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry says that Israel “deployed its AI-powered Iron Beam laser defense system for the first time in combat to intercept Hezbollah missile barrages”. It separately bundles a Hankyoreh item (dated 10 October 2023) on Hamas rockets penetrating Iron Dome. The Iron Beam claim rests on social-media footage from Monday 2 March 2026, not 28 February. Ynet (Yair Kraus) reports that the footage “appeared to show the first operational use” of the system. It was delivered to the Israeli Air Force in late December 2025. Hezbollah fired rockets and drones from Lebanon after the killing of Iran's Supreme Leader, with one drone intercepted near Shlomi. The Jerusalem Post (Seth Frantzman, 2 March 2026) says that the circulated video “did not show lasers intercepting rockets”. TimesNow (2 March 2026) says that whether this was Iron Beam's first combat use “awaits official confirmation”. Army Recognition (3 March 2026) says that officials “have not formally confirmed the specific engagements” and that no authoritative combat log names the system. The Conversation (4 March 2026) calls the reports unconfirmed. Moneycontrol (3 March 2026) sources its “first-ever use” to an “Israel War Room” social-media video. The Times of Israel reports the Defense Ministry's handover of the first operational system on 28 December 2025, after tests against rockets, mortars and drones. Times of Israel/AFP (16 March 2026) reports that the IDF “has so far refused to comment on its use during the ongoing war”. Times of Israel (Stav Levaton, 27 March 2026) reports that “military officials said it has not yet been declared operational within the IDF”. Times of Israel (Emanuel Fabian, 30 June 2026) reports that the one deployed system “has seen limited use and has not yet been declared operational within the IDF”. A senior officer called its coverage partial. The only confirmed laser combat use is aggregate and earlier. A lower-powered, shorter-range variant shot down some 35 drones from Lebanon in 2024 (Times of Israel/AFP). The Ministry of Defense announced it in May 2025 (JPost). The MoD said that no Iron Beam components were involved (Wikipedia). No cited source describes Iron Beam as an AI system. The only AI mention found is Rafael's CEO framing machine learning as a future capability (Breaking Defense, 22 October 2025). That piece is not among the sources. Metro, Economic Times, MoneyControl's second item, Hankyoreh and others among the Monitor's 20 listed sources are syndications or explainers.", "replication": { "produced_by": [ "A ground-based high-energy laser that holds its beam on a short-range target within line of sight until it is destroyed. JPost describes the terrain, weather and dwell-time limits", "Integration into the Israeli Air Force's layered air-defense array as a complement to Iron Dome" ], "must_hold": [ "IDF confirmation that Iron Beam, not Iron Dome, other interceptors or counter-UAS effects, made the 2 March 2026 interception", "Line of sight and clear weather. A senior officer said in May 2026 that the system's coverage is partial and that at least 14 batteries are sought" ], "echoes": [ { "setting": "2024 conflict with Hezbollah, northern Israel: a lower-powered, shorter-range laser variant shot down some 35 drones (Times of Israel/AFP), announced by the Ministry of Defense in May 2025 (JPost); the MoD said no Iron Beam components were involved (Wikipedia)", "model_version": "lower-powered variant (Lite Beam or Iron Beam-M, per Wikipedia's account of Israeli media)", "outcome": "recurred", "source": "https://www.timesofisrael.com/surge-in-drone-attacks-sharpens-focus-on-laser-defense/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0249", "case_url": "records/AIBD-0249.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The AI CCTV is intended to detect vehicles or pedestrians trapped in crossings and warn approaching train drivers in time to stop." }, { "label": "AI contribution", "value": "Planned system; the sources describe the intended function and rollout sequence only." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0249", "title": "Korea's transport ministry announces AI CCTV to alert train drivers to vehicles or pedestrians trapped at crossings, piloting in Nonsan and Boseong before 543 national crossings", "summary": "Kyunghyang Shinmun (11 Feb 2026): MOLIT's crossing-accident plan from March 2026 includes AI intelligent CCTV piloted in Q1 at two crossings where 2025 accidents occurred; 36 crossing accidents in ...", "date_observed": "2026-02-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Kyunghyang Shinmun's 최미랑 reported on 11 Feb 2026 that the Ministry of Land, Infrastructure and Transport had drawn up a comprehensive railway-crossing accident-prevention plan. It was to take effect from March 2026. It includes an AI-based intelligent closed-circuit television (CCTV) system, to be installed in railway protection zones nationwide. The system immediately detects a vehicle or pedestrian trapped inside a crossing. It then transmits the information to the driver of an approaching train. Pilot installations were planned within the first quarter at Nonsan in Chungnam and at Boseong, where accidents occurred in 2025. Installation at the 543 national crossings would follow in sequence, depending on the results. The plan also adds fines of up to 70,000 won, after a six-month grace period. They apply to vehicles that fail to stop or that enter while barriers operate. The ministry cited 36 crossing accidents over five years with 14 deaths and 7 injuries, 27 of them due to driver carelessness. No developer is named and no results exist. The OECD AI Incidents Monitor classified the announcement as an AI hazard rather than an incident.", "category": "general benefit", "unexpected": false, "purpose": "The AI CCTV is intended to detect vehicles or pedestrians trapped in crossings and warn approaching train drivers in time to stop.", "system": "AI-based intelligent CCTV for railway crossings (supplier not named)", "model_version": null, "developer": "Not named", "deployer": "Ministry of Land, Infrastructure and Transport (South Korea), planned", "location": null, "domain": "Railway safety", "segment": "Level-crossing obstacle detection", "benefit_type": "Increased Product Safety", "taxonomy_note": "Pilot announcement only; the type names the crossing-accident prevention aimed at.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "No outcome is documented." }, { "group": "organizational", "depth": null, "reason": "No outcome is documented." }, { "group": "personal", "depth": null, "reason": "No outcome is documented." } ], "ai_role": { "status": "unassessed", "statement": "Planned system; the sources describe the intended function and rollout sequence only." }, "evidence": [ { "kind": "news report", "citation": "최미랑 (11 Feb 2026). 철도건널목에 차량 갇히면 기관사에게 정보 전송···인공지능 CCTV 도입. 경향신문.", "title": "철도건널목에 차량 갇히면 기관사에게 정보 전송···인공지능 CCTV 도입", "url": "https://www.khan.co.kr/article/202602111540011", "date": "2026-02-11", "written_by": "independent", "quote": "국토부에 따르면 최근 5년간 철도건널목에서 발생한 36건의 사고로 14명이 숨지고 7명이 다쳤다." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-02-11-4404: South Korea Deploys AI CCTV to Prevent Railway Crossing Accidents (labelled AI hazard).", "title": "South Korea Deploys AI CCTV to Prevent Railway Crossing Accidents", "url": "https://oecd.ai/en/incidents/2026-02-11-4404", "date": "2026-02-11", "written_by": "independent", "quote": "This initiative aims to reduce frequent accidents caused by driver negligence, with nationwide rollout planned after pilot installations." } ], "replication": { "produced_by": [ "A national accident-prevention plan following fatal crossing accidents", "Pilot sites chosen where accidents occurred the previous year" ], "must_hold": [ "Pilot results support rollout", "Alerts reach drivers early enough to stop" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis linked to train braking in Japan (ITmedia)", "model_version": null, "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] }, "paired_incident": null, "headline": "AI crossing cameras to alert train drivers at Nonsan and Boseong", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The sources report the announcement of 11 February 2026. No subsequent installation or results from the Nonsan or Boseong pilots are established.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "South Korea", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Ministry_of_Land,_Infrastructure_and_Transport_(South_Korea)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0249", "catalog_version": "1.0.0", "source_fingerprint": "3fdbb43dd8967548064bb14a8359a4b0a4bc13dae589dd9ae57a508914e866a9", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0249-source-0001", "AIBD-0249-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Not named", "deployer": "Ministry of Land, Infrastructure and Transport (South Korea), planned" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "Planned system; the sources describe the intended function and rollout sequence only." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and a registry entry. 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It was to take effect from March 2026. It includes an AI-based intelligent closed-circuit television (CCTV) system, to be installed in railway protection zones nationwide. The system immediately detects a vehicle or pedestrian trapped inside a crossing. It then transmits the information to the driver of an approaching train. Pilot installations were planned within the first quarter at Nonsan in Chungnam and at Boseong, where accidents occurred in 2025. Installation at the 543 national crossings would follow in sequence, depending on the results. The plan also adds fines of up to 70,000 won, after a six-month grace period. They apply to vehicles that fail to stop or that enter while barriers operate. The ministry cited 36 crossing accidents over five years with 14 deaths and 7 injuries, 27 of them due to driver carelessness. No developer is named and no results exist. The OECD AI Incidents Monitor classified the announcement as an AI hazard rather than an incident.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "No outcome is documented." }, { "group": "organizational", "reason": "No outcome is documented." }, { "group": "personal", "reason": "No outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "The AI CCTV is intended to detect vehicles or pedestrians trapped in crossings and warn approaching train drivers in time to stop." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer_country": "South Korea" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "The AI CCTV is intended to detect vehicles or pedestrians trapped in crossings and warn approaching train drivers in time to stop." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-02-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Ministry_of_Land,_Infrastructure_and_Transport_(South_Korea)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A national accident-prevention plan following fatal crossing accidents", "Pilot sites chosen where accidents occurred the previous year" ], "must_hold": [ "Pilot results support rollout", "Alerts reach drivers early enough to stop" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis linked to train braking in Japan (ITmedia)", "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kyunghyang Shinmun (11 Feb 2026): MOLIT's crossing-accident plan from March 2026 includes AI intelligent CCTV piloted in Q1 at two crossings where 2025 accidents occurred; 36 crossing accidents in ...", "account": "Kyunghyang Shinmun's 최미랑 reported on 11 Feb 2026 that the Ministry of Land, Infrastructure and Transport had drawn up a comprehensive railway-crossing accident-prevention plan. It was to take effect from March 2026. It includes an AI-based intelligent closed-circuit television (CCTV) system, to be installed in railway protection zones nationwide. The system immediately detects a vehicle or pedestrian trapped inside a crossing. It then transmits the information to the driver of an approaching train. Pilot installations were planned within the first quarter at Nonsan in Chungnam and at Boseong, where accidents occurred in 2025. Installation at the 543 national crossings would follow in sequence, depending on the results. The plan also adds fines of up to 70,000 won, after a six-month grace period. They apply to vehicles that fail to stop or that enter while barriers operate. The ministry cited 36 crossing accidents over five years with 14 deaths and 7 injuries, 27 of them due to driver carelessness. No developer is named and no results exist. The OECD AI Incidents Monitor classified the announcement as an AI hazard rather than an incident." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI-based intelligent CCTV for railway crossings (supplier not named)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Not named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Korea's transport ministry announces AI CCTV to alert train drivers to vehicles or pedestrians trapped at crossings, piloting in Nonsan and Boseong before 543 national crossings", "headline": "AI crossing cameras to alert train drivers at Nonsan and Boseong" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "3fdbb43dd8967548064bb14a8359a4b0a4bc13dae589dd9ae57a508914e866a9" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Kyunghyang Shinmun's 최미랑 reported on 11 Feb 2026 that the Ministry of Land, Infrastructure and Transport had drawn up a comprehensive railway-crossing accident-prevention plan. It was to take effect from March 2026. It includes an AI-based intelligent closed-circuit television (CCTV) system, to be installed in railway protection zones nationwide. The system immediately detects a vehicle or pedestrian trapped inside a crossing. It then transmits the information to the driver of an approaching train. Pilot installations were planned within the first quarter at Nonsan in Chungnam and at Boseong, where accidents occurred in 2025. Installation at the 543 national crossings would follow in sequence, depending on the results. The plan also adds fines of up to 70,000 won, after a six-month grace period. They apply to vehicles that fail to stop or that enter while barriers operate. The ministry cited 36 crossing accidents over five years with 14 deaths and 7 injuries, 27 of them due to driver carelessness. No developer is named and no results exist. The OECD AI Incidents Monitor classified the announcement as an AI hazard rather than an incident.", "replication": { "produced_by": [ "A national accident-prevention plan following fatal crossing accidents", "Pilot sites chosen where accidents occurred the previous year" ], "must_hold": [ "Pilot results support rollout", "Alerts reach drivers early enough to stop" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis linked to train braking in Japan (ITmedia)", "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0309", "case_url": "records/AIBD-0309.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented." }, { "label": "AI contribution", "value": "No incident is documented. The AI character of the cameras rests on SpoorPro's caption and ProRail's “smart cameras”. The trespasser-flagging function is described by Genetec, the platform vendor. Whether the analytics changed any outcome is not reported. The full 2 February 2026 article is paywalled." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Netherlands" ] }, "record": { "id": "AIBD-0309", "title": "ProRail replaces 288 lineside cameras with AI “smart cameras” in 2026 and uses Genetec video analytics across 7,000 cameras to flag trespassers to train dispatchers", "summary": "SpoorPro: ProRail swaps 288 lineside cameras for smart cameras in 2026, tests drones and runs Genetec video analytics on 7,000 cameras to flag trespassers to dispatchers; no results are given.", "date_observed": "2026-02-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "category": "general benefit", "unexpected": false, "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented.", "system": "ProRail smart lineside cameras with Genetec Security Center / Mission Control video analytics", "model_version": null, "developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named", "deployer": "ProRail", "location": "Netherlands", "domain": "Railway safety / trespass detection", "segment": "Rail infrastructure camera surveillance", "benefit_type": "Increased Product Safety", "taxonomy_note": "Deployment only; trespass detection for dispatchers is read as a safety detection mechanism.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "ProRail says the cameras help it see and prevent incidents sooner. No incident, detection count or delay figure is published." }, { "group": "societal", "depth": null, "reason": "Fewer trespass incidents and disruptions are the stated aim. No before-and-after data are given." } ], "ai_role": { "status": "unassessed", "statement": "No incident is documented. The AI character of the cameras rests on SpoorPro's caption and ProRail's “smart cameras”. The trespasser-flagging function is described by Genetec, the platform vendor. Whether the analytics changed any outcome is not reported. The full 2 February 2026 article is paywalled." }, "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ], "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] }, "paired_incident": null, "headline": "ProRail uses Genetec analytics on 7,000 cameras to flag trespass", "metric": null, "comparator": { "against": "ProRail's previous lineside camera surveillance (ProRail's own assertion)", "before": null, "after": "incidenten sneller te zien en te voorkomen", "basis": "assumed", "quote": "‘Deze nieuwe vormen van cameratoezicht helpen ons om incidenten sneller te zien en te voorkomen. Dat maakt het spoor veiliger en zorgt voor minder storingen en vertragingen.’", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/" }, "later": { "status": "scaled", "date": "2026-03-03", "note": "A month later SpoorPro reported that ProRail is bringing all 7,000 cameras it manages into one Genetec platform monitored from its Smart Monitoring Room, with video analytics used among other things to flag people on the track to train dispatchers; no detection count or outcome is given.", "quote": "ProRail brengt de 7000 beveiligingscamera’s die de organisatie beheert onder in één geïntegreerd platform van technologiebedrijf Genetec. Daarmee wil de spoorbeheerder incidenten op en rond het spoor sneller signaleren en afhandelen.", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/" }, "value": null, "developer_country": "Canada", "deployer_country": "Netherlands", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0309", "catalog_version": "1.0.0", "source_fingerprint": "a4a48ce8da38592a09e4f07df3b22efedbddebcbe5d7f00ddd3215a53e1fc190", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0309-source-0001", "AIBD-0309-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named", "deployer": "ProRail" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "No incident is documented. The AI character of the cameras rests on SpoorPro's caption and ProRail's “smart cameras”. The trespasser-flagging function is described by Genetec, the platform vendor. Whether the analytics changed any outcome is not reported. The full 2 February 2026 article is paywalled." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports. 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No before-and-after data are given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "ProRail says the cameras help it see and prevent incidents sooner. No incident, detection count or delay figure is published." }, { "group": "societal", "reason": "Fewer trespass incidents and disruptions are the stated aim. 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"review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-03-03", "note": "A month later SpoorPro reported that ProRail is bringing all 7,000 cameras it manages into one Genetec platform monitored from its Smart Monitoring Room, with video analytics used among other things to flag people on the track to train dispatchers; no detection count or outcome is given.", "quote": "ProRail brengt de 7000 beveiligingscamera’s die de organisatie beheert onder in één geïntegreerd platform van technologiebedrijf Genetec. Daarmee wil de spoorbeheerder incidenten op en rond het spoor sneller signaleren en afhandelen.", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0309" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "Deployment only; trespass detection for dispatchers is read as a safety detection mechanism." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "ProRail says the cameras help it see and prevent incidents sooner. No incident, detection count or delay figure is published." }, { "group": "societal", "reason": "Fewer trespass incidents and disruptions are the stated aim. No before-and-after data are given." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Netherlands", "developer_country": "Canada", "deployer_country": "Netherlands" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Camera upgrades and video analytics intended to detect incidents such as trespassers sooner so that dispatchers can warn drivers. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2026-02-02", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/; https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wikidata.org/wiki/Q30267239; https://www.prorail.nl/contact", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Railway safety / trespass detection", "segment": "Rail infrastructure camera surveillance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0309-source-0001", "AIBD-0309-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail zet 288 nóg slimmere camera's in langs het spoor. 2 February 2026 (remainder paywalled).", "title": "ProRail zet 288 nóg slimmere camera's in langs het spoor", "url": "https://www.spoorpro.nl/actueel/2026/02/02/prorail-zet-288-nog-slimmere-cameras-in-langs-het-spoor/", "date": "2026-02-02", "written_by": "independent", "quote": "ProRail vervangt in 2026 in totaal 288 camera's langs het spoor voor nieuwe, slimme camera's." }, { "kind": "news report", "citation": "SpoorPro.nl (2026). ProRail gaat alle 7000 beveiligingscamera's centraal monitoren. 3 March 2026 (also published on Mobiliteit.nl).", "title": "ProRail gaat alle 7000 beveiligingscamera's centraal monitoren", "url": "https://www.spoorpro.nl/innovatie/2026/03/03/prorail-centraliseert-7-000-cameras-in-een-uniform-platform-voor-spoortoezicht/", "date": "2026-03-03", "written_by": "independent", "quote": "Video-analyse wordt onder meer ingezet om spoorlopers te signaleren. In dat geval kan de treindienstleider machinisten waarschuwen." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "SpoorPro: ProRail swaps 288 lineside cameras for smart cameras in 2026, tests drones and runs Genetec video analytics on 7,000 cameras to flag trespassers to dispatchers; no results are given.", "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ProRail smart lineside cameras with Genetec Security Center / Mission Control video analytics" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Genetec (video management and analytics platform); the smart-camera supplier is not named" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "ProRail replaces 288 lineside cameras with AI “smart cameras” in 2026 and uses Genetec video analytics across 7,000 cameras to flag trespassers to train dispatchers", "headline": "ProRail uses Genetec analytics on 7,000 cameras to flag trespass" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "a4a48ce8da38592a09e4f07df3b22efedbddebcbe5d7f00ddd3215a53e1fc190" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "SpoorPro.nl reported on 2 February 2026 that ProRail, the Dutch rail infrastructure manager, is replacing 288 lineside cameras in 2026 with new “smart” cameras. ProRail is also testing drones that fly autonomously and analyze images. ProRail is quoted that these forms of camera surveillance help it see and prevent incidents sooner, making the track safer with fewer disruptions and delays. The article's photo caption says the AI cameras' images are monitored 24/7. The rest of the text is behind a paywall. SpoorPro reported on 3 March 2026 that ProRail is bringing the 7,000 security cameras it manages into one Genetec platform. It is monitored from ProRail's Smart Monitoring Room. Genetec Mission Control filters and analyzes alerts and forwards relevant images to an operator with suggested actions. Video analysis is used among other things to flag spoorlopers (people on the track). In that case the train dispatcher can warn drivers. The article quotes ProRail project lead Kristian Ruiter and Genetec's Bob van Keulen. No source gives a detection count, a stopped train or a trespass reduction attributable to the analytics. No source on record identifies a UIC trespass-detection trial.", "replication": { "produced_by": [ "Thousands of lineside and station security cameras brought into one Genetec video-management platform", "Operators in ProRail's Smart Monitoring Room receiving filtered alerts with suggested actions", "Video analytics configured to flag people on the track" ], "must_hold": [ "A dispatcher able to warn drivers once a trespasser is flagged", "Privacy masking and blurring applied to passenger images, as the article states ProRail does" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 26, "reviewed": 0, "record_mapped": 26, "not_assessed": 65, "complete": false, "by_state": { "value_present": 26, "not_reported": 0, "unresolved": 0, "not_assessed": 65, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0181", "case_url": "records/AIBD-0181.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Identify customs fraud, undervaluation and falsified import documentation and give full coverage of customs assessment." }, { "label": "AI contribution", "value": "Neither source describes how the analytics identify undervaluation or what share of the claimed recovery the system produced. The system is vendor-built and deployed by the GRA." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Ghana" ] }, "record": { "id": "AIBD-0181", "title": "Ghana Revenue Authority launches Publican AI trade analytics, which may have recovered GH¢15 million", "summary": "GRA's Publican AI customs analytics, supplied by an unnamed Cyprus-based firm, rolled out from 1 February 2026 at Tema Port; the OECD AI Incidents Monitor summary says GH¢15 million was recovered, ...", "date_observed": "2026-01-16", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents Monitor entry dated 16 January 2026 summarizes Ghanaian reports that the Ghana Revenue Authority launched Publican AI. That is a trade data analytics platform to identify customs fraud, undervaluation and falsified import documentation. The entry states that the system has already recovered GH¢15 million from fraudulent importers. Its source is a GhanaWeb story titled “Government recovers GH¢15 million through new trade data system”. The entry is classified as an AI Incident with the harm type economic harm to property interests. The Ghanaian Times (Kingsley Asare, 19 January 2026) reported remarks by Commissioner-General Dr Anthony Sarpong. He said that the Publican AI system would give 100 percent coverage of assessment. He said that assessments could be completed within five minutes instead of about two hours. He said that pilots showed 40–45% revenue gains. He said that rollout would begin at Tema Port on 1 February 2026. That article cites no realized recovery. The vendor is described only as a Cyprus-based firm.", "category": "general benefit", "unexpected": false, "purpose": "Identify customs fraud, undervaluation and falsified import documentation and give full coverage of customs assessment.", "system": "Publican AI Trade Solution", "model_version": null, "developer": "A Cyprus-based firm, unnamed in the read article", "deployer": "Ghana Revenue Authority, Customs Division", "location": "Tema, Ghana", "domain": "Customs revenue and fraud", "segment": "Import valuation and customs fraud", "benefit_type": "Increased Product Revenue", "taxonomy_note": "Tax yield is read as the authority's revenue; Trustible's description is written for product sales.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "The recovery figure is a secondary summary and the revenue gain is a projection. No verified outcome for the GRA is documented." }, { "group": "societal", "depth": null, "reason": "Public revenue effects are projected, not observed, in the sources." } ], "ai_role": { "status": "unassessed", "statement": "Neither source describes how the analytics identify undervaluation or what share of the claimed recovery the system produced. The system is vendor-built and deployed by the GRA." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents Monitor, entry 2026-01-16-e77d, classified AI Incident", "title": "Ghana Deploys AI System to Combat Customs Fraud and Recover Revenue", "url": "https://oecd.ai/en/incidents/2026-01-16-e77d", "date": "2026-01-16", "written_by": "independent", "quote": "Ghana Deploys AI System to Combat Customs Fraud and Recover Revenue" }, { "kind": "news report", "citation": "Kingsley Asare, Ghanaian Times, 19 January 2026", "title": "AI trade analytics rollout to boost customs revenue by 45% – GRA Commissioner-General", "url": "https://ghanaiantimes.com.gh/ai-trade-analytics-rollout-to-boost-customs-revenue-by-45-gra-commissioner-general/", "date": "2026-01-19", "written_by": "independent", "quote": "The Publican AI system will give us 100 per cent coverage of assessment, ensuring that every shipment is scrutinised and valued accurately." }, { "kind": "news report", "citation": "MyJoyOnline (2026). Publican AI boosts GRA customs revenue to GH¢6.1bn in July – Commissioner-General. MyJoyOnline, 9 August 2026.", "title": "Publican AI boosts GRA customs revenue to GH¢6.1bn in July – Commissioner-General", "url": "https://www.myjoyonline.com/publican-ai-boosts-gra-customs-revenue-to-gh%C2%A26-1bn-in-july-commissioner-general/", "date": "2026-08-09", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Trade data analytics applied to every import declaration rather than sampled assessments" ], "must_hold": [ "Access to shipment declarations and valuation references", "Enforcement capacity to collect assessed underpayments", "Rollout beyond the Tema Port start" ], "echoes": [] }, "paired_incident": null, "headline": "GRA's Publican AI may have recovered GH¢15 million", "metric": "the system has already recovered GH¢15 million from fraudulent importers", "comparator": { "against": "manual assessment and classification of goods by customs officers", "before": "could take up to two hours after importers submitted their documentation", "after": "assessments could be completed within five minutes", "basis": "assumed", "quote": "Dr Sarpong noted that currently, the assessment and classification of goods involved considerable human effort, which could take up to two hours after importers submitted their documentation. With the introduction of the AI tool, he said, assessments could be completed within five minutes, thereby improving speed and efficiency in clearance processes.", "url": "https://ghanaiantimes.com.gh/ai-trade-analytics-rollout-to-boost-customs-revenue-by-45-gra-commissioner-general/" }, "later": { "status": "scaled", "date": "2026-08-09", "note": "After full implementation from April 2026, GRA said customs collections rose from about GH¢4 billion a month to GH¢6.1 billion in July 2026.", "quote": "Commissioner-General of the GRA, Anthony Sarpong, said monthly customs revenue averaged about GH¢4 billion before the full implementation of the system, indicating an increase of about GH¢2.1 billion in July.", "url": "https://www.myjoyonline.com/publican-ai-boosts-gra-customs-revenue-to-gh%C2%A26-1bn-in-july-commissioner-general/" }, "value": { "amount": "GH¢15 million", "what": "recovered from fraudulent importers", "who": "Government of Ghana (Ghana Revenue Authority)", "estimate": false, "quote": "The system has already enabled the government to recover GH¢15 million from fraudulent importers and aims to address significant revenue leakages in Ghana's customs operations.", "url": "https://oecd.ai/en/incidents/2026-01-16-e77d" }, "developer_country": null, "deployer_country": "Ghana", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Revenue under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://ghanaiantimes.com.gh/ai-trade-analytics-rollout-to-boost-customs-revenue-by-45-gra-commissioner-general/; https://www.myjoyonline.com/publican-ai-boosts-gra-customs-revenue-to-gh%C2%A26-1bn-in-july-commissioner-general/; https://oecd.ai/en/incidents/2026-01-16-e77d", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://gra.gov.gh/contact-us/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0181", "catalog_version": "1.0.0", "source_fingerprint": "b42ede4e5e6af041e90e473f78c60cb3a33f3d27e621921ec0b6e244c98605f9", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0181-source-0001", "AIBD-0181-source-0002", "AIBD-0181-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0181-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "A Cyprus-based firm, unnamed in the read article", "deployer": "Ghana Revenue Authority, Customs Division" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "Neither source describes how the analytics identify undervaluation or what share of the claimed recovery the system produced. The system is vendor-built and deployed by the GRA." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "The recovery figure is a secondary summary and the revenue gain is a projection. No verified outcome for the GRA is documented." }, { "group": "societal", "reason": "Public revenue effects are projected, not observed, in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "The recovery figure is a secondary summary and the revenue gain is a projection. 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MyJoyOnline, 9 August 2026.", "title": "Publican AI boosts GRA customs revenue to GH¢6.1bn in July – Commissioner-General", "url": "https://www.myjoyonline.com/publican-ai-boosts-gra-customs-revenue-to-gh%C2%A26-1bn-in-july-commissioner-general/", "date": "2026-08-09", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "GRA's Publican AI customs analytics, supplied by an unnamed Cyprus-based firm, rolled out from 1 February 2026 at Tema Port; the OECD AI Incidents Monitor summary says GH¢15 million was recovered, ...", "account": "The OECD AI Incidents Monitor entry dated 16 January 2026 summarizes Ghanaian reports that the Ghana Revenue Authority launched Publican AI. That is a trade data analytics platform to identify customs fraud, undervaluation and falsified import documentation. The entry states that the system has already recovered GH¢15 million from fraudulent importers. Its source is a GhanaWeb story titled “Government recovers GH¢15 million through new trade data system”. The entry is classified as an AI Incident with the harm type economic harm to property interests. The Ghanaian Times (Kingsley Asare, 19 January 2026) reported remarks by Commissioner-General Dr Anthony Sarpong. He said that the Publican AI system would give 100 percent coverage of assessment. He said that assessments could be completed within five minutes instead of about two hours. He said that pilots showed 40–45% revenue gains. He said that rollout would begin at Tema Port on 1 February 2026. That article cites no realized recovery. The vendor is described only as a Cyprus-based firm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Publican AI Trade Solution" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "A Cyprus-based firm, unnamed in the read article" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Ghana Revenue Authority launches Publican AI trade analytics, which may have recovered GH¢15 million", "headline": "GRA's Publican AI may have recovered GH¢15 million" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "b42ede4e5e6af041e90e473f78c60cb3a33f3d27e621921ec0b6e244c98605f9" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents Monitor entry dated 16 January 2026 summarizes Ghanaian reports that the Ghana Revenue Authority launched Publican AI. That is a trade data analytics platform to identify customs fraud, undervaluation and falsified import documentation. The entry states that the system has already recovered GH¢15 million from fraudulent importers. Its source is a GhanaWeb story titled “Government recovers GH¢15 million through new trade data system”. The entry is classified as an AI Incident with the harm type economic harm to property interests. The Ghanaian Times (Kingsley Asare, 19 January 2026) reported remarks by Commissioner-General Dr Anthony Sarpong. He said that the Publican AI system would give 100 percent coverage of assessment. He said that assessments could be completed within five minutes instead of about two hours. He said that pilots showed 40–45% revenue gains. He said that rollout would begin at Tema Port on 1 February 2026. That article cites no realized recovery. The vendor is described only as a Cyprus-based firm.", "replication": { "produced_by": [ "Trade data analytics applied to every import declaration rather than sampled assessments" ], "must_hold": [ "Access to shipment declarations and valuation references", "Enforcement capacity to collect assessed underpayments", "Rollout beyond the Tema Port start" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0180", "case_url": "records/AIBD-0180.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Identify bank accounts likely used as money mules in financial fraud." }, { "label": "AI contribution", "value": "The tool is described as AI-based pattern analysis across banks. No detection result is available to assess its contribution. RBIH, a central-bank subsidiary, developed it. Commercial and public-sector banks deploy it." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0180", "title": "RBI Innovation Hub's MuleHunter.AI adopted by 23 banks; RBI declines to disclose mule accounts found", "summary": "MediaNama's RTI (December 2025) found 23 banks had implemented RBIH's MuleHunter.AI, which analyzes transaction patterns to spot mule accounts; the RBI refused to release detection figures, citing ...", "date_observed": "2025-12-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "MediaNama (Prabhanu Kumar Das) reported on 30 December 2025 on a Right to Information response. It showed 23 banks had implemented MuleHunter.AI as of 10 December 2025. That was up from at least 15 in August and nearly 20 in November 2025. The report named Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank. The tool was developed by the Reserve Bank Innovation Hub (RBIH). It analyzes transaction and account activity patterns across banks to identify accounts likely used as mules in financial fraud. The Reserve Bank of India (RBI) said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI. The reason given was that it holds such information in a fiduciary capacity with banks and disclosure could harm competitive interests. RBI Governor Sanjay Malhotra is reported as saying that the tool is showing a good success rate, without figures. RBIH's own project page was not available. News headlines from May to September 2026 describe an I4C-RBIH memorandum and deployment across 31 banks. The articles behind those headlines are not among the sources.", "category": "general benefit", "unexpected": false, "purpose": "Identify bank accounts likely used as money mules in financial fraud.", "system": "MuleHunter.AI", "model_version": null, "developer": "Reserve Bank Innovation Hub (RBIH)", "deployer": "23 Indian banks as of 10 December 2025, including Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank", "location": null, "domain": "Anti-money laundering and cyber fraud", "segment": "Mule account detection", "benefit_type": "Reduced Risk", "taxonomy_note": "Adoption only; the type names the mule-account fraud the tool is meant to catch.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "No count of mule accounts identified or frozen is disclosed. The RBI refused the RTI request for that data." }, { "group": "societal", "depth": null, "reason": "No fraud loss avoided is documented." } ], "ai_role": { "status": "unassessed", "statement": "The tool is described as AI-based pattern analysis across banks. No detection result is available to assess its contribution. RBIH, a central-bank subsidiary, developed it. Commercial and public-sector banks deploy it." }, "evidence": [ { "kind": "news report", "citation": "Prabhanu Kumar Das, MediaNama, 30 December 2025", "title": "RTI Reveals 23 Banks Have Implemented Mulehunter.AI, But Total Mule Accounts Identified Still Not Disclosed", "url": "https://www.medianama.com/2025/12/223-rti-23-banks-mulehunter-mule-accounts/", "date": "2025-12-30", "written_by": "independent", "quote": "cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI" }, { "kind": "press release", "citation": "Press Information Bureau (2026). I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds. Ministry of Home Affairs via PIB, 12 May 2026.", "title": "I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "date": "2026-05-12", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Cross-bank transaction and account-activity pattern analysis run by a central-bank-owned hub" ], "must_hold": [ "Bank adoption and data sharing with the hub", "Public disclosure of detection outcomes, without which no evaluation is possible" ], "echoes": [ { "date": "2026-05-12", "setting": "I4C and RBIH memorandum on AI-driven mule-account detection", "model_version": null, "outcome": "unknown", "source": "Google News RSS headlines (Indian Express, Economic Times, DD News) only" } ] }, "paired_incident": null, "headline": "RBIH's MuleHunter.AI adopted by 23 banks; RBI withholds figures", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-05-12", "note": "I4C and RBIH signed an MoU to feed suspect identifiers from I4C's Suspect Registry into MuleHunter.ai and to use the data to retrain it.", "quote": "Under the MoU, I4C and RBIH will collaborate for sharing of mule account related intelligence and suspect identifiers from I4C-MHA’s Suspect Registry to strengthen AI-driven fraud detection systems such as MuleHunter.ai™ implemented across banks. RBIH will utilise the datasets for training and enhancement of AI-driven fraud-risk assessment models, including MuleHunter.ai™.", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277" }, "value": null, "developer_country": "India", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Reserve_Bank_of_India; https://en.wikipedia.org/wiki/Canara_Bank", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0180", "catalog_version": "1.0.0", "source_fingerprint": "3a66e7de9ab0a77fb448b9bf589ebea2a990899ef305b2b3dc90d0d5b687ffd1", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0180-source-0001", "AIBD-0180-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Reserve Bank Innovation Hub (RBIH)", "deployer": "23 Indian banks as of 10 December 2025, including Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "The tool is described as AI-based pattern analysis across banks. No detection result is available to assess its contribution. RBIH, a central-bank subsidiary, developed it. Commercial and public-sector banks deploy it." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "No count of mule accounts identified or frozen is disclosed. The RBI refused the RTI request for that data." }, { "group": "societal", "reason": "No fraud loss avoided is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "No count of mule accounts identified or frozen is disclosed. The RBI refused the RTI request for that data." }, { "group": "societal", "reason": "No fraud loss avoided is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-05-12", "note": "I4C and RBIH signed an MoU to feed suspect identifiers from I4C's Suspect Registry into MuleHunter.ai and to use the data to retrain it.", "quote": "Under the MoU, I4C and RBIH will collaborate for sharing of mule account related intelligence and suspect identifiers from I4C-MHA’s Suspect Registry to strengthen AI-driven fraud detection systems such as MuleHunter.ai™ implemented across banks. RBIH will utilise the datasets for training and enhancement of AI-driven fraud-risk assessment models, including MuleHunter.ai™.", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0180" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Adoption only; the type names the mule-account fraud the tool is meant to catch." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "MediaNama (Prabhanu Kumar Das) reported on 30 December 2025 on a Right to Information response. It showed 23 banks had implemented MuleHunter.AI as of 10 December 2025. That was up from at least 15 in August and nearly 20 in November 2025. The report named Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank. The tool was developed by the Reserve Bank Innovation Hub (RBIH). It analyzes transaction and account activity patterns across banks to identify accounts likely used as mules in financial fraud. The Reserve Bank of India (RBI) said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI. The reason given was that it holds such information in a fiduciary capacity with banks and disclosure could harm competitive interests. RBI Governor Sanjay Malhotra is reported as saying that the tool is showing a good success rate, without figures. RBIH's own project page was not available. News headlines from May to September 2026 describe an I4C-RBIH memorandum and deployment across 31 banks. The articles behind those headlines are not among the sources.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "No count of mule accounts identified or frozen is disclosed. The RBI refused the RTI request for that data." }, { "group": "societal", "reason": "No fraud loss avoided is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify bank accounts likely used as money mules in financial fraud." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer_country": "India", "deployer_country": "India" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Identify bank accounts likely used as money mules in financial fraud." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-12-30", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Reserve_Bank_of_India; https://en.wikipedia.org/wiki/Canara_Bank", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Cross-bank transaction and account-activity pattern analysis run by a central-bank-owned hub" ], "must_hold": [ "Bank adoption and data sharing with the hub", "Public disclosure of detection outcomes, without which no evaluation is possible" ], "echoes": [ { "date": "2026-05-12", "setting": "I4C and RBIH memorandum on AI-driven mule-account detection", "outcome": "unknown", "source": "Google News RSS headlines (Indian Express, Economic Times, DD News) only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Cross-bank transaction and account-activity pattern analysis run by a central-bank-owned hub" ], "must_hold": [ "Bank adoption and data sharing with the hub", "Public disclosure of detection outcomes, without which no evaluation is possible" ], "echoes": [ { "date": "2026-05-12", "setting": "I4C and RBIH memorandum on AI-driven mule-account detection", "outcome": "unknown", "source": "Google News RSS headlines (Indian Express, Economic Times, DD News) only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Reserve_Bank_of_India; https://en.wikipedia.org/wiki/Canara_Bank", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Anti-money laundering and cyber fraud", "segment": "Mule account detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Prabhanu Kumar Das, MediaNama, 30 December 2025", "title": "RTI Reveals 23 Banks Have Implemented Mulehunter.AI, But Total Mule Accounts Identified Still Not Disclosed", "url": "https://www.medianama.com/2025/12/223-rti-23-banks-mulehunter-mule-accounts/", "date": "2025-12-30", "written_by": "independent", "quote": "cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI" }, { "kind": "press release", "citation": "Press Information Bureau (2026). I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds. Ministry of Home Affairs via PIB, 12 May 2026.", "title": "I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "date": "2026-05-12", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0180-source-0001", "AIBD-0180-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Prabhanu Kumar Das, MediaNama, 30 December 2025", "title": "RTI Reveals 23 Banks Have Implemented Mulehunter.AI, But Total Mule Accounts Identified Still Not Disclosed", "url": "https://www.medianama.com/2025/12/223-rti-23-banks-mulehunter-mule-accounts/", "date": "2025-12-30", "written_by": "independent", "quote": "cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI" }, { "kind": "press release", "citation": "Press Information Bureau (2026). I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds. Ministry of Home Affairs via PIB, 12 May 2026.", "title": "I4C and RBIH Sign MoU to Strengthen AI-Driven Detection of Mule Accounts and Cyber Financial Frauds", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260277", "date": "2026-05-12", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MediaNama's RTI (December 2025) found 23 banks had implemented RBIH's MuleHunter.AI, which analyzes transaction patterns to spot mule accounts; the RBI refused to release detection figures, citing ...", "account": "MediaNama (Prabhanu Kumar Das) reported on 30 December 2025 on a Right to Information response. It showed 23 banks had implemented MuleHunter.AI as of 10 December 2025. That was up from at least 15 in August and nearly 20 in November 2025. The report named Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank. The tool was developed by the Reserve Bank Innovation Hub (RBIH). It analyzes transaction and account activity patterns across banks to identify accounts likely used as mules in financial fraud. The Reserve Bank of India (RBI) said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI. The reason given was that it holds such information in a fiduciary capacity with banks and disclosure could harm competitive interests. RBI Governor Sanjay Malhotra is reported as saying that the tool is showing a good success rate, without figures. RBIH's own project page was not available. News headlines from May to September 2026 describe an I4C-RBIH memorandum and deployment across 31 banks. The articles behind those headlines are not among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "MuleHunter.AI" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Reserve Bank Innovation Hub (RBIH)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "RBI Innovation Hub's MuleHunter.AI adopted by 23 banks; RBI declines to disclose mule accounts found", "headline": "RBIH's MuleHunter.AI adopted by 23 banks; RBI withholds figures" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "3a66e7de9ab0a77fb448b9bf589ebea2a990899ef305b2b3dc90d0d5b687ffd1" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "MediaNama (Prabhanu Kumar Das) reported on 30 December 2025 on a Right to Information response. It showed 23 banks had implemented MuleHunter.AI as of 10 December 2025. That was up from at least 15 in August and nearly 20 in November 2025. The report named Canara Bank, Punjab National Bank, Bank of India, Bank of Baroda, AU Small Finance Bank and Federal Bank. The tool was developed by the Reserve Bank Innovation Hub (RBIH). It analyzes transaction and account activity patterns across banks to identify accounts likely used as mules in financial fraud. The Reserve Bank of India (RBI) said that it cannot provide data on the number of mule accounts identified or acted upon through MuleHunter.AI. The reason given was that it holds such information in a fiduciary capacity with banks and disclosure could harm competitive interests. RBI Governor Sanjay Malhotra is reported as saying that the tool is showing a good success rate, without figures. RBIH's own project page was not available. News headlines from May to September 2026 describe an I4C-RBIH memorandum and deployment across 31 banks. The articles behind those headlines are not among the sources.", "replication": { "produced_by": [ "Cross-bank transaction and account-activity pattern analysis run by a central-bank-owned hub" ], "must_hold": [ "Bank adoption and data sharing with the hub", "Public disclosure of detection outcomes, without which no evaluation is possible" ], "echoes": [ { "date": "2026-05-12", "setting": "I4C and RBIH memorandum on AI-driven mule-account detection", "outcome": "unknown", "source": "Google News RSS headlines (Indian Express, Economic Times, DD News) only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0163", "case_url": "records/AIBD-0163.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Quartz Solar is built to forecast national and regional solar PV output for grid balancing. Operational use for reserve setting is the intended purpose." }, { "label": "AI contribution", "value": "The machine-learning forecast is the system in use. But both figures (£30m, 300,000 t CO2) come from Open Climate Fix's announcement reproduced by trade press. NESO's own quoted statement confirms deployment and 36-hour accuracy only. No NESO-authored page or regulator document with a savings figure was found." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0163", "title": "Open Climate Fix's Quartz Solar goes live in NESO's control room and may avoid £30m a year in imbalance costs", "summary": "Open Climate Fix's machine-learning solar forecast is live in NESO's control room; OCF claims at least £30m a year in avoided imbalance costs, a figure NESO's quoted statement does not give.", "date_observed": "2025-11-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 6 November 2025 Open Climate Fix (OCF), a not-for-profit, announced that Quartz Solar was live in the National Energy System Operator's (NESO) control room. OCF's AI-powered solar PV forecasting tool combines machine learning, live satellite imagery and weather data. It is fully integrated from day-ahead scheduling to real-time control after starting as a Network Innovation Allowance project (The Energyst, 6 November 2025). The announcement says the tool helps avoid at least £30 million in imbalance costs a year and cuts around 300,000 tonnes of CO2. NESO's Carolina Tortora, Head of AI, Digitalisation and Innovation, is quoted saying it can accurately predict solar generation up to 36 hours in advance. She says it is revolutionizing solar forecasts in the control room. She does not state the £30m figure. pv magazine (7 November 2025) attributes the £30 million (US$39 million) per year saving to Quartz Solar itself. It reports OCF's claim of halving forecasting errors across about 300 grid supply points. OCF's website says the forecast is used daily by NESO. The savings are the maker's estimate, not an operator-reported or audited figure.", "category": "general benefit", "unexpected": false, "purpose": "Quartz Solar is built to forecast national and regional solar PV output for grid balancing. Operational use for reserve setting is the intended purpose.", "system": "Quartz Solar (Open Climate Fix machine-learning solar PV forecast, formerly PVNet)", "model_version": "PVNet", "developer": "Open Climate Fix", "deployer": "National Energy System Operator (NESO)", "location": "United Kingdom", "domain": "Energy", "segment": "Electricity system operation and balancing", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "NESO confirms operational use and 36-hour forecasting. The £30m saving is OCF's estimate, so no measured outcome is documented." }, { "group": "societal", "depth": null, "reason": "The 300,000-tonne CO2 figure is the maker's estimate. No independent measurement is documented." } ], "ai_role": { "status": "unassessed", "statement": "The machine-learning forecast is the system in use. But both figures (£30m, 300,000 t CO2) come from Open Climate Fix's announcement reproduced by trade press. NESO's own quoted statement confirms deployment and 36-hour accuracy only. No NESO-authored page or regulator document with a savings figure was found." }, "evidence": [ { "kind": "news report", "citation": "Tim McManan-Smith, 'National Energy System Operator adopts AI solar forecasting to save £30m', The Energyst, 6 November 2025", "title": "National Energy System Operator adopts AI solar forecasting to save £30m", "url": "https://theenergyst.com/national-energy-system-operator-adopts-ai-solar-forecasting-to-save-30m/", "date": "2025-11-06", "written_by": "independent", "quote": "developed a cutting-edge tool that can accurately predict solar generation up to 36 hours in advance—revolutionising our solar forecasts in the control room." }, { "kind": "news report", "citation": "The Energyst, 6 November 2025 (claim from Open Climate Fix's announcement)", "title": "National Energy System Operator adopts AI solar forecasting to save £30m", "url": "https://theenergyst.com/national-energy-system-operator-adopts-ai-solar-forecasting-to-save-30m/", "date": "2025-11-06", "written_by": "independent", "quote": "helping to avoid at least £30 million in imbalance costs each year and cutting around 300,000 tonnes of CO₂ emissions." }, { "kind": "news report", "citation": "Matthew Lynas, 'AI powered solar forecasting helps UK grid operator reduce balancing costs', pv magazine, 7 November 2025", "title": "AI powered solar forecasting helps UK grid operator reduce balancing costs", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/", "date": "2025-11-07", "written_by": "independent", "quote": "Quartz Solar says its tool saves Great Britain's grid operator GBP 30 million ($39 million) per year through more accurate forecasting" }, { "kind": "press release", "citation": "Open Climate Fix website, product spotlight, GB National Solar Forecast", "title": "Open Climate Fix", "url": "https://openclimatefix.org/", "date": null, "written_by": "makers", "quote": "Used daily by the National Energy System Operator, as well as some of the top power trading desks across the UK." } ], "replication": { "produced_by": [ "Satellite imagery, numerical weather data and historical generation data for a national PV fleet", "A system operator innovation program (NIA) that funded testing before operational integration", "A forecast refresh cycle faster than the operator's existing six-hourly weather updates" ], "must_hold": [ "Access to live satellite and generation data", "An operator willing to integrate a third-party forecast into reserve setting", "Independent measurement of imbalance-cost changes, which is absent here" ], "echoes": [] }, "paired_incident": null, "headline": "Quartz Solar goes live at NESO and may avoid £30m a year in costs", "metric": "at least £30m a year in avoided imbalance costs", "comparator": { "against": "NESO's solar forecasts before Quartz Solar was adopted", "before": "forecasting errors before adoption (not quantified)", "after": "halved 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year, with potential to increase to GBP 150 million ($197.0 million) per year in 2035 if government solar capacity targets are met.", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/" }, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version, the value as stated from the cited sources.", "source": 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OCF's AI-powered solar PV forecasting tool combines machine learning, live satellite imagery and weather data. It is fully integrated from day-ahead scheduling to real-time control after starting as a Network Innovation Allowance project (The Energyst, 6 November 2025). The announcement says the tool helps avoid at least £30 million in imbalance costs a year and cuts around 300,000 tonnes of CO2. NESO's Carolina Tortora, Head of AI, Digitalisation and Innovation, is quoted saying it can accurately predict solar generation up to 36 hours in advance. She says it is revolutionizing solar forecasts in the control room. She does not state the £30m figure. pv magazine (7 November 2025) attributes the £30 million (US$39 million) per year saving to Quartz Solar itself. It reports OCF's claim of halving forecasting errors across about 300 grid supply points. OCF's website says the forecast is used daily by NESO. The savings are the maker's estimate, not an operator-reported or audited figure.", "replication": { "produced_by": [ "Satellite imagery, numerical weather data and historical generation data for a national PV fleet", "A system operator innovation program (NIA) that funded testing before operational integration", "A forecast refresh cycle faster than the operator's existing six-hourly weather updates" ], "must_hold": [ "Access to live satellite and generation data", "An operator willing to integrate a third-party forecast into reserve setting", "Independent measurement of imbalance-cost changes, which is absent here" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 37, "reviewed": 0, "record_mapped": 37, "not_assessed": 54, "complete": false, "by_state": { "value_present": 37, "not_reported": 0, "unresolved": 0, "not_assessed": 54, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0238", "case_url": "records/AIBD-0238.html", "reason": "Detection or repair of software and network vulnerabilities.", "details": [ { "label": "Focus", "value": "Cybersecurity" }, { "label": "Purpose", "value": "Autonomous AI systems that find and patch vulnerabilities in open-source software underlying critical infrastructure." }, { "label": "AI contribution", "value": "DARPA, the organizer, reports the figures. The Record corroborates from the event. Stenberg's independent look at the curl tasks suggests the injected flaws were easy. That bounds how much the synthetic scores show." } ], "filters": { "topic": "Cybersecurity", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0238", "title": "DARPA's AIxCC final: autonomous systems find 54 of 63 synthetic vulnerabilities, patch 43, and surface 18 real bugs in open-source code", "summary": "At DEF CON 33 (8 August 2025) DARPA reported seven teams' systems found 54 synthetic vulnerabilities, patched 43, and found 18 real bugs for disclosure; Team Atlanta won $4 million.", "date_observed": "2025-08-08", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "DARPA's results release describes the Final Competition. Teams' cyber reasoning systems tried to find and patch synthetic vulnerabilities across 54 million lines of real-world open-source code. They found 54 unique synthetic vulnerabilities in 63 challenges and patched 43. They also found 18 real, non-synthetic vulnerabilities (six in C codebases, 12 in Java), now being responsibly disclosed. There were 11 patches for real bugs. The average cost was about $152 per task. Team Atlanta (Georgia Tech, Samsung Research, KAIST, POSTECH) won $4 million, Trail of Bits $3 million and Theori $1.5 million. Four of seven systems were released open source. The Record reported the same figures from Las Vegas with program manager Andrew Carney's remarks. curl maintainer Daniel Stenberg, whose project was among the targets, examined the injected curl flaws. He judged them straightforward and not deeply hidden.", "category": "general benefit", "unexpected": false, "purpose": "Autonomous AI systems that find and patch vulnerabilities in open-source software underlying critical infrastructure.", "system": "AIxCC finalist cyber reasoning systems (Team Atlanta, Trail of Bits, Theori and four others)", "model_version": null, "developer": "Seven finalist teams (Team Atlanta et al.), using LLM credits from Anthropic, Google and OpenAI", "deployer": "DARPA (with ARPA-H), AIxCC Final Competition", "location": "Las Vegas, United States", "domain": "Cybersecurity / automated vulnerability discovery and patching", "segment": "Government", "benefit_type": "Reduced Risk", "taxonomy_note": "A competition result; the real bugs found are read as risk reduced.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "Aggregate competition results. 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That bounds how much the synthetic scores show." }, "evidence": [ { "kind": "press release", "citation": "DARPA, news release, 8 Aug 2025", "title": "AI Cyber Challenge marks pivotal inflection point for cyber defense", "url": "https://www.darpa.mil/news/2025/aixcc-results", "date": "2025-08-08", "written_by": "deployer", "quote": "In the Final Competition, teams also discovered 18 real, non-synthetic vulnerabilities that are being responsibly disclosed to open source project maintainers." }, { "kind": "news report", "citation": "Jonathan Greig, The Record from Recorded Future News, 8 Aug 2025", "title": "DARPA announces $4 million winner of AI code review competition at DEF CON", "url": "https://therecord.media/darpa-ai-code-competition-winner-def-con", "date": "2025-08-08", "written_by": "independent", "quote": "Overall, competitors found 54 unique synthetic vulnerabilities and were able to patch 43 of them — representing 77% of the synthetic vulnerabilities introduced." }, { "kind": "report", "citation": "Daniel Stenberg, daniel.haxx.se, 22 Oct 2025", "title": "AIxCC curl details", "url": "https://daniel.haxx.se/blog/2025/10/22/aixcc-curl-details/", "date": "2025-10-22", "written_by": "beneficiary", "quote": "As seen above here, they were all rather straight forward and not hidden or deeply layered very much." } ], "replication": { "produced_by": [ "Injected flaws in real open-source projects with scoring for proofs and patches", "Two years of funded development and frontier-lab model credits" ], "must_hold": [ "Real-world codebases with harder, layered flaws than the injected ones", "Maintainer acceptance of machine-generated patches" ], "echoes": [] }, "paired_incident": null, "headline": "AIxCC systems find 54 of 63 synthetic vulnerabilities, patch 43", "metric": "found 54 synthetic vulnerabilities, patched 43, and found 18 real bugs for disclosure", "comparator": { "against": "the same competition's semifinal round (2024)", "before": "37% at semifinals", "after": "teams identified 86% of the competition's synthetic vulnerabilities", "basis": "measured", "quote": "In the Final Competition scored round, teams identified 86% of the competition's synthetic vulnerabilities, an increase from 37% at semifinals, and patched 68% of the vulnerabilities identified, an increase from 25% at semifinals.", "url": "https://www.darpa.mil/news/2025/aixcc-results" }, "later": { "status": "unknown", "date": null, "note": "The only later item found is curl maintainer Daniel Stenberg's 22 October 2025 review judging the injected curl flaws straightforward; no adoption, reversal or litigation was found.", "quote": null, "url": null }, "value": { "amount": "$152", "what": "average cost per competition task for the systems' bug reports and patches, which DARPA calls a fraction of the cost of traditional methods", "who": "DARPA (cost of running the competitors' cyber reasoning systems per task)", "estimate": false, "quote": "Competitor CRSs proved they can create valuable bug reports and patches for a fraction of the cost of traditional methods, with an average cost per competition task of about $152.", "url": "https://www.darpa.mil/news/2025/aixcc-results" }, "developer_country": null, "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://www.darpa.mil/news/2025/aixcc-results; https://www.darpa.mil/news/2025/aixcc-results", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/DARPA", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the deployer's press release and the beneficiary's report. 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https://www.darpa.mil/news/2025/aixcc-results", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/DARPA", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Injected flaws in real open-source projects with scoring for proofs and patches", "Two years of funded development and frontier-lab model credits" ], "must_hold": [ "Real-world codebases with harder, layered flaws than the injected ones", "Maintainer acceptance of machine-generated patches" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "At DEF CON 33 (8 August 2025) DARPA reported seven teams' systems found 54 synthetic vulnerabilities, patched 43, and found 18 real bugs for disclosure; Team Atlanta won $4 million.", "account": "DARPA's results release describes the Final Competition. Teams' cyber reasoning systems tried to find and patch synthetic vulnerabilities across 54 million lines of real-world open-source code. They found 54 unique synthetic vulnerabilities in 63 challenges and patched 43. They also found 18 real, non-synthetic vulnerabilities (six in C codebases, 12 in Java), now being responsibly disclosed. There were 11 patches for real bugs. The average cost was about $152 per task. Team Atlanta (Georgia Tech, Samsung Research, KAIST, POSTECH) won $4 million, Trail of Bits $3 million and Theori $1.5 million. Four of seven systems were released open source. The Record reported the same figures from Las Vegas with program manager Andrew Carney's remarks. curl maintainer Daniel Stenberg, whose project was among the targets, examined the injected curl flaws. He judged them straightforward and not deeply hidden." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AIxCC finalist cyber reasoning systems (Team Atlanta, Trail of Bits, Theori and four others)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Seven finalist teams (Team Atlanta et al.), using LLM credits from Anthropic, Google and OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "DARPA's AIxCC final: autonomous systems find 54 of 63 synthetic vulnerabilities, patch 43, and surface 18 real bugs in open-source code", "headline": "AIxCC systems find 54 of 63 synthetic vulnerabilities, patch 43" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "01edde86def16471a3fe4b3c89e5f67489b28fa09b20437cf77586aebe710a73" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "DARPA's results release describes the Final Competition. Teams' cyber reasoning systems tried to find and patch synthetic vulnerabilities across 54 million lines of real-world open-source code. They found 54 unique synthetic vulnerabilities in 63 challenges and patched 43. They also found 18 real, non-synthetic vulnerabilities (six in C codebases, 12 in Java), now being responsibly disclosed. There were 11 patches for real bugs. The average cost was about $152 per task. Team Atlanta (Georgia Tech, Samsung Research, KAIST, POSTECH) won $4 million, Trail of Bits $3 million and Theori $1.5 million. Four of seven systems were released open source. The Record reported the same figures from Las Vegas with program manager Andrew Carney's remarks. curl maintainer Daniel Stenberg, whose project was among the targets, examined the injected curl flaws. He judged them straightforward and not deeply hidden.", "replication": { "produced_by": [ "Injected flaws in real open-source projects with scoring for proofs and patches", "Two years of funded development and frontier-lab model credits" ], "must_hold": [ "Real-world codebases with harder, layered flaws than the injected ones", "Maintainer acceptance of machine-generated patches" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0178", "case_url": "records/AIBD-0178.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Analyze, identify and weed out fake, forged and non-bonafide mobile connections to curb cyber crime." }, { "label": "AI contribution", "value": "DoT describes ASTR as an indigenous AI and big-data analytic tool. It uses facial recognition to find connections taken by the same person under different names. Disconnection follows a failed re-verification step, so the tool flags connections rather than deciding. Developer and deployer are the same public body (DoT), which wrote the PIB releases." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0178", "title": "India's DoT disconnects over 82 lakh mobile connections flagged by its AI facial-recognition tool ASTR", "summary": "DoT's indigenous ASTR tool matches subscriber photos to find connections taken by one person under different names; a Rajya Sabha reply (August 2025) counts more than 82 lakh disconnected after fai...", "date_observed": "2025-08-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Department of Telecommunications (DoT), Government of India, states that it designed and implemented ASTR. ASTR, the “AI and facial recognition powered Solution for Telecom SIM Subscriber Verification”, is to identify and weed out fake, forged and non-bonafide mobile connections. A PIB Chennai release of 8 May 2023 reported that ASTR identified more than 50,000 non-bonafide connections in Tamil Nadu. All were disconnected by operators, with point-of-sale details handed to the Tamil Nadu Police Cyber Crime Wing. The Hindu (PTI, 28 May 2023) quoted a senior DoT official in Patna. The official said that ASTR analysis found at least 21,800 connections exceeding the nine-per-subscriber limit in Bihar and Jharkhand. Around 17,000 of them were disconnected, and operators blacklisted 2,900 points of sale. Minister of State Dr Pemmasani Chandra Sekhar gave a Rajya Sabha written reply, posted by PIB on 7 August 2025. He said that DoT disconnected more than 82 lakh connections through ASTR after they failed re-verification. The OECD AI Incidents Monitor records the Bihar and Jharkhand deactivations as an AI Incident citing privacy, misidentification and data-protection concerns. The cumulative figure carries the reply's posting date. No independent accuracy evaluation was found.", "category": "general benefit", "unexpected": false, "purpose": "Analyze, identify and weed out fake, forged and non-bonafide mobile connections to curb cyber crime.", "system": "ASTR, AI and facial recognition powered Solution for Telecom SIM Subscriber Verification", "model_version": null, "developer": "Department of Telecommunications, Government of India", "deployer": "Department of Telecommunications with telecom service providers", "location": null, "domain": "Telecom-enabled cyber financial fraud", "segment": "SIM subscriber verification", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "Disconnection counts are aggregates. No measured reduction in fraud losses is documented." }, { "group": "organizational", "depth": null, "reason": "DoT and operators report disconnections and blacklisting without an evaluated outcome." } ], "ai_role": { "status": "assessed", "statement": "DoT describes ASTR as an indigenous AI and big-data analytic tool. It uses facial recognition to find connections taken by the same person under different names. Disconnection follows a failed re-verification step, so the tool flags connections rather than deciding. Developer and deployer are the same public body (DoT), which wrote the PIB releases." }, "evidence": [ { "kind": "press release", "citation": "Press Information Bureau, Ministry of Communications, written reply in Rajya Sabha by Dr Pemmasani Chandra Sekhar, 7 August 2025", "title": "Telecom-Related Cyber Frauds and Safety Measures", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2153524", "date": "2025-08-07", "written_by": "makers", "quote": "disconnected more than 82 lakhs such connections through ASTR after they failed reverification" }, { "kind": "press release", "citation": "Press Information Bureau Chennai, release issued by M. Venaiah, Director (Admin), O/o Special Director General Telecom, 8 May 2023", "title": "Department of Telecommunications Initiatives for curbing Cyber Crime", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=1922542", "date": "2023-05-08", "written_by": "makers", "quote": "ASTR has identified more than 50,000 non-bonafide mobile connections issued by Telecom Service Providers (TSP) in the State of Tamilnadu." }, { "kind": "news report", "citation": "PTI via The Hindu, Patna, 28 May 2023", "title": "17,000 extra SIM cards of subscribers beyond 9 connections deactivated in Bihar and Jharkhand", "url": "https://www.thehindu.com/news/national/17000-extra-sim-cards-of-subscribers-beyond-9-connections-deactivated-in-bihar-and-jharkhand/article66903692.ece", "date": "2023-05-28", "written_by": "independent", "quote": "Around 17,000 such numbers have been disconnected. Further analysis is going on to identify such numbers," }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, entry 2023-05-28-a3cc, classified AI Incident", "title": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns", "url": "https://oecd.ai/en/incidents/2023-05-28-a3cc", "date": "2023-05-28", "written_by": "independent", "quote": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns" }, { "kind": "news report", "citation": "IANS via The Hans India (2026). Govt disconnects over 82 lakh fraudulent mobile connections via ASTR. The Hans India, 9 October 2026.", "title": "Govt disconnects over 82 lakh fraudulent mobile connections via ASTR", "url": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "date": "2026-10-09", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Subscriber KYC photographs held by operators, matched across names by facial recognition", "A legal cap of nine connections per subscriber defining violations" ], "must_hold": [ "A re-verification step whose failure triggers disconnection", "Point-of-sale accountability and police follow-up", "Safeguards against misidentification, which the OECD AI Incidents Monitor entry flags as a concern" ], "echoes": [ { "date": "2023-05-08", "setting": "Tamil Nadu", "model_version": null, "outcome": "recurred", "source": "PIB Chennai: more than 50,000 connections identified and disconnected" }, { "date": "2023-05-28", "setting": "Bihar and Jharkhand", "model_version": null, "outcome": "recurred", "source": "The Hindu/PTI: around 17,000 of 21,800 disconnected" }, { "date": "2026-10-09", "setting": "Statement by MoS Pemmasani reported by The Statesman", "model_version": null, "outcome": "unknown", "source": "Google News RSS headline only; article returned 403" } ] }, "paired_incident": null, "headline": "India's DoT disconnects over 82 lakh connections flagged by ASTR", "metric": "disconnected more than 82 lakh connections through ASTR after they failed re-verification", "comparator": null, "later": { "status": "persisted", "date": "2026-10-09", "note": "Minister of State Pemmasani restated at the India Mobile Congress that over 82 lakh fraudulent connections had been disconnected through ASTR.", "quote": "India has disconnected over 82 lakh fraudulent mobile connections through the Artificial Intelligence and Facial Recognition powered Telecom SIM Subscriber Verification (ASTR) tool, Union Minister of State for Communications and Rural Development Dr Chandra Sekhar Pemmasani said on Friday.", "url": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704" }, "value": null, "developer_country": "India", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Department_of_Telecommunications; https://en.wikipedia.org/wiki/Department_of_Telecommunications", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Department of Telecommunications.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, a registry entry and two press releases from the makers. 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ASTR.", "quote": "India has disconnected over 82 lakh fraudulent mobile connections through the Artificial Intelligence and Facial Recognition powered Telecom SIM Subscriber Verification (ASTR) tool, Union Minister of State for Communications and Rural Development Dr Chandra Sekhar Pemmasani said on Friday.", "url": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0178" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources 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Telecommunications (DoT), Government of India, states that it designed and implemented ASTR. ASTR, the “AI and facial recognition powered Solution for Telecom SIM Subscriber Verification”, is to identify and weed out fake, forged and non-bonafide mobile connections. A PIB Chennai release of 8 May 2023 reported that ASTR identified more than 50,000 non-bonafide connections in Tamil Nadu. All were disconnected by operators, with point-of-sale details handed to the Tamil Nadu Police Cyber Crime Wing. The Hindu (PTI, 28 May 2023) quoted a senior DoT official in Patna. The official said that ASTR analysis found at least 21,800 connections exceeding the nine-per-subscriber limit in Bihar and Jharkhand. Around 17,000 of them were disconnected, and operators blacklisted 2,900 points of sale. Minister of State Dr Pemmasani Chandra Sekhar gave a Rajya Sabha written reply, posted by PIB on 7 August 2025. He said that DoT disconnected more than 82 lakh connections through ASTR after they failed re-verification. The OECD AI Incidents Monitor records the Bihar and Jharkhand deactivations as an AI Incident citing privacy, misidentification and data-protection concerns. The cumulative figure carries the reply's posting date. No independent accuracy evaluation was found.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "Disconnection counts are aggregates. 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external sources were not rechecked.", "record_fields": [ "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Analyze, identify and weed out fake, forged and non-bonafide mobile connections to curb cyber crime." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-08-07", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Department_of_Telecommunications; https://en.wikipedia.org/wiki/Department_of_Telecommunications", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Department of Telecommunications.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Subscriber KYC photographs held by operators, matched across names by facial recognition", "A legal cap of nine connections per subscriber defining violations" ], "must_hold": [ "A re-verification step whose failure triggers disconnection", "Point-of-sale accountability and police follow-up", "Safeguards against misidentification, which the OECD AI Incidents Monitor entry flags as a concern" ], "echoes": [ { "date": "2023-05-08", "setting": "Tamil Nadu", "outcome": "recurred", "source": "PIB Chennai: more than 50,000 connections identified and disconnected" }, { "date": "2023-05-28", "setting": "Bihar and Jharkhand", "outcome": "recurred", "source": "The Hindu/PTI: around 17,000 of 21,800 disconnected" }, { "date": "2026-10-09", "setting": "Statement by MoS Pemmasani reported by The Statesman", "outcome": "unknown", "source": "Google News RSS headline only; article returned 403" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Subscriber KYC photographs held by operators, matched across names by facial recognition", "A legal cap of nine connections per subscriber defining violations" ], "must_hold": [ "A re-verification step whose failure triggers disconnection", "Point-of-sale accountability and police follow-up", "Safeguards against misidentification, which the OECD AI Incidents Monitor entry flags as a concern" ], "echoes": [ { "date": "2023-05-08", "setting": "Tamil Nadu", "outcome": "recurred", "source": "PIB Chennai: more than 50,000 connections identified and disconnected" }, { "date": "2023-05-28", "setting": "Bihar and Jharkhand", "outcome": "recurred", "source": "The Hindu/PTI: around 17,000 of 21,800 disconnected" }, { "date": "2026-10-09", "setting": "Statement by MoS Pemmasani reported by The Statesman", "outcome": "unknown", "source": "Google News RSS headline only; article returned 403" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Department_of_Telecommunications; https://en.wikipedia.org/wiki/Department_of_Telecommunications", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Department of Telecommunications.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Telecom-enabled cyber financial fraud", "segment": "SIM subscriber verification" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Press Information Bureau, Ministry of Communications, written reply in Rajya Sabha by Dr Pemmasani Chandra Sekhar, 7 August 2025", "title": "Telecom-Related Cyber Frauds and Safety Measures", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2153524", "date": "2025-08-07", "written_by": "makers", "quote": "disconnected more than 82 lakhs such connections through ASTR after they failed reverification" }, { "kind": "press release", "citation": "Press Information Bureau Chennai, release issued by M. Venaiah, Director (Admin), O/o Special Director General Telecom, 8 May 2023", "title": "Department of Telecommunications Initiatives for curbing Cyber Crime", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=1922542", "date": "2023-05-08", "written_by": "makers", "quote": "ASTR has identified more than 50,000 non-bonafide mobile connections issued by Telecom Service Providers (TSP) in the State of Tamilnadu." }, { "kind": "news report", "citation": "PTI via The Hindu, Patna, 28 May 2023", "title": "17,000 extra SIM cards of subscribers beyond 9 connections deactivated in Bihar and Jharkhand", "url": "https://www.thehindu.com/news/national/17000-extra-sim-cards-of-subscribers-beyond-9-connections-deactivated-in-bihar-and-jharkhand/article66903692.ece", "date": "2023-05-28", "written_by": "independent", "quote": "Around 17,000 such numbers have been disconnected. Further analysis is going on to identify such numbers," }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, entry 2023-05-28-a3cc, classified AI Incident", "title": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns", "url": "https://oecd.ai/en/incidents/2023-05-28-a3cc", "date": "2023-05-28", "written_by": "independent", "quote": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns" }, { "kind": "news report", "citation": "IANS via The Hans India (2026). Govt disconnects over 82 lakh fraudulent mobile connections via ASTR. The Hans India, 9 October 2026.", "title": "Govt disconnects over 82 lakh fraudulent mobile connections via ASTR", "url": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "date": "2026-10-09", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0178-source-0001", "AIBD-0178-source-0002", "AIBD-0178-source-0003", "AIBD-0178-source-0004", "AIBD-0178-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Press Information Bureau, Ministry of Communications, written reply in Rajya Sabha by Dr Pemmasani Chandra Sekhar, 7 August 2025", "title": "Telecom-Related Cyber Frauds and Safety Measures", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=2153524", "date": "2025-08-07", "written_by": "makers", "quote": "disconnected more than 82 lakhs such connections through ASTR after they failed reverification" }, { "kind": "press release", "citation": "Press Information Bureau Chennai, release issued by M. Venaiah, Director (Admin), O/o Special Director General Telecom, 8 May 2023", "title": "Department of Telecommunications Initiatives for curbing Cyber Crime", "url": "https://www.pib.gov.in/PressReleasePage.aspx?PRID=1922542", "date": "2023-05-08", "written_by": "makers", "quote": "ASTR has identified more than 50,000 non-bonafide mobile connections issued by Telecom Service Providers (TSP) in the State of Tamilnadu." }, { "kind": "news report", "citation": "PTI via The Hindu, Patna, 28 May 2023", "title": "17,000 extra SIM cards of subscribers beyond 9 connections deactivated in Bihar and Jharkhand", "url": "https://www.thehindu.com/news/national/17000-extra-sim-cards-of-subscribers-beyond-9-connections-deactivated-in-bihar-and-jharkhand/article66903692.ece", "date": "2023-05-28", "written_by": "independent", "quote": "Around 17,000 such numbers have been disconnected. Further analysis is going on to identify such numbers," }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, entry 2023-05-28-a3cc, classified AI Incident", "title": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns", "url": "https://oecd.ai/en/incidents/2023-05-28-a3cc", "date": "2023-05-28", "written_by": "independent", "quote": "AI Facial Recognition Leads to Mass SIM Card Deactivation in India Over Fraud Concerns" }, { "kind": "news report", "citation": "IANS via The Hans India (2026). Govt disconnects over 82 lakh fraudulent mobile connections via ASTR. The Hans India, 9 October 2026.", "title": "Govt disconnects over 82 lakh fraudulent mobile connections via ASTR", "url": "https://www.thehansindia.com/business/govt-disconnects-over-82-lakh-fraudulent-mobile-connections-via-astr-1130704", "date": "2026-10-09", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "DoT's indigenous ASTR tool matches subscriber photos to find connections taken by one person under different names; a Rajya Sabha reply (August 2025) counts more than 82 lakh disconnected after fai...", "account": "The Department of Telecommunications (DoT), Government of India, states that it designed and implemented ASTR. ASTR, the “AI and facial recognition powered Solution for Telecom SIM Subscriber Verification”, is to identify and weed out fake, forged and non-bonafide mobile connections. A PIB Chennai release of 8 May 2023 reported that ASTR identified more than 50,000 non-bonafide connections in Tamil Nadu. All were disconnected by operators, with point-of-sale details handed to the Tamil Nadu Police Cyber Crime Wing. The Hindu (PTI, 28 May 2023) quoted a senior DoT official in Patna. The official said that ASTR analysis found at least 21,800 connections exceeding the nine-per-subscriber limit in Bihar and Jharkhand. Around 17,000 of them were disconnected, and operators blacklisted 2,900 points of sale. Minister of State Dr Pemmasani Chandra Sekhar gave a Rajya Sabha written reply, posted by PIB on 7 August 2025. He said that DoT disconnected more than 82 lakh connections through ASTR after they failed re-verification. The OECD AI Incidents Monitor records the Bihar and Jharkhand deactivations as an AI Incident citing privacy, misidentification and data-protection concerns. The cumulative figure carries the reply's posting date. No independent accuracy evaluation was found." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ASTR, AI and facial recognition powered Solution for Telecom SIM Subscriber Verification" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Department of Telecommunications, Government of India" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "India's DoT disconnects over 82 lakh mobile connections flagged by its AI facial-recognition tool ASTR", "headline": "India's DoT disconnects over 82 lakh connections flagged by ASTR" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0ee3171393b93d69f5590e3a237ee805a460dd658fb47df72b475010d5019a9c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Department of Telecommunications (DoT), Government of India, states that it designed and implemented ASTR. ASTR, the “AI and facial recognition powered Solution for Telecom SIM Subscriber Verification”, is to identify and weed out fake, forged and non-bonafide mobile connections. A PIB Chennai release of 8 May 2023 reported that ASTR identified more than 50,000 non-bonafide connections in Tamil Nadu. All were disconnected by operators, with point-of-sale details handed to the Tamil Nadu Police Cyber Crime Wing. The Hindu (PTI, 28 May 2023) quoted a senior DoT official in Patna. The official said that ASTR analysis found at least 21,800 connections exceeding the nine-per-subscriber limit in Bihar and Jharkhand. Around 17,000 of them were disconnected, and operators blacklisted 2,900 points of sale. Minister of State Dr Pemmasani Chandra Sekhar gave a Rajya Sabha written reply, posted by PIB on 7 August 2025. He said that DoT disconnected more than 82 lakh connections through ASTR after they failed re-verification. The OECD AI Incidents Monitor records the Bihar and Jharkhand deactivations as an AI Incident citing privacy, misidentification and data-protection concerns. The cumulative figure carries the reply's posting date. No independent accuracy evaluation was found.", "replication": { "produced_by": [ "Subscriber KYC photographs held by operators, matched across names by facial recognition", "A legal cap of nine connections per subscriber defining violations" ], "must_hold": [ "A re-verification step whose failure triggers disconnection", "Point-of-sale accountability and police follow-up", "Safeguards against misidentification, which the OECD AI Incidents Monitor entry flags as a concern" ], "echoes": [ { "date": "2023-05-08", "setting": "Tamil Nadu", "outcome": "recurred", "source": "PIB Chennai: more than 50,000 connections identified and disconnected" }, { "date": "2023-05-28", "setting": "Bihar and Jharkhand", "outcome": "recurred", "source": "The Hindu/PTI: around 17,000 of 21,800 disconnected" }, { "date": "2026-10-09", "setting": "Statement by MoS Pemmasani reported by The Statesman", "outcome": "unknown", "source": "Google News RSS headline only; article returned 403" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0383", "case_url": "records/AIBD-0383.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Built to shoot down Shahed-type attack drones and other small air targets automatically. The reported interceptions are that purpose." }, { "label": "AI contribution", "value": "Per UNITED24 Media and Forbes via Euromaidan Press, the AI does the whole engagement. It takes radar data, distinguishes drones from birds and tracks the flight path. It computes the shot with wind and target speed, and fires conventional rounds. The four kills are the makers' claim relayed by the fundraiser. No independent confirmation, footage date or location is given. The Monitor's human-authorization step does not appear in the read reports." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0383", "title": "Sky Sentinel, an AI-controlled machine-gun turret built by Ukrainian engineers, is reported to have downed four Shahed drones with one prototype; UNITED24 raises $1.5 million for 10 units", "summary": "Ukraine's Sky Sentinel, an AI-controlled heavy-machine-gun turret, is reported to have downed four Russian Shahed drones with one prototype; dates and places are withheld (May-June 2025).", "date_observed": "2025-05-26", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Sky Sentinel is an autonomous turret, a heavy machine gun on a 360-degree mount. Its software, written by Ukrainian engineers, takes radar input and separates drones from birds. It tracks the target, computes the ballistic solution and fires without a human. UNITED24 Media, the outlet of the UNITED24 fundraising platform, reported on 26 May 2025 that one prototype had downed four Shahed drones at the front. Dates and places were withheld for security. Its $1.5 million campaign for 10 turrets at about $150,000 each was complete. Forbes (David Hambling, 27 May 2025), as relayed by Euromaidan Press on 28 May 2025, repeats the four kills. It gives a pointing precision of 17 micro-radians. The Defense Post (3 June 2025) adds that 10 to 30 units would be needed to protect a city. It adds that production is scaling to dozens a month. The Monitor's statement that a human authorizes each shot is not in any read report.", "category": "general benefit", "unexpected": false, "purpose": "Built to shoot down Shahed-type attack drones and other small air targets automatically. The reported interceptions are that purpose.", "system": "Sky Sentinel AI-controlled anti-drone turret (heavy machine gun, radar-cued)", "model_version": "prototype", "developer": "Ukrainian engineering team (not named in the reports)", "deployer": "Ukrainian Defense Forces (unit not named)", "location": null, "domain": "Air defense / counter-drone", "segment": "Shahed-type one-way attack drones", "benefit_type": "Reduced Violence", "taxonomy_note": "Trustible has no military-defense type; the reported good is attack drones destroyed before reaching their targets, counted here as weapons removed, the nearest fit to Reduced Violence.", "registry_ids": { "oecd_aim": "2025-06-02-0176" }, "ratings": [ { "group": "societal", "depth": null, "reason": "Four shoot-downs are reported by the fundraising platform with no dates, places or targets. No protected population or averted strike is therefore documented." }, { "group": "organizational", "depth": null, "reason": "The reports do not identify the unit that fielded the prototype or any result beyond the kill count." } ], "ai_role": { "status": "assessed", "statement": "Per UNITED24 Media and Forbes via Euromaidan Press, the AI does the whole engagement. It takes radar data, distinguishes drones from birds and tracks the flight path. It computes the shot with wind and target speed, and fires conventional rounds. The four kills are the makers' claim relayed by the fundraiser. No independent confirmation, footage date or location is given. The Monitor's human-authorization step does not appear in the read reports." }, "evidence": [ { "kind": "news report", "citation": "Kabachynskyi, I. (2025). Ukraine Develops New AI-controlled Sky Sentinel Turret to Combat Russian Drone Threats. UNITED24 Media, May 26, 2025.", "title": "Ukraine Develops New AI-controlled Sky Sentinel Turret to Combat Russian Drone Threats", "url": "https://united24media.com/war-in-ukraine/ai-powered-turret-that-hunts-russian-drones-meet-sky-sentinel-ukraines-new-air-defense-8589", "date": "2025-05-26", "written_by": "deployer", "quote": "We had to build a system that moves a lot with zero mechanical play." }, { "kind": "news report", "citation": "Tril, M. (2025). Forbes: Ukraine deploys AI turrets against record Russian drone attacks. Euromaidan Press, May 28, 2025.", "title": "Forbes: Ukraine deploys AI turrets against record Russian drone attacks", "url": "https://euromaidanpress.com/2025/05/28/forbes-ukraine-deploys-ai-turrets-against-record-russian-drone-attacks/", "date": "2025-05-28", "written_by": "independent", "quote": "It never needs rest, never gets tired, and reacts instantly and precisely to incoming threats at any time day or night" }, { "kind": "news report", "citation": "Bisht, I. S. (2025). Ukraine Develops AI-Run Turret to Take Down Erratic Shahed Drones. The Defense Post, June 3, 2025.", "title": "Ukraine Develops AI-Run Turret to Take Down Erratic Shahed Drones", "url": "https://thedefensepost.com/2025/06/03/ukraine-ai-shahed-drones", "date": "2025-06-03", "written_by": "independent", "quote": "detects, locks on, tracks the flight paths, calculates the shot, and fires. All on its own" }, { "kind": "registry label", "citation": "OECD.AI (2025). Ukraine Deploys AI-Powered Sky Sentinel Turret in Drone Combat. 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Ukraine Deploys AI-Powered Sky Sentinel Turret in Drone Combat. OECD AI Incidents and Hazards Monitor, 2025-06-02.", "title": "Ukraine Deploys AI-Powered Sky Sentinel Turret in Drone Combat", "url": "https://oecd.ai/en/incidents/2025-06-02-0176", "date": "2025-06-02", "written_by": "independent", "quote": "has autonomously downed several Russian Shahed drones in active combat" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0383-source-0001", "AIBD-0383-source-0002", "AIBD-0383-source-0003", "AIBD-0383-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Kabachynskyi, I. (2025). Ukraine Develops New AI-controlled Sky Sentinel Turret to Combat Russian Drone Threats. UNITED24 Media, May 26, 2025.", "title": "Ukraine Develops New AI-controlled Sky Sentinel Turret to Combat Russian Drone Threats", "url": "https://united24media.com/war-in-ukraine/ai-powered-turret-that-hunts-russian-drones-meet-sky-sentinel-ukraines-new-air-defense-8589", "date": "2025-05-26", "written_by": "deployer", "quote": "We had to build a system that moves a lot with zero mechanical play." }, { "kind": "news report", "citation": "Tril, M. (2025). Forbes: Ukraine deploys AI turrets against record Russian drone attacks. Euromaidan Press, May 28, 2025.", "title": "Forbes: Ukraine deploys AI turrets against record Russian drone attacks", "url": "https://euromaidanpress.com/2025/05/28/forbes-ukraine-deploys-ai-turrets-against-record-russian-drone-attacks/", "date": "2025-05-28", "written_by": "independent", "quote": "It never needs rest, never gets tired, and reacts instantly and precisely to incoming threats at any time day or night" }, { "kind": "news report", "citation": "Bisht, I. S. (2025). Ukraine Develops AI-Run Turret to Take Down Erratic Shahed Drones. The Defense Post, June 3, 2025.", "title": "Ukraine Develops AI-Run Turret to Take Down Erratic Shahed Drones", "url": "https://thedefensepost.com/2025/06/03/ukraine-ai-shahed-drones", "date": "2025-06-03", "written_by": "independent", "quote": "detects, locks on, tracks the flight paths, calculates the shot, and fires. All on its own" }, { "kind": "registry label", "citation": "OECD.AI (2025). Ukraine Deploys AI-Powered Sky Sentinel Turret in Drone Combat. OECD AI Incidents and Hazards Monitor, 2025-06-02.", "title": "Ukraine Deploys AI-Powered Sky Sentinel Turret in Drone Combat", "url": "https://oecd.ai/en/incidents/2025-06-02-0176", "date": "2025-06-02", "written_by": "independent", "quote": "has autonomously downed several Russian Shahed drones in active combat" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Ukraine's Sky Sentinel, an AI-controlled heavy-machine-gun turret, is reported to have downed four Russian Shahed drones with one prototype; dates and places are withheld (May-June 2025).", "account": "Sky Sentinel is an autonomous turret, a heavy machine gun on a 360-degree mount. Its software, written by Ukrainian engineers, takes radar input and separates drones from birds. It tracks the target, computes the ballistic solution and fires without a human. UNITED24 Media, the outlet of the UNITED24 fundraising platform, reported on 26 May 2025 that one prototype had downed four Shahed drones at the front. Dates and places were withheld for security. Its $1.5 million campaign for 10 turrets at about $150,000 each was complete. Forbes (David Hambling, 27 May 2025), as relayed by Euromaidan Press on 28 May 2025, repeats the four kills. It gives a pointing precision of 17 micro-radians. The Defense Post (3 June 2025) adds that 10 to 30 units would be needed to protect a city. It adds that production is scaling to dozens a month. The Monitor's statement that a human authorizes each shot is not in any read report." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The developers reported that the system classified radar detections, tracked drones, and calculated firing corrections." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The project used a prototype." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "prototype" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Sky Sentinel AI-controlled anti-drone turret (heavy machine gun, radar-cued)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Ukrainian engineering team (not named in the reports)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Sky Sentinel, an AI-controlled machine-gun turret built by Ukrainian engineers, is reported to have downed four Shahed drones with one prototype; UNITED24 raises $1.5 million for 10 units", "headline": "Sky Sentinel AI turret may have downed four Shahed drones" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "406751e34e0ddb062d1db3036f93b2c6191624c4a25d0b89f12234ccff7fbaf0" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Sky Sentinel is an autonomous turret, a heavy machine gun on a 360-degree mount. Its software, written by Ukrainian engineers, takes radar input and separates drones from birds. It tracks the target, computes the ballistic solution and fires without a human. UNITED24 Media, the outlet of the UNITED24 fundraising platform, reported on 26 May 2025 that one prototype had downed four Shahed drones at the front. Dates and places were withheld for security. Its $1.5 million campaign for 10 turrets at about $150,000 each was complete. Forbes (David Hambling, 27 May 2025), as relayed by Euromaidan Press on 28 May 2025, repeats the four kills. It gives a pointing precision of 17 micro-radians. The Defense Post (3 June 2025) adds that 10 to 30 units would be needed to protect a city. It adds that production is scaling to dozens a month. The Monitor's statement that a human authorizes each shot is not in any read report.", "replication": { "produced_by": [ "Radar cueing that hands the turret a track on a slow, low-flying drone", "A fixed heavy-machine-gun mount with near-zero mechanical play and fast optics", "Rules that let the system engage without a human in the loop" ], "must_hold": [ "Targets fly within the gun's effective range and are not masked by birds or clutter", "Enough units are fielded to cover a city, which the makers put at 10 to 30" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0326", "case_url": "records/AIBD-0326.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal." }, { "label": "AI contribution", "value": "No source independent of the commentary chain describes X-Guard as an AI system. idrw writes “AI-enhanced jamming decoy” and “AI-enhanced algorithms dynamically adjust signals” without attribution. ET, India TV, LatestLY, Oneindia and TFIPOST repeat the label. The Week gives no source for “AI-enabled”. Rafael's own page was unreadable. Deployment itself is unconfirmed by the IAF, MoD, Dassault or Rafael. The no-loss outcome is contradicted by the CDS. Neither the system's AI character nor any contribution to the engagements can therefore be assessed." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "India", "Pakistan" ] }, "record": { "id": "AIBD-0326", "title": "Rafael's X-Guard towed decoy, labeled “AI-powered”, may have diverted Pakistani missiles from IAF Rafales in Operation Sindoor (7–10 May 2025)", "summary": "Every account traces to ex-USAF pilot Ryan Bodenheimer via idrw.org (6 July 2025). No IAF, MoD, Dassault or Rafael confirmation; CDS Gen Anil Chauhan admitted on 31 May 2025 that India lost jets.", "date_observed": "2025-05-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "category": "harm prevented", "unexpected": false, "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal.", "system": "X-Guard fiber-optic towed decoy (Rafael Advanced Defense Systems), described by Indian outlets as 'AI-powered' with 'AI-enhanced algorithms' adjusting its emissions; no read manufacturer or official source describes it", "model_version": null, "developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'", "deployer": "Indian Air Force (claimed by commentary; unconfirmed by any official source)", "location": "India-Pakistan border airspace, Operation Sindoor, 7-10 May 2025", "domain": "Defense: electronic warfare", "segment": "Airborne towed radar decoys", "benefit_type": "Reduced Violence", "taxonomy_note": "Missiles diverted from aircraft are read as a physically harmful incident averted; no defense type exists.", "registry_ids": { "oecd_aim": "2025-07-07-7754" }, "ratings": [ { "group": "personal", "depth": null, "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "depth": null, "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ], "ai_role": { "status": "unassessed", "statement": "No source independent of the commentary chain describes X-Guard as an AI system. idrw writes “AI-enhanced jamming decoy” and “AI-enhanced algorithms dynamically adjust signals” without attribution. ET, India TV, LatestLY, Oneindia and TFIPOST repeat the label. The Week gives no source for “AI-enabled”. Rafael's own page was unreadable. Deployment itself is unconfirmed by the IAF, MoD, Dassault or Rafael. The no-loss outcome is contradicted by the CDS. Neither the system's AI character nor any contribution to the engagements can therefore be assessed." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "date": null, "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ], "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] }, "paired_incident": null, "headline": "X-Guard decoy may have diverted Pakistani missiles from IAF jets", "metric": null, "comparator": null, "later": { "status": "reversed", "date": "2025-05-31", "note": "India's chief of defence staff admitted on 31 May 2025 that an unspecified number of Indian jets were shot down in the May conflict, which contradicts the outlets' claim that the decoy kept missiles from hitting real aircraft.", "quote": "General Anil Chauhan, India's chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month.", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said" }, "value": null, "developer_country": "Israel", "deployer_country": "India", "unrated_because": "no official confirmation, and the CDS admitted losses", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: seven independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0326", "catalog_version": "1.0.0", "source_fingerprint": "16fad776b788e393ffadc107502a285dd2a9c30163095ec46c10bfe8653abb72", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0326-source-0001", "AIBD-0326-source-0002", "AIBD-0326-source-0003", "AIBD-0326-source-0004", "AIBD-0326-source-0005", "AIBD-0326-source-0006", "AIBD-0326-source-0007", "AIBD-0326-source-0008", "AIBD-0326-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'", "deployer": "Indian Air Force (claimed by commentary; unconfirmed by any official source)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "No source independent of the commentary chain describes X-Guard as an AI system. idrw writes “AI-enhanced jamming decoy” and “AI-enhanced algorithms dynamically adjust signals” without attribution. ET, India TV, LatestLY, Oneindia and TFIPOST repeat the label. The Week gives no source for “AI-enabled”. Rafael's own page was unreadable. Deployment itself is unconfirmed by the IAF, MoD, Dassault or Rafael. The no-loss outcome is contradicted by the CDS. Neither the system's AI character nor any contribution to the engagements can therefore be assessed." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: seven independent news reports, a registry entry and one other independent source. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-07-07-7754" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "reversed", "date": "2025-05-31", "note": "India's chief of defence staff admitted on 31 May 2025 that an unspecified number of Indian jets were shot down in the May conflict, which contradicts the outlets' claim that the decoy kept missiles from hitting real aircraft.", "quote": "General Anil Chauhan, India's chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month.", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0326" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "Missiles diverted from aircraft are read as a physically harmful incident averted; no defense type exists." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No pilot or aircraft is documented as saved by the decoy. The CDS admitted jet losses. The claim rests on one commentator's inference." }, { "group": "organizational", "reason": "No official account of the engagements names X-Guard. India's later move to expedite X-Guard deliveries (idrw search listing, 13 July 2025) is procurement news, not an outcome of this operation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "India-Pakistan border airspace, Operation Sindoor, 7-10 May 2025", "developer_country": "Israel", "deployer_country": "India" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "A towed decoy is built to present a false target to enemy radars and radar-guided missiles. They then engage the decoy rather than the aircraft. The outlets describe a 30 kg body on a 100 m fiber-optic cable emitting a 500 W jamming signal." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-05-07", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: no official confirmation, and the CDS admitted losses.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Rafael_Advanced_Defense_Systems; https://en.wikipedia.org/wiki/Indian_Air_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Defense: electronic warfare", "segment": "Airborne towed radar decoys" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0326-source-0001", "AIBD-0326-source-0002", "AIBD-0326-source-0003", "AIBD-0326-source-0004", "AIBD-0326-source-0005", "AIBD-0326-source-0006", "AIBD-0326-source-0007", "AIBD-0326-source-0008", "AIBD-0326-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-07-07-7754, 'AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor' (AI-generated summary).", "title": "AI-Powered X-Guard Decoy Alters Outcome of Operation Sindoor", "url": "https://oecd.ai/en/incidents/2025-07-07-7754", "date": "2025-07-07", "written_by": "independent", "quote": "This technology prevented enemy missiles from hitting real aircraft, enabling successful Indian airstrikes and avoiding loss of personnel and equipment." }, { "kind": "news report", "citation": "idrw.org (sourced to AFI), 6 July 2025, 'India's X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot' (origin of the claim).", "title": "India’s X-Guard Decoy System was Game-Changing Deception in Operation Sindoor Claims former F-15 Pilot", "url": "https://idrw.org/indias-x-guard-decoy-system-was-game-changing-deception-in-operation-sindoor-claims-former-f-15-pilot/", "date": "2025-07-06", "written_by": "independent", "quote": "crediting the Rafale’s X-Guard AI-enhanced jamming decoy system for outsmarting Pakistan’s air defenses" }, { "kind": "news report", "citation": "The Economic Times (ET Online), 8 July 2025, ''Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale'.", "title": "'Best deception ever seen': US F-16 pilot reveals how a 30 kg device fooled Pakistan into thinking it downed a Rafale", "url": "https://economictimes.indiatimes.com/news/defence/best-deception-ever-seen-us-f-16-pilot-reveals-how-a-30-kg-device-fooled-pakistan-into-thinking-it-downed-a-rafale/articleshow/122313728.cms", "date": "2025-07-08", "written_by": "independent", "quote": "the Indian Air Force (IAF) reportedly used artificial intelligence-powered technology during a four-day military operation with Pakistan in May 2025" }, { "kind": "news report", "citation": "India TV News (ed. Ankit Kumar), 9 July 2025, 'Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained'.", "title": "Operation Sindoor: How India's AI-powered ghost Rafales outfoxed Pakistan | Explained", "url": "https://www.indiatvnews.com/news/india/operation-sindoor-how-indias-ai-powered-ghost-rafales-outfoxed-pakistan-explained-2025-07-09-998147", "date": "2025-07-09", "written_by": "independent", "quote": "A recent Jane's Defence Weekly investigation has revealed that several Pakistani claims of downing Rafales were based on successful hits on towed decoys, not actual aircraft." }, { "kind": "news report", "citation": "LatestLY (Sunny Rodricks), 9 July 2025, 'What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System..'.", "title": "What Is X-Guard? All About Indian Rafale's AI-Powered Towed Decoy System Which Helped Air Force Neutralise Pakistani Air Threats During Operation Sindoor", "url": "https://www.latestly.com/india/news/what-is-x-guard-all-about-indian-rafales-ai-powered-towed-decoy-system-which-helped-air-force-neutralise-pakistani-air-threats-during-operation-sindoor-6982417.html", "date": "2025-07-09", "written_by": "independent", "quote": "it is learned that the Indian Air Force (IAF) allegedly used artificial intelligence-powered technology during Operation Sindoor against Pakistan in May 2025" }, { "kind": "news report", "citation": "TFIPOST (Harishanker R P), 9 July 2025, 'Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot'.", "title": "Rafale Decoy Confused Pakistan Jets During Operation Sindoor, Claims US Veteran Pilot", "url": "https://tfipost.com/2025/07/rafale-decoy-confused-pakistan-jets-during-operation-sindoor-claims-us-veteran-pilot", "date": "2025-07-09", "written_by": "independent", "quote": "Developed by Rafael Advanced Defense Systems, this smart decoy transformed the battlefield" }, { "kind": "news report", "citation": "Oneindia, 9 July 2025, 'AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses'.", "title": "AI-Powered Decoy System Turns The Tide: How India's Rafale Outsmarted Pakistan's Air Defenses", "url": "https://www.oneindia.com/artificial-intelligence/ai-powered-decoy-system-turns-the-tide-how-indias-rafale-outsmarted-pakistans-air-defenses-7795811.html", "date": "2025-07-09", "written_by": "independent", "quote": "false claims of downed Rafales-many of which may have been decoys" }, { "kind": "news report", "citation": "Al Jazeera, 1 June 2025, 'India general admits jet losses in clash with Pakistan: Here's what he said'.", "title": "India general admits jet losses in clash with Pakistan: Here’s what he said", "url": "https://www.aljazeera.com/news/2025/6/1/india-general-admits-jet-losses-in-clash-with-pakistan-heres-what-he-said", "date": "2025-06-01", "written_by": "independent", "quote": "General Anil Chauhan, India’s chief of defence staff, has admitted that an unspecified number of its jets were shot down during its conflict with Pakistan last month." }, { "kind": "other", "citation": "Wikipedia, '2025 India–Pakistan conflict', accessed 20 October 2026 (cites Bloomberg, 31 May 2025, 'India Confirms It Lost Fighter Jets in Recent Pakistan Conflict'; the article does not mention X-Guard).", "title": "2025 India–Pakistan conflict", "url": "https://en.wikipedia.org/wiki/2025_India%E2%80%93Pakistan_conflict", "written_by": "independent", "quote": "On 31 May 2025, India's Chief of Defence Staff General Anil Chauhan admitted that India lost jets, but dismissed Pakistan's claim of downing six fighter jets." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Every account traces to ex-USAF pilot Ryan Bodenheimer via idrw.org (6 July 2025). No IAF, MoD, Dassault or Rafael confirmation; CDS Gen Anil Chauhan admitted on 31 May 2025 that India lost jets.", "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "X-Guard fiber-optic towed decoy (Rafael Advanced Defense Systems), described by Indian outlets as 'AI-powered' with 'AI-enhanced algorithms' adjusting its emissions; no read manufacturer or official source describes it" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rafael Advanced Defense Systems (Israel); several outlets misname it 'Rafale Advanced Defense Systems'" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Rafael's X-Guard towed decoy, labeled “AI-powered”, may have diverted Pakistani missiles from IAF Rafales in Operation Sindoor (7–10 May 2025)", "headline": "X-Guard decoy may have diverted Pakistani missiles from IAF jets" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "16fad776b788e393ffadc107502a285dd2a9c30163095ec46c10bfe8653abb72" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry concerns Operation Sindoor in May 2025. It says the Indian Air Force (IAF) used “the AI-powered X-Guard decoy system on Rafale jets”. It says this “prevented enemy missiles from hitting real aircraft”. The claim originates in an idrw.org post of 6 July 2025, sourced to “AFI”, an outsourced content partner. The post quotes Ryan Bodenheimer, a former US Air Force F-15E and F-16 pilot. He calls the IAF's tactics “the best spoofing and deception we've ever seen” and credits “the Rafale's X-Guard AI-enhanced jamming decoy system”. idrw itself writes that the system “likely played a pivotal role”. It writes that Pakistani claims of downed Rafales were “likely X-Guard decoys” according to Jane's Defence Weekly. The Economic Times (ET) writes on 8 July 2025 that the IAF “reportedly used artificial intelligence-powered technology”. LatestLY (9 Jul) says it “allegedly used” it. India TV (9 Jul) attributes a finding that Pakistani kills were hits on towed decoys to “a recent Jane's Defence Weekly investigation”. No other outlet on record corroborates it, and the Jane's Defence Weekly investigation itself is not among the sources. Oneindia (9 Jul) says false claims of downed Rafales “may have been decoys”. TFIPOST (9 Jul) says the IAF “reportedly deployed” the system and names the maker correctly as Rafael Advanced Defense Systems. ET, India TV, LatestLY, Oneindia and idrw write “Rafale Advanced Defense Systems”. The Week and Business Today are quoted from an earlier reading; their pages were not available afterward. The Week's headline says the IAF “may have used” X-Guard. It describes Bodenheimer as “a military tech digital creator”. Business Today writes “reportedly misdirected”. No IAF, Ministry of Defence (MoD), Dassault or Rafael statement confirms that X-Guard was carried or deployed. Rafael's product page was not available. The claimed outcome (no losses) is contradicted by India's Chief of Defence Staff (CDS), General Anil Chauhan. At the Shangri-La Dialogue on 31 May 2025 he admitted that an unspecified number of Indian jets were shot down. He rejected Pakistan's claim of six (Al Jazeera, 1 June 2025; Wikipedia citing Bloomberg). Operation Sindoor began on 7 May 2025 and lasted four days (India TV). The Monitor's 7 July date is the coverage date.", "replication": { "produced_by": [ "A towed decoy on a fiber-optic cable emitting a jamming signal that mimics the aircraft's radar return and Doppler shift (as the outlets describe it)", "Radar-guided missiles (PL-15E, per the outlets) engaging the decoy rather than the aircraft" ], "must_hold": [ "Official confirmation that X-Guard was carried and deployed by the IAF in the 7–10 May 2025 engagements", "Evidence that missiles struck decoys rather than aircraft. India TV attributes this to Jane's Defence Weekly, but no other outlet corroborates it. It is at odds with the CDS's admission of losses." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0357", "case_url": "records/AIBD-0357.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Automated night-time detection of human and vehicle intrusion at rhino sanctuaries to alert rangers. The claimed benefit is the intended one." }, { "label": "AI contribution", "value": "The sole cited source is one news report (Citizen Digital, 21 September 2024). It describes AI analytics that classify humans, vehicles and animals in thermal video and alert rangers automatically. The statements come from WWF and KWS officials at the handover ceremony. No alert, arrest or prevented poaching event is attributed to the system in any cited source. Its contribution to rhino protection is not established." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Kenya" ] }, "record": { "id": "AIBD-0357", "title": "Kenya Wildlife Service installs AI-enabled FLIR thermal cameras at Solio, Lake Nakuru and Ol Pejeta to send intrusion alerts to rhino rangers", "summary": "KWS installed AI-enabled FLIR thermal cameras at three black rhino sites by Sept 2024 (US State Department and WWF funding); intrusion alerts only; no AI-linked arrest or prevented event documented.", "date_observed": "2024-09-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras.", "category": "general benefit", "unexpected": false, "purpose": "Automated night-time detection of human and vehicle intrusion at rhino sanctuaries to alert rangers. The claimed benefit is the intended one.", "system": "Teledyne FLIR thermal cameras with on-board AI analytics for human, vehicle and animal classification and automated ranger alerts", "model_version": null, "developer": "Teledyne FLIR (camera analytics; developer named only as FLIR in the cited source)", "deployer": "Kenya Wildlife Service with WWF-Kenya, WWF-US and WWF-UK; Solio Game Reserve management", "location": "Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, Kenya", "domain": "Wildlife protection and anti-poaching", "segment": "National wildlife authority and private conservancies", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "Intrusion alerts at rhino sites protect an endangered species, which Trustible's description reaches only as “improved environment”; no interception is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary or ranger outcome is documented." }, { "group": "organizational", "depth": null, "reason": "KWS reports installation and intent, not a detected intrusion or response." }, { "group": "societal", "depth": null, "reason": "Kenya's reduced poaching figures cited in the article are not linked to the cameras." } ], "ai_role": { "status": "assessed", "statement": "The sole cited source is one news report (Citizen Digital, 21 September 2024). It describes AI analytics that classify humans, vehicles and animals in thermal video and alert rangers automatically. The statements come from WWF and KWS officials at the handover ceremony. No alert, arrest or prevented poaching event is attributed to the system in any cited source. Its contribution to rhino protection is not established." }, "evidence": [ { "kind": "news report", "citation": "Citizen Reporter (2024). KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ], "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "date": null, "setting": "Lake Nakuru National Park camera system upgraded under the same program", "model_version": null, "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "date": null, "setting": "Meru National Park named as the next installation site", "model_version": null, "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] }, "paired_incident": null, "headline": "KWS's FLIR cameras send intrusion alerts at three rhino sites", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The article names Meru National Park as the next site; nothing dated after 21 September 2024 was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Kenya", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Teledyne_FLIR; https://en.wikipedia.org/wiki/Kenya_Wildlife_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0357", "catalog_version": "1.0.0", "source_fingerprint": "78575ef230d9f2d3194cbfda50cc133cbbe7be7f6d3e448b16246f06635e3eff", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0357-source-0001", "AIBD-0357-source-0002", "AIBD-0357-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Teledyne FLIR (camera analytics; developer named only as FLIR in the cited source)", "deployer": "Kenya Wildlife Service with WWF-Kenya, WWF-US and WWF-UK; Solio Game Reserve management" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The sole cited source is one news report (Citizen Digital, 21 September 2024). It describes AI analytics that classify humans, vehicles and animals in thermal video and alert rangers automatically. The statements come from WWF and KWS officials at the handover ceremony. No alert, arrest or prevented poaching event is attributed to the system in any cited source. Its contribution to rhino protection is not established." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The sole cited source is one news report (Citizen Digital, 21 September 2024). It describes AI analytics that classify humans, vehicles and animals in thermal video and alert rangers automatically. The statements come from WWF and KWS officials at the handover ceremony. No alert, arrest or prevented poaching event is attributed to the system in any cited source. Its contribution to rhino protection is not established." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary or ranger outcome is documented." }, { "group": "organizational", "reason": "KWS reports installation and intent, not a detected intrusion or response." }, { "group": "societal", "reason": "Kenya's reduced poaching figures cited in the article are not linked to the cameras." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary or ranger outcome is documented." }, { "group": "organizational", "reason": "KWS reports installation and intent, not a detected intrusion or response." }, { "group": "societal", "reason": "Kenya's reduced poaching figures cited in the article are not linked to the cameras." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": 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"family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The article names Meru National Park as the next site; nothing dated after 21 September 2024 was found in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0357" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": "Intrusion alerts at rhino sites protect an endangered species, which Trustible's description reaches only as “improved environment”; no interception is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras.", "benefit_type": "Improved Environmental Sustainability" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary or ranger outcome is documented." }, { "group": "organizational", "reason": "KWS reports installation and intent, not a detected intrusion or response." }, { "group": "societal", "reason": "Kenya's reduced poaching figures cited in the article are not linked to the cameras." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Automated night-time detection of human and vehicle intrusion at rhino sanctuaries to alert rangers. The claimed benefit is the intended one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, Kenya", "developer_country": "United States", "deployer_country": "Kenya" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Automated night-time detection of human and vehicle intrusion at rhino sanctuaries to alert rangers. The claimed benefit is the intended one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-09-21", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Teledyne_FLIR; https://en.wikipedia.org/wiki/Kenya_Wildlife_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "setting": "Lake Nakuru National Park camera system upgraded under the same program", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "setting": "Meru National Park named as the next installation site", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "setting": "Lake Nakuru National Park camera system upgraded under the same program", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "setting": "Meru National Park named as the next installation site", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Teledyne_FLIR; https://en.wikipedia.org/wiki/Kenya_Wildlife_Service", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Wildlife protection and anti-poaching", "segment": "National wildlife authority and private conservancies" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Citizen Reporter (2024). KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0357-source-0001", "AIBD-0357-source-0002", "AIBD-0357-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Citizen Reporter (2024). KWS, rhino sites using AI-enabled cameras to stop black rhino poaching. Citizen Digital, 21 Sep 2024.", "title": "KWS, rhino sites using AI-enabled cameras to stop black rhino poaching", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "The artificial intelligence-enabled FLIR cameras have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, KWS Director General Dr Erustus Kanga quoted.", "title": "KWS, rhino sites using AI-enabled cameras (Director General statement)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "I am aware that over the last six years, we have not lost a single black rhino in this sanctuary." }, { "kind": "news report", "citation": "Citizen Reporter (2024). Same article, funding statement.", "title": "KWS, rhino sites using AI-enabled cameras (funding)", "url": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961", "date": "2024-09-21", "written_by": "independent", "quote": "came from the US State Department through WWF US and the WWF UK" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "KWS installed AI-enabled FLIR thermal cameras at three black rhino sites by Sept 2024 (US State Department and WWF funding); intrusion alerts only; no AI-linked arrest or prevented event documented.", "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Teledyne FLIR thermal cameras with on-board AI analytics for human, vehicle and animal classification and automated ranger alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Teledyne FLIR (camera analytics; developer named only as FLIR in the cited source)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Kenya Wildlife Service installs AI-enabled FLIR thermal cameras at Solio, Lake Nakuru and Ol Pejeta to send intrusion alerts to rhino rangers", "headline": "KWS's FLIR cameras send intrusion alerts at three rhino sites" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "78575ef230d9f2d3194cbfda50cc133cbbe7be7f6d3e448b16246f06635e3eff" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Citizen Digital reported on 21 September 2024 that the Kenya Wildlife Service (KWS) had installed FLIR thermal cameras at three black rhino sites. The US Department of State Bureau of International Narcotics and Law Enforcement Affairs and WWF supported it. They are Solio Game Reserve, Lake Nakuru National Park and Ol Pejeta Conservancy, with Solio the latest and Meru National Park named next. Geoffrey Chege (WWF-Kenya) said the cameras “have inbuilt analytics that send out automated alerts every time they detect an intrusion from humans, vehicles or wildlife”. Colby Loucks of WWF-US said the cameras “can tell you when a human is in the picture or an animal” and alert rangers. KWS Director General Dr Erustus Kanga said: “I am aware that over the last six years, we have not lost a single black rhino in this sanctuary”. That predates the Solio installation. The article reports no arrest, interception or prevented poaching event attributable to the cameras.", "replication": { "produced_by": [ "Thermal cameras with on-board human/vehicle/animal analytics at sanctuary perimeters", "Ranger teams receiving automated alerts", "Donor funding (US State Department INL via WWF) for installation and upgrades" ], "must_hold": [ "Camera coverage of intrusion routes", "Ranger response capacity at night", "Maintenance and power at remote sites", "Evaluation that records alerts and responses, absent so far" ], "echoes": [ { "setting": "Lake Nakuru National Park camera system upgraded under the same program", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" }, { "setting": "Meru National Park named as the next installation site", "outcome": "unknown", "source": "https://www.citizen.digital/news/kws-rhino-sites-using-ai-enabled-cameras-to-stop-black-rhino-poaching-n349961" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0315", "case_url": "records/AIBD-0315.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Built to flag landmines and other debris of war in drone imagery so that HALO analysts spend less time searching thousands of images per minefield. The pilot is that intended use." }, { "label": "AI contribution", "value": "The model is being trained to detect explosive remnants in drone imagery with HALO analysts labeling and vetting outputs. The only performance figure, around 70% accuracy, is reported by Reuters without its basis. The organization's own June 2024 and February 2025 pages report plans and workflow, not results. No independent evaluation, cleared area or detected-mine count is documented." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Ukraine" ] }, "record": { "id": "AIBD-0315", "title": "HALO Trust pilots AWS-supported AI to find explosive remnants in drone imagery of Ukrainian minefields; it may be about 70% accurate, with no clearance outcome yet", "summary": "HALO announced a US$4m AWS package (25 June 2024) to trial AI on drone imagery of Ukrainian minefields; Reuters (30 April 2026) reports about 70% accuracy; no cleared land or found mine attributed.", "date_observed": "2024-06-25", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated.", "category": "general benefit", "unexpected": false, "purpose": "Built to flag landmines and other debris of war in drone imagery so that HALO analysts spend less time searching thousands of images per minefield. The pilot is that intended use.", "system": "HALO Trust AI and machine-learning analysis of drone imagery for explosive remnants of war (hosted on AWS; SageMaker used for satellite-imagery damage detection)", "model_version": null, "developer": "The HALO Trust and development team with Amazon Web Services", "deployer": "The HALO Trust, Ukraine program", "location": "Ukraine", "domain": "Humanitarian mine action", "segment": "Survey and prioritization of contaminated land by a demining NGO", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The only figure is a pilot accuracy; faster analyst work is HALO's stated aim, not a result.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "HALO's pages state the aim (three to five analyst-days per minefield) and Reuters gives “around 70% accuracy”. No time saving, detection count or land release attributable to the model is documented." }, { "group": "societal", "depth": null, "reason": "No cited source links the AI pilot to any cleared hectare, found mine or avoided casualty in Ukraine." } ], "ai_role": { "status": "assessed", "statement": "The model is being trained to detect explosive remnants in drone imagery with HALO analysts labeling and vetting outputs. The only performance figure, around 70% accuracy, is reported by Reuters without its basis. The organization's own June 2024 and February 2025 pages report plans and workflow, not results. No independent evaluation, cleared area or detected-mine count is documented." }, "evidence": [ { "kind": "press release", "citation": "The HALO Trust (2024). AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ], "replication": { "produced_by": [ "High-resolution drone imagery of known minefields (542 flights, 11 terabytes by June 2024) manually labeled by HALO staff", "AWS cloud credits, storage and SageMaker tooling to train and host the models" ], "must_hold": [ "Trained HALO personnel vet model outputs to remove false positives and negatives before survey decisions", "Imagery resolution and ground conditions resemble the training minefields" ], "echoes": [] }, "paired_incident": null, "headline": "HALO's AI may spot mines in Ukraine with about 70% accuracy", "metric": "achieving around 70% accuracy already", "comparator": { "against": "manual analyst review of drone imagery of a minefield", "before": "three to five days to look through the imagery for an average minefield", "after": null, "basis": "assumed", "quote": "“The problem is that an analyst searching through thousands of images takes a very long time. 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external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Humanitarian mine action", "segment": "Survey and prioritization of contaminated land by a demining NGO" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "The HALO Trust (2024). AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0315-source-0001", "AIBD-0315-source-0002", "AIBD-0315-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "The HALO Trust (2024). AI to help accelerate landmine clearance. halotrust.org, 25 June 2024 (original URL now returns 404).", "title": "AI to help accelerate landmine clearance", "url": "http://web.archive.org/web/20260520220522/https://www.halotrust.org/news/artificial-intelligence-to-help-accelerate-landmine-clearance/", "date": "2024-06-25", "written_by": "makers", "quote": "HALO expects this pilot project will begin trialing drone imagery analysis using AI in the coming months." }, { "kind": "press release", "citation": "The HALO Trust (2025). Using drones and AI to eradicate landmines. halotrust.org, 21 February 2025.", "title": "Using drones and AI to eradicate landmines", "url": "https://www.halotrust.org/news/using-drones-and-ai-to-eradicate-landmines/", "date": "2025-02-21", "written_by": "makers", "quote": "It can take three to five days to look through the imagery for an average minefield," }, { "kind": "news report", "citation": "Karazy S (2026). Man, machine and AI combine in battle to clear Ukraine of mines. Reuters, 30 April 2026, syndicated by KELO.", "title": "Man, machine and AI combine in battle to clear Ukraine of mines", "url": "https://kelo.com/2026/04/30/man-machine-and-ai-combine-in-battle-to-clear-ukraine-of-mines/", "date": "2026-04-30", "written_by": "independent", "quote": "HALO Trust has turned to AI to analyse high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "HALO announced a US$4m AWS package (25 June 2024) to trial AI on drone imagery of Ukrainian minefields; Reuters (30 April 2026) reports about 70% accuracy; no cleared land or found mine attributed.", "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "HALO Trust AI and machine-learning analysis of drone imagery for explosive remnants of war (hosted on AWS; SageMaker used for satellite-imagery damage detection)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "The HALO Trust and development team with Amazon Web Services" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "HALO Trust pilots AWS-supported AI to find explosive remnants in drone imagery of Ukrainian minefields; it may be about 70% accurate, with no clearance outcome yet", "headline": "HALO's AI may spot mines in Ukraine with about 70% accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "ea9458b9fb0cadf42b4bf16a68eda0d38176e92a9ee731a432964a53e4f9a156" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The HALO Trust announced a US$4 million package of support from Amazon Web Services on 25 June 2024. An archived copy of the page, dated 20 May 2026, is among the sources. It “will enable HALO to trial artificial intelligence (AI) to assist in the process of detecting debris of war (including landmines and other explosives) in drone imagery for existing minefields and battlefields in Ukraine”. It also covers machine-learning detection of damaged buildings in satellite imagery with SageMaker. By then HALO had flown 542 drone flights over Ukrainian minefields, producing 11 terabytes of imagery. It said training would require manually labeling thousands of images and ongoing vetting by HALO staff to remove false positives and negatives. A HALO page of 21 February 2025 describes the pilot through its head of research and development, Matthew Abercrombie. He says an analyst “can take three to five days to look through the imagery for an average minefield”. It reports no results. Reuters (Sergiy Karazy, 30 April 2026, syndicated by KELO) reported from Myrotske, 40 km north-west of Kyiv. HALO “has turned to AI to analyze high-resolution drone imagery of contaminated areas and train systems to identify mines and explosive remnants, achieving around 70% accuracy already”. It quoted HALO media manager Olena Shustova. The article does not mention AWS and attributes no cleared land, found mine or time saving to the model. No later milestone is dated.", "replication": { "produced_by": [ "High-resolution drone imagery of known minefields (542 flights, 11 terabytes by June 2024) manually labeled by HALO staff", "AWS cloud credits, storage and SageMaker tooling to train and host the models" ], "must_hold": [ "Trained HALO personnel vet model outputs to remove false positives and negatives before survey decisions", "Imagery resolution and ground conditions resemble the training minefields" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0371", "case_url": "records/AIBD-0371.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Meta's pipeline is built to find posts indicating suicide or self-injury risk and route them to trained reviewers. In imminent cases it contacts local authorities (Meta Newsroom). The Kota arrangement routes those flags to police to intervene. This case is that purpose." }, { "label": "AI contribution", "value": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. Account information is then shared with police. It does not say whether Meta's classifier or a user report raised the flag in the Jhunjhunu case, nor what happened on arrival. Meta's own description (2018, updated 2021) says machine-learning detection is followed by human review before authorities are contacted. The intervention itself was police work. PTI also notes that no red-flag tags had been reported in Kota city since the launch." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0371", "title": "Kota police tie up with Meta to route suicide and self-harm flags from Facebook and Instagram to Abhay Command Centre; within a week police report a possible suicide by a Jhunjhunu student prevented", "summary": "Kota City SP Amrita Duhan said Meta's red-flag tags for suicidal posts go to a round-the-clock police team; within a week, police prevented a possible suicide by a student from Jhunjhunu (PTI).", "date_observed": "2024-05-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given.", "category": "protective response", "unexpected": false, "purpose": "Meta's pipeline is built to find posts indicating suicide or self-injury risk and route them to trained reviewers. In imminent cases it contacts local authorities (Meta Newsroom). The Kota arrangement routes those flags to police to intervene. This case is that purpose.", "system": "Meta suicide and self-injury detection on Facebook and Instagram, with red-flag tags shared with Kota police and Abhay Command Centre", "model_version": null, "developer": "Meta Platforms", "deployer": "Kota City Police (Rajasthan Police), Abhay Command Centre", "location": "Kota, India", "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2024-05-28-2dfd" }, "ratings": [ { "group": "personal", "depth": null, "reason": "PTI reports only that police “prevented a possible suicide by a student hailing from the Jhunjhunu district” within a week of the launch. The sources give no account of what the student posted, their condition when reached or what officers did. So the outcome is not established." } ], "ai_role": { "status": "assessed", "statement": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. Account information is then shared with police. It does not say whether Meta's classifier or a user report raised the flag in the Jhunjhunu case, nor what happened on arrival. Meta's own description (2018, updated 2021) says machine-learning detection is followed by human review before authorities are contacted. The intervention itself was police work. PTI also notes that no red-flag tags had been reported in Kota city since the launch." }, "evidence": [ { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024.", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "the police have prevented a possible suicide by a student hailing from the Jhunjhunu district" }, { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024 (second quotation).", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "A dedicated team, working round the clock on eight-hour shifts, has been deputed at Abhay Command Centre" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom, 10 September 2018 (updated 29 September 2021).", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "Since these efforts began last year, we've worked with first responders on over 1,000 wellness checks based on reports we've received from our proactive detection efforts." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). Kota Police and Meta Use AI Monitoring to Prevent Student Suicides. OECD.AI, 28 May 2024.", "title": "Kota Police and Meta Use AI Monitoring to Prevent Student Suicides", "url": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "date": "2024-05-28", "written_by": "independent", "quote": "Within a week, officers intervened to avert a suicide attempt from a student in Jhunjhunu." } ], "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] }, "paired_incident": null, "headline": "Kota police act on a Meta self-harm flag within a week of tie-up", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "United States", "deployer_country": "India", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, a registry entry and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0371", "catalog_version": "1.0.0", "source_fingerprint": "fde9db963ea3a4d8e85db3224fe3d54c6602cd7c4f686e22a5a6be98d4698e74", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0371-source-0001", "AIBD-0371-source-0002", "AIBD-0371-source-0003", "AIBD-0371-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Meta Platforms", "deployer": "Kota City Police (Rajasthan Police), Abhay Command Centre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. Account information is then shared with police. It does not say whether Meta's classifier or a user report raised the flag in the Jhunjhunu case, nor what happened on arrival. Meta's own description (2018, updated 2021) says machine-learning detection is followed by human review before authorities are contacted. The intervention itself was police work. PTI also notes that no red-flag tags had been reported in Kota city since the launch." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "PTI's report says the chain starts with a red-flag tag on Meta's system when a student posts suicide or self-harm content. Account information is then shared with police. It does not say whether Meta's classifier or a user report raised the flag in the Jhunjhunu case, nor what happened on arrival. Meta's own description (2018, updated 2021) says machine-learning detection is followed by human review before authorities are contacted. The intervention itself was police work. 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"account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). 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In imminent cases it contacts local authorities (Meta Newsroom). The Kota arrangement routes those flags to police to intervene. This case is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2024-05-28", "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Meta_Platforms; https://en.wikipedia.org/wiki/Rajasthan_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Meta Platforms.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Suicide prevention / online safety", "segment": "Social media platforms and police" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. LatestLY, 28 May 2024.", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "the police have prevented a possible suicide by a student hailing from the Jhunjhunu district" }, { "kind": "news report", "citation": "PTI (2024). Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students. 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OECD.AI, 28 May 2024.", "title": "Kota Police and Meta Use AI Monitoring to Prevent Student Suicides", "url": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "date": "2024-05-28", "written_by": "independent", "quote": "Within a week, officers intervened to avert a suicide attempt from a student in Jhunjhunu." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0371-source-0001", "AIBD-0371-source-0002", "AIBD-0371-source-0003", "AIBD-0371-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "PTI (2024). 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LatestLY, 28 May 2024 (second quotation).", "title": "India News | Kota Police Ropes in Meta to Prevent Suicides Among Coaching Students", "url": "https://www.latestly.com/agency-news/india-news-kota-police-ropes-in-meta-to-prevent-suicides-among-coaching-students-5996637.html", "date": "2024-05-28", "written_by": "independent", "quote": "A dedicated team, working round the clock on eight-hour shifts, has been deputed at Abhay Command Centre" }, { "kind": "press release", "citation": "Card, C. (2018). How Facebook AI Helps Suicide Prevention. Meta Newsroom, 10 September 2018 (updated 29 September 2021).", "title": "How Facebook AI Helps Suicide Prevention", "url": "https://about.fb.com/news/2018/09/inside-feed-suicide-prevention-and-ai/", "date": "2018-09-10", "written_by": "makers", "quote": "Since these efforts began last year, we've worked with first responders on over 1,000 wellness checks based on reports we've received from our proactive detection efforts." }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2024). Kota Police and Meta Use AI Monitoring to Prevent Student Suicides. OECD.AI, 28 May 2024.", "title": "Kota Police and Meta Use AI Monitoring to Prevent Student Suicides", "url": "https://oecd.ai/en/incidents/2024-05-28-2dfd", "date": "2024-05-28", "written_by": "independent", "quote": "Within a week, officers intervened to avert a suicide attempt from a student in Jhunjhunu." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Kota City SP Amrita Duhan said Meta's red-flag tags for suicidal posts go to a round-the-clock police team; within a week, police prevented a possible suicide by a student from Jhunjhunu (PTI).", "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Meta suicide and self-injury detection on Facebook and Instagram, with red-flag tags shared with Kota police and Abhay Command Centre" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Meta Platforms" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Kota police tie up with Meta to route suicide and self-harm flags from Facebook and Instagram to Abhay Command Centre; within a week police report a possible suicide by a Jhunjhunu student prevented", "headline": "Kota police act on a Meta self-harm flag within a week of tie-up" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "fde9db963ea3a4d8e85db3224fe3d54c6602cd7c4f686e22a5a6be98d4698e74" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "PTI reported from Kota on 28 May 2024 that city police under Superintendent Amrita Duhan had tied up with Meta. It aims to identify students exhibiting suicidal tendencies on Facebook and Instagram. When a student posts suicide or self-harm content, a red-flag tag appears on Meta's system and account information is shared with Kota police. For students outside Kota it goes to Abhay Command Centre or the district control room. There a dedicated team works round the clock in eight-hour shifts. Within a week of the launch, police said they had prevented a possible suicide by a student from Jhunjhunu district. No red-flag tags had yet been reported in the city. PTI counted nine coaching-student suicides in 2024 to date and 26 in 2023. Duhan planned to extend the arrangement to Jaipur (PTI via LatestLY). Meta's newsroom describes machine-learning detection with human review before authorities are contacted (Card, 2018). The intervention date is not given.", "replication": { "produced_by": [ "Meta's detection-and-review pipeline flagging suicide or self-harm posts by students on Facebook and Instagram", "A standing arrangement under which Meta shares account information with Kota police. For students outside Kota it goes to Abhay Command Centre or a district control room", "A police team monitoring the flags round the clock in eight-hour shifts" ], "must_hold": [ "The student must post on Facebook or Instagram in a way the pipeline recognizes", "Account details must resolve to a reachable location in time", "Police must be able to reach the student quickly" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0169", "case_url": "records/AIBD-0169.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "The smart network was built to detect leaks early from sensor data against predicted levels and dispatch repairs. The reduced run times are its intended use." }, { "label": "AI contribution", "value": "The BBC report, an independent source with named utility staff, describes the AI only in general terms (prediction of expected levels, alarms within minutes). The developer is unnamed. The 40% figure is the deployer's claim, so the AI's share of any leakage reduction is unverified." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0169", "title": "SES Water's network-wide AI leak alarms may cut leakage run times by up to 40%", "summary": "SES Water installed 1,000 underground flow and pressure sensors with AI prediction that raises leak alarms within minutes; it told the BBC leakage run rate times fell by up to 40% (11 March 2024).", "date_observed": "2024-03-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The BBC (Yvette Austin, 11 March 2024) reported on Sutton and East Surrey (SES) Water. It supplies 160 million liters a day to 300,000 homes and businesses in parts of Surrey, Kent and South London. It was among the first UK companies using AI across its whole network to reduce leaks. 1,000 underground sensors monitor flow and pressure. The technology predicts what levels should be. When a leak happens an alarm is raised within minutes and engineers deployed. Innovation manager Jeremy Heath said pipes are often repaired before customers notice a drop in pressure. Head of asset strategy Liam Ahearne said the smart network had reduced leakage run rate times by up to 40%. The BBC noted Ofwat's figure that about a fifth of water put into the network in England and Wales is lost to leakage. It gave SES Water's 2022/23 loss of 76 liters per property per day. It cited the Angling Trust's criticism that the company's targets were not good enough. The vendor and method are not named. No regulator report of a specific repaired burst was found.", "category": "general benefit", "unexpected": false, "purpose": "The smart network was built to detect leaks early from sensor data against predicted levels and dispatch repairs. The reduced run times are its intended use.", "system": "SES Water smart network: 1,000 flow and pressure sensors with AI-based predicted-level leak alarms (vendor not named)", "model_version": "Aquasuite Burst", "developer": null, "deployer": "Sutton and East Surrey (SES) Water", "location": "Surrey, United Kingdom", "domain": "Water infrastructure", "segment": "Water distribution leakage management", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": "The measured quantity is leak run time; the water saved is inferred, and Trustible's description names water consumption.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "The up-to-40% reduction in leakage run rate times is the utility's own claim without an independent figure." }, { "group": "societal", "depth": null, "reason": "Leakage reduction targets (15% over five years) are stated as plans; no measured outcome is attributed to the AI." } ], "ai_role": { "status": "unassessed", "statement": "The BBC report, an independent source with named utility staff, describes the AI only in general terms (prediction of expected levels, alarms within minutes). The developer is unnamed. The 40% figure is the deployer's claim, so the AI's share of any leakage reduction is unverified." }, "evidence": [ { "kind": "news report", "citation": "Yvette Austin, 'SES Water: Artificial intelligence helps reduce water leaks', BBC News, 11 March 2024", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Sutton and East Surrey (SES) Water has installed 1000 sensors underground that monitor flow and pressure, then send data back to base." }, { "kind": "news report", "citation": "BBC News, 11 March 2024 (Liam Ahearne, SES Water)", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Our smart network has enabled us to reduce our leakage run rate times by up to 40%." }, { "kind": "case report", "citation": "Royal HaskoningDHV (undated). SES Water achieves a 40% reduction in leak run time with Aquasuite. Haskoning project page.", "title": "SES Water achieves a 40% reduction in leak run time with Aquasuite", "url": "https://www.haskoning.com/en/projects/ses-water-achieves-a-reduction-in-leak-run-time", "date": null, "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Dense sensor coverage (1,000 flow and pressure sensors) across a compact supply area", "Predicted-versus-actual comparison raising alarms within minutes", "Repair crews dispatched on alarms rather than customer reports" ], "must_hold": [ "Sensor density and telemetry sufficient to localize anomalies", "Crew capacity to act on alarms quickly", "Independent reporting of leakage volumes to confirm the claimed effect" ], "echoes": [] }, "paired_incident": null, "headline": "SES Water's AI leak alarms may cut leakage run times by up to 40%", "metric": "reduced leakage run rate times by up to 40%", "comparator": { "against": "leak run times before the sensor network", "before": "previous leakage run rate times (not quantified)", "after": "reduce our leakage run rate times by up to 40%", "basis": "measured", "quote": "Our smart network has enabled us to reduce our leakage run rate times by up to 40%.", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o" }, "later": { "status": "unknown", "date": null, "note": "No dated development after March 2024 was found; the vendor's undated case study repeats the 40% figure.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.bbc.co.uk/news/articles/c4n5pwny536o; https://www.haskoning.com/en/projects/ses-water-achieves-a-reduction-in-leak-run-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.seswater.co.uk/about-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and the makers' case report. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0169", "catalog_version": "1.0.0", "source_fingerprint": "dbaab786bf860854009ad610e7138b10284f0fa325e2828b8d190cd27df6093f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0169-source-0001", "AIBD-0169-source-0002", "AIBD-0169-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "Sutton and East Surrey (SES) Water" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "The BBC report, an independent source with named utility staff, describes the AI only in general terms (prediction of expected levels, alarms within minutes). The developer is unnamed. The 40% figure is the deployer's claim, so the AI's share of any leakage reduction is unverified." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and the makers' case report. 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"state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No dated development after March 2024 was found; the vendor's undated case study repeats the 40% figure." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0169" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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"review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The BBC (Yvette Austin, 11 March 2024) reported on Sutton and East Surrey (SES) Water. 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https://www.haskoning.com/en/projects/ses-water-achieves-a-reduction-in-leak-run-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.seswater.co.uk/about-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Dense sensor coverage (1,000 flow and pressure sensors) across a compact supply area", "Predicted-versus-actual comparison raising alarms within minutes", "Repair crews dispatched on alarms rather than customer reports" ], "must_hold": [ "Sensor density and telemetry sufficient to localize anomalies", "Crew capacity to act on alarms quickly", "Independent reporting of leakage volumes to confirm the claimed effect" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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https://www.haskoning.com/en/projects/ses-water-achieves-a-reduction-in-leak-run-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.seswater.co.uk/about-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Water infrastructure", "segment": "Water distribution leakage management" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Yvette Austin, 'SES Water: Artificial intelligence helps reduce water leaks', BBC News, 11 March 2024", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Sutton and East Surrey (SES) Water has installed 1000 sensors underground that monitor flow and pressure, then send data back to base." }, { "kind": "news report", "citation": "BBC News, 11 March 2024 (Liam Ahearne, SES Water)", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Our smart network has enabled us to reduce our leakage run rate times by up to 40%." }, { "kind": "case report", "citation": "Royal HaskoningDHV (undated). SES Water achieves a 40% reduction in leak run time with Aquasuite. Haskoning project page.", "title": "SES Water achieves a 40% reduction in leak run time with Aquasuite", "url": "https://www.haskoning.com/en/projects/ses-water-achieves-a-reduction-in-leak-run-time", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0169-source-0001", "AIBD-0169-source-0002", "AIBD-0169-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Yvette Austin, 'SES Water: Artificial intelligence helps reduce water leaks', BBC News, 11 March 2024", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Sutton and East Surrey (SES) Water has installed 1000 sensors underground that monitor flow and pressure, then send data back to base." }, { "kind": "news report", "citation": "BBC News, 11 March 2024 (Liam Ahearne, SES Water)", "title": "Artificial intelligence helps reduce water leaks", "url": "https://www.bbc.co.uk/news/articles/c4n5pwny536o", "date": "2024-03-11", "written_by": "independent", "quote": "Our smart network has enabled us to reduce our leakage run rate times by up to 40%." }, { "kind": "case report", "citation": "Royal HaskoningDHV (undated). SES Water achieves a 40% reduction in leak run time with Aquasuite. 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It supplies 160 million liters a day to 300,000 homes and businesses in parts of Surrey, Kent and South London. It was among the first UK companies using AI across its whole network to reduce leaks. 1,000 underground sensors monitor flow and pressure. The technology predicts what levels should be. When a leak happens an alarm is raised within minutes and engineers deployed. Innovation manager Jeremy Heath said pipes are often repaired before customers notice a drop in pressure. Head of asset strategy Liam Ahearne said the smart network had reduced leakage run rate times by up to 40%. The BBC noted Ofwat's figure that about a fifth of water put into the network in England and Wales is lost to leakage. It gave SES Water's 2022/23 loss of 76 liters per property per day. It cited the Angling Trust's criticism that the company's targets were not good enough. The vendor and method are not named. No regulator report of a specific repaired burst was found." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The software compared sensor readings with predicted values to flag possible leaks." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. 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No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Aquasuite Burst" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "SES Water smart network: 1,000 flow and pressure sensors with AI-based predicted-level leak alarms (vendor not named)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "SES Water's network-wide AI leak alarms may cut leakage run times by up to 40%", "headline": "SES Water's AI leak alarms may cut leakage run times by up to 40%" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "dbaab786bf860854009ad610e7138b10284f0fa325e2828b8d190cd27df6093f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The BBC (Yvette Austin, 11 March 2024) reported on Sutton and East Surrey (SES) Water. It supplies 160 million liters a day to 300,000 homes and businesses in parts of Surrey, Kent and South London. It was among the first UK companies using AI across its whole network to reduce leaks. 1,000 underground sensors monitor flow and pressure. The technology predicts what levels should be. When a leak happens an alarm is raised within minutes and engineers deployed. Innovation manager Jeremy Heath said pipes are often repaired before customers notice a drop in pressure. Head of asset strategy Liam Ahearne said the smart network had reduced leakage run rate times by up to 40%. The BBC noted Ofwat's figure that about a fifth of water put into the network in England and Wales is lost to leakage. It gave SES Water's 2022/23 loss of 76 liters per property per day. It cited the Angling Trust's criticism that the company's targets were not good enough. The vendor and method are not named. No regulator report of a specific repaired burst was found.", "replication": { "produced_by": [ "Dense sensor coverage (1,000 flow and pressure sensors) across a compact supply area", "Predicted-versus-actual comparison raising alarms within minutes", "Repair crews dispatched on alarms rather than customer reports" ], "must_hold": [ "Sensor density and telemetry sufficient to localize anomalies", "Crew capacity to act on alarms quickly", "Independent reporting of leakage volumes to confirm the claimed effect" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0179", "case_url": "records/AIBD-0179.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams." }, { "label": "AI contribution", "value": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. So only the SMS figures involve the model. The parliamentary reply gives counts without attributing them to AI. Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. The reply was given by their ministry." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Singapore" ] }, "record": { "id": "AIBD-0179", "title": "Singapore's ScamShield flags about 5 million SMSes and blocks about 80,000 scam numbers by October 2023", "summary": "MHA told Parliament in November 2023 that ScamShield, built by Open Government Products with the NCPC and the Police, had about 750,000 downloads, about 80,000 numbers blacklisted and about 5 milli...", "date_observed": "2023-11-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "category": "general benefit", "unexpected": false, "purpose": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams.", "system": "ScamShield app", "model_version": null, "developer": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force", "deployer": "Singapore Police Force and National Crime Prevention Council", "location": "Singapore, Singapore", "domain": "Scam prevention", "segment": "Consumer scam calls and SMS", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Flagged-SMS and blocked-number counts are aggregates. No loss avoided by an identifiable user is documented." }, { "group": "societal", "depth": null, "reason": "No measured change in scam losses is attributed to the app in the sources." } ], "ai_role": { "status": "assessed", "statement": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. So only the SMS figures involve the model. The parliamentary reply gives counts without attributing them to AI. Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. The reply was given by their ministry." }, "evidence": [ { "kind": "report", "citation": "Ministry of Home Affairs, Singapore, oral reply by Ms Sun Xueling, Minister of State, to parliamentary questions, 22 November 2023", "title": "Oral Reply to Parliamentary Questions on Effectiveness of ScamShield in Preventing Scams", "url": "https://www.mha.gov.sg/media-room/newsroom/oral-reply-to-pq-on-effectiveness-of-scamshield-in-preventing-scams/", "date": "2023-11-22", "written_by": "deployer", "quote": "ScamShield has been effective in protecting users from scam calls and SMSes" }, { "kind": "news report", "citation": "Shirley Tay, GovInsider, June 2021 (date from Google News RSS)", "title": "Meet Singapore's latest weapon against scams", "url": "https://govinsider.asia/intl-en/article/meet-singapores-latest-weapon-against-scams-open-government-products-scamshield", "date": "2021-06-06", "written_by": "independent", "quote": "Meet Singapore's latest weapon against scams" }, { "kind": "other", "citation": "Ministry of Digital Development and Information (2026). Anti-scam efforts by MDDI. MDDI web page, last updated 18 August 2026.", "title": "Anti-scam efforts by MDDI", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "date": "2026-08-18", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "model_version": null, "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "model_version": null, "outcome": "unknown", "source": "Google News RSS headline only" } ] }, "paired_incident": null, "headline": "ScamShield flags about 5 million SMSes by October 2023", "metric": "about 5 million SMSes had been flagged to users as potential scams", "comparator": null, "later": { "status": "scaled", "date": "2026-08-18", "note": "MDDI's anti-scam page, updated 18 August 2026, says the enhanced ScamShield app launched in August 2024 grew its user base from 944,000 to 1.53 million.", "quote": "Since the launch of the enhanced ScamShield app in August 2024, the app’s user base has grown by 61.8%, from 944,000 to 1.53 million users.", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/" }, "value": null, "developer_country": "Singapore", "deployer_country": "Singapore", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Open_Government_Products; https://en.wikipedia.org/wiki/Singapore_Police_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the deployer's report and the deployer's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0179", "catalog_version": "1.0.0", "source_fingerprint": "e18a9e6a97e4e84c7230cc2cedcf3c3d96e6cedbadbeead0db7ce282981d4728", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0179-source-0001", "AIBD-0179-source-0002", "AIBD-0179-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force", "deployer": "Singapore Police Force and National Crime Prevention Council" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. 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Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. 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million SMSes had been flagged to users as potential scams", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-08-18", "note": "MDDI's anti-scam page, updated 18 August 2026, says the enhanced ScamShield app launched in August 2024 grew its user base from 944,000 to 1.53 million.", "quote": "Since the launch of the enhanced ScamShield app in August 2024, the app’s user base has grown by 61.8%, from 944,000 to 1.53 million users.", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0179" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Flagged-SMS and blocked-number counts are aggregates. No loss avoided by an identifiable user is documented." }, { "group": "societal", "reason": "No measured change in scam losses is attributed to the app in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Singapore, Singapore", "developer_country": "Singapore", "deployer_country": "Singapore" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-11-22", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Open_Government_Products; https://en.wikipedia.org/wiki/Singapore_Police_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "outcome": "unknown", "source": "Google News RSS headline only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "outcome": "unknown", "source": "Google News RSS headline only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Open_Government_Products; https://en.wikipedia.org/wiki/Singapore_Police_Force", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Scam prevention", "segment": "Consumer scam calls and SMS" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Ministry of Home Affairs, Singapore, oral reply by Ms Sun Xueling, Minister of State, to parliamentary questions, 22 November 2023", "title": "Oral Reply to Parliamentary Questions on Effectiveness of ScamShield in Preventing Scams", "url": "https://www.mha.gov.sg/media-room/newsroom/oral-reply-to-pq-on-effectiveness-of-scamshield-in-preventing-scams/", "date": "2023-11-22", "written_by": "deployer", "quote": "ScamShield has been effective in protecting users from scam calls and SMSes" }, { "kind": "news report", "citation": "Shirley Tay, GovInsider, June 2021 (date from Google News RSS)", "title": "Meet Singapore's latest weapon against scams", "url": "https://govinsider.asia/intl-en/article/meet-singapores-latest-weapon-against-scams-open-government-products-scamshield", "date": "2021-06-06", "written_by": "independent", "quote": "Meet Singapore's latest weapon against scams" }, { "kind": "other", "citation": "Ministry of Digital Development and Information (2026). 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MDDI web page, last updated 18 August 2026.", "title": "Anti-scam efforts by MDDI", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "date": "2026-08-18", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0179-source-0001", "AIBD-0179-source-0002", "AIBD-0179-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Ministry of Home Affairs, Singapore, oral reply by Ms Sun Xueling, Minister of State, to parliamentary questions, 22 November 2023", "title": "Oral Reply to Parliamentary Questions on Effectiveness of ScamShield in Preventing Scams", "url": "https://www.mha.gov.sg/media-room/newsroom/oral-reply-to-pq-on-effectiveness-of-scamshield-in-preventing-scams/", "date": "2023-11-22", "written_by": "deployer", "quote": "ScamShield has been effective in protecting users from scam calls and SMSes" }, { "kind": "news report", "citation": "Shirley Tay, GovInsider, June 2021 (date from Google News RSS)", "title": "Meet Singapore's latest weapon against scams", "url": "https://govinsider.asia/intl-en/article/meet-singapores-latest-weapon-against-scams-open-government-products-scamshield", "date": "2021-06-06", "written_by": "independent", "quote": "Meet Singapore's latest weapon against scams" }, { "kind": "other", "citation": "Ministry of Digital Development and Information (2026). 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It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ScamShield app" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Singapore's ScamShield flags about 5 million SMSes and blocks about 80,000 scam numbers by October 2023", "headline": "ScamShield flags about 5 million SMSes by October 2023" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "e18a9e6a97e4e84c7230cc2cedcf3c3d96e6cedbadbeead0db7ce282981d4728" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "outcome": "unknown", "source": "Google News RSS headline only" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0247", "case_url": "records/AIBD-0247.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The system is built to detect people or vehicles remaining inside a crossing and warn approaching train drivers." }, { "label": "AI contribution", "value": "The AI detects and the signal lamps alert human drivers, who brake. No detection statistics are given." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0247", "title": "Seibu Railway puts AI and 3D-image crossing anomaly detection into full operation at three crossings from 14 November 2022", "summary": "Response.jp (11 Nov 2022): OKI and Marubeni Network Solutions' AI monitoring and a stereo-camera 3D system detect people or vehicles left in crossings, light special signal lamps for drivers and se...", "date_observed": "2022-11-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented.", "category": "general benefit", "unexpected": false, "purpose": "The system is built to detect people or vehicles remaining inside a crossing and warn approaching train drivers.", "system": "踏切異常検知システム: 踏切滞留AI監視システム (skeleton-recognition AI) and 3D画像解析踏切監視システム (stereo-camera depth analysis)", "model_version": null, "developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)", "deployer": "Seibu Railway", "location": null, "domain": "Railway safety", "segment": "Level-crossing obstacle detection", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": null, "reason": "No outcome is documented." }, { "group": "organizational", "depth": null, "reason": "Only stable operation in trials is reported." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "unassessed", "statement": "The AI detects and the signal lamps alert human drivers, who brake. No detection statistics are given." }, "evidence": [ { "kind": "news report", "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.", "title": "西武が踏切異常検知システムを本運用", "url": "https://response.jp/article/2022/11/11/363968.html", "date": "2022-11-11", "written_by": "independent", "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.", "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents", "url": "https://oecd.ai/en/incidents/2022-11-09-4a03", "date": "2022-11-09", "written_by": "independent", "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings." } ], "replication": { "produced_by": [ "Camera coverage of smaller pedestrian crossings", "Special signal lamps already used to warn drivers", "A year-long trial before full operation" ], "must_hold": [ "Detection remains reliable across weather and lighting", "Drivers can stop in time once the lamp operates" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)", "model_version": null, "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] }, "paired_incident": null, "headline": "Seibu puts OKI AI crossing anomaly detection into full operation", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "No source after 14 November 2022 was available; the pre-launch report only states plans for two more crossings within fiscal 2022.", "quote": null, "url": null }, "value": null, "developer_country": "Japan", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Oki_Electric_Industry; https://en.wikipedia.org/wiki/Seibu_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0247", "catalog_version": "1.0.0", "source_fingerprint": "56ef2cfbd48eecd3f321afef991947a6df736b6cb1f825be9f355f8bbd351e87", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0247-source-0001", "AIBD-0247-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)", "deployer": "Seibu Railway" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "The AI detects and the signal lamps alert human drivers, who brake. No detection statistics are given." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and a registry entry. 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It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. 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https://en.wikipedia.org/wiki/Seibu_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Camera coverage of smaller pedestrian crossings", "Special signal lamps already used to warn drivers", "A year-long trial before full operation" ], "must_hold": [ "Detection remains reliable across weather and lighting", "Drivers can stop in time once the lamp operates" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)", "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.", "title": "西武が踏切異常検知システムを本運用", "url": "https://response.jp/article/2022/11/11/363968.html", "date": "2022-11-11", "written_by": "independent", "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.", "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents", "url": "https://oecd.ai/en/incidents/2022-11-09-4a03", "date": "2022-11-09", "written_by": "independent", "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0247-source-0001", "AIBD-0247-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.", "title": "西武が踏切異常検知システムを本運用", "url": "https://response.jp/article/2022/11/11/363968.html", "date": "2022-11-11", "written_by": "independent", "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.", "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents", "url": "https://oecd.ai/en/incidents/2022-11-09-4a03", "date": "2022-11-09", "written_by": "independent", "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Response.jp (11 Nov 2022): OKI and Marubeni Network Solutions' AI monitoring and a stereo-camera 3D system detect people or vehicles left in crossings, light special signal lamps for drivers and se...", "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "踏切異常検知システム: 踏切滞留AI監視システム (skeleton-recognition AI) and 3D画像解析踏切監視システム (stereo-camera depth analysis)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Seibu Railway puts AI and 3D-image crossing anomaly detection into full operation at three crossings from 14 November 2022", "headline": "Seibu puts OKI AI crossing anomaly detection into full operation" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "56ef2cfbd48eecd3f321afef991947a6df736b6cb1f825be9f355f8bbd351e87" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented.", "replication": { "produced_by": [ "Camera coverage of smaller pedestrian crossings", "Special signal lamps already used to warn drivers", "A year-long trial before full operation" ], "must_hold": [ "Detection remains reliable across weather and lighting", "Drivers can stop in time once the lamp operates" ], "echoes": [ { "date": "2026-06-24", "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)", "outcome": "recurred", "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0356", "case_url": "records/AIBD-0356.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one." }, { "label": "AI contribution", "value": "RFCx's own pages describe AI classification of streamed audio generating app alerts for responders. The 2021 Reuters report confirms the mechanism at the Solok site: AI picks out chainsaw sounds and patrols are alerted. It quotes a patroller that logging stopped, while also crediting a government enforcement crackdown. Apart from that single news item, only the maker's account is among the sources. No park authority, ministry or court record of an AI-triggered interception was found. RFCx itself said in 2021 that peer-review data to prove the effect was still being collected." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Indonesia" ] }, "record": { "id": "AIBD-0356", "title": "Rainforest Connection's Guardian acoustic AI sends chainsaw alerts to community patrols; logging in West Sumatra may have stopped as a result", "summary": "RFCx Guardian sensors detect chainsaws with AI and alert patrols; a 2021 Reuters report from Solok, West Sumatra quotes a patroller that logging stopped. No dated intervention is documented.", "date_observed": "2021-02-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded.", "category": "general benefit", "unexpected": false, "purpose": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one.", "system": "Rainforest Connection Guardian bioacoustic monitoring with cloud AI sound classification and app alerts", "model_version": "Guardian G1 (2015-2019, repurposed smartphones), G2 (2020-2025), G3 (2026-present) per rfcx.org", "developer": "Rainforest Connection (RFCx)", "deployer": "Community forest patrols and the West Sumatra forestry department (Solok regency, Indonesia); RFCx partners in other countries", "location": "Solok regency, West Sumatra, Indonesia (reported deployment); global program", "domain": "Forest protection and anti-poaching", "segment": "Non-profit conservation technology with community and government partners", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual outcome is documented." }, { "group": "organizational", "depth": null, "reason": "The West Sumatra patrol's reported easier work is not tied to a dated alert or action." }, { "group": "societal", "depth": null, "reason": "RFCx's hectare figures are self-reported aggregates with no independent evaluation." } ], "ai_role": { "status": "assessed", "statement": "RFCx's own pages describe AI classification of streamed audio generating app alerts for responders. The 2021 Reuters report confirms the mechanism at the Solok site: AI picks out chainsaw sounds and patrols are alerted. It quotes a patroller that logging stopped, while also crediting a government enforcement crackdown. Apart from that single news item, only the maker's account is among the sources. No park authority, ministry or court record of an AI-triggered interception was found. RFCx itself said in 2021 that peer-review data to prove the effect was still being collected." }, "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "date": null, "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "date": null, "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Same report, patroller statement.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging (patroller quote)", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "(Logging) has totally stopped - people are afraid of coming to this area" } ], "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "date": null, "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "model_version": null, "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "date": null, "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "model_version": null, "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] }, "paired_incident": null, "headline": "RFCx Guardian alerts may have stopped logging in Solok", "metric": null, "comparator": { "against": "logging in the area before the sensors (amid a wider enforcement crackdown)", "before": null, "after": "(Logging) has totally stopped", "basis": "assumed", "quote": "\"(Logging) has totally stopped - people are afraid of coming to this area,\" said patroller Jasrialdi, who goes by one name like many Indonesians.", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" }, "later": { "status": "unknown", "date": null, "note": "The 2021 report says RFCx planned to collect data for peer review that year; nothing dated after 11 February 2021 about Solok was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Indonesia", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/search?q=rainforest+connection; https://dishut.sumbarprov.go.id/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0356", "catalog_version": "1.0.0", "source_fingerprint": "1692d66dd5159d9e6b4caecbeb60c423c7f3bd62e6050b1ae6009910c1d99a4f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0356-source-0001", "AIBD-0356-source-0002", "AIBD-0356-source-0003", "AIBD-0356-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Rainforest Connection (RFCx)", "deployer": "Community forest patrols and the West Sumatra forestry department (Solok regency, Indonesia); RFCx partners in other countries" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "RFCx's own pages describe AI classification of streamed audio generating app alerts for responders. 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual outcome is documented." }, { "group": "organizational", "reason": "The West Sumatra patrol's reported easier work is not tied to a dated alert or action." }, { "group": "societal", "reason": "RFCx's hectare figures are self-reported aggregates with no independent evaluation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD 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"follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The 2021 report says RFCx planned to collect data for peer review that year; nothing dated after 11 February 2021 about Solok was found in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0356" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Environmental Sustainability" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded.", "benefit_type": "Improved Environmental Sustainability" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual outcome is documented." }, { "group": "organizational", "reason": "The West Sumatra patrol's reported easier work is not tied to a dated alert or action." }, { "group": "societal", "reason": "RFCx's hectare figures are self-reported aggregates with no independent evaluation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Solok regency, West Sumatra, Indonesia (reported deployment); global program", "developer_country": "United States", "deployer_country": "Indonesia" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Real-time acoustic detection of illegal logging and poaching sounds so that rangers or community patrols can respond; the reported benefit is the intended one." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2021-02-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/search?q=rainforest+connection; https://dishut.sumbarprov.go.id/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://projects.propublica.org/nonprofits/search?q=rainforest+connection; https://dishut.sumbarprov.go.id/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Forest protection and anti-poaching", "segment": "Non-profit conservation technology with community and government partners" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Same report, patroller statement.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging (patroller quote)", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "(Logging) has totally stopped - people are afraid of coming to this area" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0356-source-0001", "AIBD-0356-source-0002", "AIBD-0356-source-0003", "AIBD-0356-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Rainforest Connection (n.d.). Real-Time Threat Detection (Guardian). rfcx.org, fetched 20 Oct 2026.", "title": "Real-Time Threat Detection", "url": "https://rfcx.org/guardian", "written_by": "makers", "quote": "An agent nearby receives a real-time alert of the detected threat via our app, enabling rapid response before any damage can occur." }, { "kind": "press release", "citation": "Rainforest Connection (n.d.). Impact & Partnerships. rfcx.org, fetched 20 Oct 2026.", "title": "Impact & Partnerships", "url": "https://rfcx.org/impact", "written_by": "makers", "quote": "We work closely with NGOs and governmental organizations, including WWF, IUCN, Conservation International, the Jane Goodall Institute and the United Nations, to strengthen on-the-ground protection" }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging. The Straits Times, 11 Feb 2021.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "If the system hears a chainsaw, it sends an alert via an app to community patrols, who can check on the ground for logging." }, { "kind": "news report", "citation": "Thomson Reuters Foundation (2021). Same report, patroller statement.", "title": "Treetop sensors help Indonesia eavesdrop on forests to curb illegal logging (patroller quote)", "url": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging", "date": "2021-02-11", "written_by": "independent", "quote": "(Logging) has totally stopped - people are afraid of coming to this area" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "RFCx Guardian sensors detect chainsaws with AI and alert patrols; a 2021 Reuters report from Solok, West Sumatra quotes a patroller that logging stopped. No dated intervention is documented.", "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The system classified recorded sounds and alerted responders to possible logging." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "Guardian G1 used repurposed smartphones in 2015–2019. Later deployments used G2 from 2020 and G3 from 2026." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Guardian G1 (2015-2019, repurposed smartphones), G2 (2020-2025), G3 (2026-present) per rfcx.org" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Rainforest Connection Guardian bioacoustic monitoring with cloud AI sound classification and app alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Rainforest Connection (RFCx)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Rainforest Connection's Guardian acoustic AI sends chainsaw alerts to community patrols; logging in West Sumatra may have stopped as a result", "headline": "RFCx Guardian alerts may have stopped logging in Solok" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "1692d66dd5159d9e6b4caecbeb60c423c7f3bd62e6050b1ae6009910c1d99a4f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Rainforest Connection (RFCx), a US non-profit founded by Topher White, deploys solar-powered “Guardian” acoustic sensors. In its own words, they use “sound & artificial intelligence” to track chainsaws, vehicle engines, hunting dogs and gunshots. They send real-time alerts to a mobile app so that “an agent nearby” can respond. RFCx states it has protected over 726,000 (impact page) or 750,000 (guardian page) hectares across 8,662+ projects in 137 countries. The only non-vendor account read is a Thomson Reuters Foundation report published by The Straits Times on 11 February 2021. It is from Solok regency, West Sumatra, Indonesia, where 27 Guardian sensors had been installed more than a year earlier. Audio streams are analyzed by AI trained to pick out chainsaw sounds, and an alert goes to community patrols. Patroller Jasrialdi said logging “has totally stopped” in the area. The head of West Sumatra's forestry department, Yozarwardi Usama Putra, said he would like to expand the “early-warning system”. The same report attributes the decline in logging to a wider enforcement crackdown as well. It says RFCx planned to collect data for peer review to prove the system curbs logging. No park authority, ministry or court document records a specific alert leading to an interception, so no event is recorded.", "replication": { "produced_by": [ "Acoustic sensors with mobile-network or satellite uplink placed in forest canopy", "Cloud AI classifier trained on chainsaw and other threat sounds", "Community patrols or rangers able to reach alert locations", "Parallel government enforcement crackdown in the reported area" ], "must_hold": [ "Network coverage or satellite link at the sensor site", "Patrol capacity to respond to alerts on the ground", "Low false-alert rate so responders keep acting", "Independent evaluation to separate the sensor effect from enforcement" ], "echoes": [ { "setting": "Tembé indigenous territory, Brazil, Guardian units used by indigenous rangers against illegal logging (RFCx guardian page)", "outcome": "unknown", "source": "https://rfcx.org/guardian" }, { "setting": "Vancouver shipping lanes (whales) and a Greek national park (gunshots), per the 2021 Reuters report", "outcome": "unknown", "source": "https://www.straitstimes.com/asia/se-asia/treetop-sensors-help-indonesia-eavesdrop-on-forests-to-curb-illegal-logging" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0248", "case_url": "records/AIBD-0248.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The trial was built to detect people in crossings in real time and alert train drivers." }, { "label": "AI contribution", "value": "Trial design only. No detection or accuracy results were published in the sources." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0248", "title": "Optage and Sanyo Electric Railway trial AI analysis of crossing cameras at three crossings, August to October 2020, with full deployment targeted for spring 2021", "summary": "Mynavi News (12 Aug 2020, from a press release): edge-device AI detects people including prams and wheelchairs in crossings and signals drivers via special signal lamps and an IP-VPN link to the op...", "date_observed": "2020-08-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Mynavi News reported on 12 Aug 2020, from a press release, a field trial of AI image analysis of crossing surveillance cameras. Optage, Sanyo Electric Railway and the Sanyo Electric Railway Information Center had begun it. It ran from early August to mid-October 2020 at three crossings on Sanyo Electric Railway lines. Edge devices analyze the video in real time to detect people, including those with prams or wheelchairs. They aim to stop trains safely by signaling the driver through a special signal lamp and notifying the operations center over an IP-VPN. Accuracy in rain and at night was to be verified with K4Digital. Full deployment was targeted for spring 2021. 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Optage / Sanyo Electric Railway AI crossing image-analysis trial (press-release based).", "title": "オプテージと山陽電気鉄道、AI画像解析で踏切の安全性向上を目指す実証実験", "url": "https://news.mynavi.jp/article/20200812-1216717", "date": "2020-08-12", "written_by": "independent", "quote": "雨天時や夜間の精度の検証(K4Digital社協力)も行われる" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2020-08-11-4fb3: AI Image Analysis Trial to Enhance Railway Crossing Safety in Japan.", "title": "AI Image Analysis Trial to Enhance Railway Crossing Safety in Japan", "url": "https://oecd.ai/en/incidents/2020-08-11-4fb3", "date": "2020-08-11", "written_by": "independent", "quote": "The system aims to prevent accidents by alerting train operators in real time, with full deployment targeted for spring 2021." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Mynavi News (12 Aug 2020, from a press release): edge-device AI detects people including prams and wheelchairs in crossings and signals drivers via special signal lamps and an IP-VPN link to the op...", "account": "Mynavi News reported on 12 Aug 2020, from a press release, a field trial of AI image analysis of crossing surveillance cameras. Optage, Sanyo Electric Railway and the Sanyo Electric Railway Information Center had begun it. It ran from early August to mid-October 2020 at three crossings on Sanyo Electric Railway lines. Edge devices analyze the video in real time to detect people, including those with prams or wheelchairs. They aim to stop trains safely by signaling the driver through a special signal lamp and notifying the operations center over an IP-VPN. Accuracy in rain and at night was to be verified with K4Digital. Full deployment was targeted for spring 2021. No results are reported, and the OECD AI Incidents Monitor entry notes that no incidents had been reported." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI image analysis of crossing cameras on edge devices with special-signal-lamp and IP-VPN alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Optage (with K4Digital for accuracy verification)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Optage and Sanyo Electric Railway trial AI analysis of crossing cameras at three crossings, August to October 2020, with full deployment targeted for spring 2021", "headline": "Optage and Sanyo Electric Railway trial AI crossing cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "875b7ddd759f558729893af7d1b83c0b01966bac3ce01c0176a4f45fcb46d45c" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Mynavi News reported on 12 Aug 2020, from a press release, a field trial of AI image analysis of crossing surveillance cameras. Optage, Sanyo Electric Railway and the Sanyo Electric Railway Information Center had begun it. It ran from early August to mid-October 2020 at three crossings on Sanyo Electric Railway lines. Edge devices analyze the video in real time to detect people, including those with prams or wheelchairs. They aim to stop trains safely by signaling the driver through a special signal lamp and notifying the operations center over an IP-VPN. Accuracy in rain and at night was to be verified with K4Digital. Full deployment was targeted for spring 2021. No results are reported, and the OECD AI Incidents Monitor entry notes that no incidents had been reported.", "replication": { "produced_by": [ "Existing crossing surveillance cameras", "Edge-device inference and a special signal lamp link", "A telecom partner supplying the analysis" ], "must_hold": [ "Accuracy in rain and at night, which the trial set out to verify" ], "echoes": [ { "date": "2022-11-14", "setting": "Seibu Railway full operation of AI crossing anomaly detection (Response.jp)", "outcome": "recurred", "source": "https://response.jp/article/2022/11/11/363968.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0250", "case_url": "records/AIBD-0250.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "The technology was built to detect near misses and predict collision-risk areas to help vessel traffic controllers warn ships earlier." }, { "label": "AI contribution", "value": "The AI produced risk information that controllers used to decide warnings. The two-minute gain and doubled warning count were measured against conventional operation in replayed near-miss scenarios with six controllers." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0250", "title": "Fujitsu Zinrai collision-risk prediction trialled with the Japan Coast Guard at the Tokyo Wan Vessel Traffic Service Center: warnings about two minutes earlier, nearly double the warnings", "summary": "The Maritime Executive (15 Apr 2020): in a Dec 2019 to Mar 2020 trial, six experienced controllers re-ran past near-miss cases with and without AI risk predictions from AIS and radar data; warnings...", "date_observed": "2020-04-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Maritime Executive reported on 15 Apr 2020 that Fujitsu Limited and the Japan Coast Guard had field-tested an artificial-intelligence system to predict vessel collisions. The test ran from December 2019 to March 2020 at the Tokyo Wan Vessel Traffic Service Center. It provides navigation support under an outsourcing contract with the Coast Guard. It uses Fujitsu Human Centric AI Zinrai from Fujitsu Laboratories. The technology detects near misses between vessels and predicts areas where collision risk concentrates in Tokyo Bay. In the center's training environment, six experienced maritime traffic controllers and instructors simulated control tasks based on past near-miss accidents. They compared conventional operation with operation supported by risk information computed from past AIS data and Coast Guard risk-recommendation records. It shortened the time before a controller sent a risk warning to a vessel by an average of about two minutes. The number of warnings issued to risk-prone vessels nearly doubled, and it was found particularly effective for newcomers. The article cites the Japan Transport Safety Board's count of 2,863 marine collisions in Japan from 2009 to 2019. The results come from a simulation, not live traffic.", "category": "general benefit", "unexpected": false, "purpose": "The technology was built to detect near misses and predict collision-risk areas to help vessel traffic controllers warn ships earlier.", "system": "Fujitsu Human Centric AI Zinrai collision-risk prediction using AIS and radar data", "model_version": "Fujitsu Human Centric AI Zinrai", "developer": "Fujitsu Laboratories / Fujitsu Limited", "deployer": "Japan Coast Guard, Tokyo Wan Vessel Traffic Service Center (trial)", "location": null, "domain": "Maritime safety", "segment": "Vessel traffic services / collision-risk prediction", "benefit_type": "Reduced Task Time", "taxonomy_note": "The trial was simulated, and the earlier warning serves collision avoidance, which no measured outcome documents.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Simulation results only. No live operational outcome is documented." }, { "group": "societal", "depth": null, "reason": "No collision avoided or reduced is documented." }, { "group": "personal", "depth": null, "reason": "No individual outcome is documented." } ], "ai_role": { "status": "assessed", "statement": "The AI produced risk information that controllers used to decide warnings. The two-minute gain and doubled warning count were measured against conventional operation in replayed near-miss scenarios with six controllers." }, "evidence": [ { "kind": "news report", "citation": "The Maritime Executive (15 Apr 2020). AI System Tested by Tokyo Bay Vessel Traffic Controllers.", "title": "AI System Tested by Tokyo Bay Vessel Traffic Controllers", "url": "https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers", "date": "2020-04-15", "written_by": "independent", "quote": "It was also confirmed that the number of warnings issued for risk-prone vessels nearly doubled with the technology, indicating proactive preventive measures" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2020-04-16-d2f1: Fujitsu AI System Reduces Ship Collision Risks in Tokyo Bay Trial.", "title": "Fujitsu AI System Reduces Ship Collision Risks in Tokyo Bay Trial", "url": "https://oecd.ai/en/incidents/2020-04-16-d2f1", "date": "2020-04-16", "written_by": "independent", "quote": "Fujitsu, in partnership with the Japan Coast Guard, tested its Zinrai AI system in Tokyo Bay from December 2019 to March 2020." }, { "kind": "press release", "citation": "Fujitsu Limited (28 Sep 2021). Fujitsu Introduces AI technology Enabling Highly Accurate Prediction of Vessel Collision Risks on Complex Maritime Routes.", "title": "Fujitsu Introduces AI technology Enabling Highly Accurate Prediction of Vessel Collision Risks on Complex Maritime Routes", "url": "https://info.archives.global.fujitsu/global/about/resources/news/press-releases/2021/0928-01.html", "date": "2021-09-28", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "A VTS center with radar and AIS feeds and archived risk recommendations", "Replayed near-miss scenarios in a training environment", "Experienced controllers comparing conventional and AI-supported operation" ], "must_hold": [ "Risk predictions are computed in real time on live traffic", "Controllers trust and act on the predictions" ], "echoes": [ { "date": "2026-04-15", "setting": "Shipboard AI watchkeeping (Orca AI) on 139 container ships: 52% fewer high-severity close encounters in the first year, per a NorthStandard study", "model_version": null, "outcome": "unknown", "source": "https://www.rivieramm.com/news-content-hub/news-content-hub/northstandard-study-demonstrates-benefits-of-ai-navigation-support-88473" } ] }, "paired_incident": null, "headline": "Fujitsu Zinrai trial speeds vessel warnings by about two minutes", "metric": "the number of warnings issued to risk-prone vessels nearly doubled", "comparator": { "against": "conventional control operation without AI risk information, in a simulation of past near-miss cases", "before": "conventional operation (no absolute figure given)", "after": "risk warning sent an average of approximately two minutes earlier; number of warnings issued for risk-prone vessels nearly doubled", "basis": "measured", "quote": "It was found that the new technology could shorten the time before a risk warning is sent to a vessel by the operation controller by an average of approximately two minutes, enabling the early detection of vessels at risk of collision. It was also confirmed that the number of warnings issued for risk-prone vessels nearly doubled with the technology, indicating proactive preventive measures and the potential to enhance safety in Tokyo Bay.", "url": "https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers" }, "later": { "status": "persisted", "date": "2021-09-28", "note": "Fujitsu ran a second field trial at the same center from 17 November 2020 to 2 September 2021 with an added course-following algorithm, reported about 90% fewer unnecessary alerts than the earlier technology, and planned a commercial navigation-support service from March 2022.", "quote": "Results of the trial demonstrated that this technology can reduce unnecessary alerts by about 90% on all sea routes, especially on curved sections where alerts frequently occurred.", "url": "https://info.archives.global.fujitsu/global/about/resources/news/press-releases/2021/0928-01.html" }, "value": null, "developer_country": "Japan", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers; https://info.archives.global.fujitsu/global/about/resources/news/press-releases/2021/0928-01.html; https://www.maritime-executive.com/article/ai-system-tested-by-tokyo-bay-vessel-traffic-controllers", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Fujitsu; https://en.wikipedia.org/wiki/Japan_Coast_Guard", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, a registry entry and the makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0250", "catalog_version": "1.0.0", "source_fingerprint": "033c4a9735cf57678c91e223cbfac009a844380672754fa4cd48f9b713bbcd65", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0250-source-0001", "AIBD-0250-source-0002", "AIBD-0250-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Fujitsu Laboratories / Fujitsu Limited", "deployer": "Japan Coast Guard, Tokyo Wan Vessel Traffic Service Center (trial)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI produced risk information that controllers used to decide warnings. 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Simulation results only. No live operational outcome is documented." }, { "group": "societal", "reason": "No collision avoided or reduced is documented." }, { "group": "personal", "reason": "No individual outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Simulation results only. 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The test ran from December 2019 to March 2020 at the Tokyo Wan Vessel Traffic Service Center. It provides navigation support under an outsourcing contract with the Coast Guard. It uses Fujitsu Human Centric AI Zinrai from Fujitsu Laboratories. The technology detects near misses between vessels and predicts areas where collision risk concentrates in Tokyo Bay. In the center's training environment, six experienced maritime traffic controllers and instructors simulated control tasks based on past near-miss accidents. They compared conventional operation with operation supported by risk information computed from past AIS data and Coast Guard risk-recommendation records. It shortened the time before a controller sent a risk warning to a vessel by an average of about two minutes. The number of warnings issued to risk-prone vessels nearly doubled, and it was found particularly effective for newcomers. The article cites the Japan Transport Safety Board's count of 2,863 marine collisions in Japan from 2009 to 2019. The results come from a simulation, not live traffic." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The system estimated collision risk from vessel-position and radar data." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The deployment used Fujitsu Human Centric AI Zinrai." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Fujitsu Human Centric AI Zinrai" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Fujitsu Human Centric AI Zinrai collision-risk prediction using AIS and radar data" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Fujitsu Laboratories / Fujitsu Limited" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Fujitsu Zinrai collision-risk prediction trialled with the Japan Coast Guard at the Tokyo Wan Vessel Traffic Service Center: warnings about two minutes earlier, nearly double the warnings", "headline": "Fujitsu Zinrai trial speeds vessel warnings by about two minutes" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "033c4a9735cf57678c91e223cbfac009a844380672754fa4cd48f9b713bbcd65" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Maritime Executive reported on 15 Apr 2020 that Fujitsu Limited and the Japan Coast Guard had field-tested an artificial-intelligence system to predict vessel collisions. The test ran from December 2019 to March 2020 at the Tokyo Wan Vessel Traffic Service Center. It provides navigation support under an outsourcing contract with the Coast Guard. It uses Fujitsu Human Centric AI Zinrai from Fujitsu Laboratories. The technology detects near misses between vessels and predicts areas where collision risk concentrates in Tokyo Bay. In the center's training environment, six experienced maritime traffic controllers and instructors simulated control tasks based on past near-miss accidents. They compared conventional operation with operation supported by risk information computed from past AIS data and Coast Guard risk-recommendation records. It shortened the time before a controller sent a risk warning to a vessel by an average of about two minutes. The number of warnings issued to risk-prone vessels nearly doubled, and it was found particularly effective for newcomers. The article cites the Japan Transport Safety Board's count of 2,863 marine collisions in Japan from 2009 to 2019. The results come from a simulation, not live traffic.", "replication": { "produced_by": [ "A VTS center with radar and AIS feeds and archived risk recommendations", "Replayed near-miss scenarios in a training environment", "Experienced controllers comparing conventional and AI-supported operation" ], "must_hold": [ "Risk predictions are computed in real time on live traffic", "Controllers trust and act on the predictions" ], "echoes": [ { "date": "2026-04-15", "setting": "Shipboard AI watchkeeping (Orca AI) on 139 container ships: 52% fewer high-severity close encounters in the first year, per a NorthStandard study", "outcome": "unknown", "source": "https://www.rivieramm.com/news-content-hub/news-content-hub/northstandard-study-demonstrates-benefits-of-ai-navigation-support-88473" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0307", "case_url": "records/AIBD-0307.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Built to detect people falling from the platform onto the tracks or lingering at the platform edge and alert station staff. No detection event is documented." }, { "label": "AI contribution", "value": "No event involving the system is documented. The account comes from Odakyu's own launch release and Impress Watch's report of it. They describe what the deep-learning analysis is meant to detect and how staff are alerted. The 2024 Asilla trial at two other stations is a separate system whose results were not reported." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Japan" ] }, "record": { "id": "AIBD-0307", "title": "Odakyu Electric Railway starts a Panasonic deep-learning “fall detection system” on existing station cameras at Kyōdō and Soshigaya-Ōkura stations", "summary": "From 1 April 2020 Odakyu's system analyzes existing platform camera video to flag people who fall onto the tracks or linger at the platform edge and alerts station staff; no outcomes are reported.", "date_observed": "2020-04-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Odakyu Electric Railway announced on 30 March 2020 (press release republished by Nikkei) a 転落検知システム (fall detection system). It would operate from the first train on 1 April 2020 at Kyōdō (経堂) and Soshigaya-Ōkura (祖師ヶ谷大蔵) stations on the Odakyu Line. It uses video from existing station cameras. Built with Panasonic System Solutions Japan, the system applies Panasonic's image-analysis and deep-learning technology to analyze the camera feeds in real time. It alerts a dedicated terminal in the station office, showing the camera image, when it detects a person fallen from the platform onto the tracks. It does the same for a person who has stayed at the platform edge for a set time and risks contact with a train. Staff can then take safety measures such as an emergency stop. Pre-learning to set each camera's detection area and to distinguish people from trains had run at both stations since December 2019. Impress Watch reported the launch on 31 March 2020. Neither source reports any detection, prevented accident or performance figure, and no later outcome report was found. Impress Watch (7 October 2024) reported a separate trial of Asilla's behavior-recognition “AI Security asilla” on all 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations. It ran from 10 October 2024 to 9 January 2025. It tested detection of falls from the platform, wheelchair and white-cane users, people lingering on the platform, disputes between passengers and suspicious objects. Results were not reported. Romanizations of station names are ours; the sources give them in Japanese.", "category": "general benefit", "unexpected": false, "purpose": "Built to detect people falling from the platform onto the tracks or lingering at the platform edge and alert station staff. No detection event is documented.", "system": "転落検知システム (fall detection system): Panasonic image-analysis and deep-learning on existing station cameras", "model_version": null, "developer": "Panasonic System Solutions Japan, with Odakyu Electric Railway", "deployer": "Odakyu Electric Railway", "location": "Kyōdō and Soshigaya-Ōkura stations, Odakyu Line, Japan", "domain": "Railway safety / platform fall detection", "segment": "Station platform monitoring", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No fall, alert or prevented accident is reported by any source." }, { "group": "organizational", "depth": null, "reason": "The operator describes the intended staff workflow; no count of alerts, false alarms or staff time saved is published." } ], "ai_role": { "status": "unassessed", "statement": "No event involving the system is documented. The account comes from Odakyu's own launch release and Impress Watch's report of it. They describe what the deep-learning analysis is meant to detect and how staff are alerted. The 2024 Asilla trial at two other stations is a separate system whose results were not reported." }, "evidence": [ { "kind": "news report", "citation": "Impress Watch (2020). 既設の駅構内カメラで転落者自動検知。小田急. 31 March 2020.", "title": "既設の駅構内カメラで転落者自動検知。小田急", "url": "https://www.watch.impress.co.jp/docs/news/1243957.html", "date": "2020-03-31", "written_by": "independent", "quote": "ホームから線路への転落者を自動検知、駅係員に通知する「転落検知システム」の運用を4月1日より開始。小田急線経堂駅と祖師ヶ谷大蔵駅に設置される。" }, { "kind": "press release", "citation": "Odakyu Electric Railway (2020). 小田急電鉄、既設の駅構内カメラを活用した「転落検知システム」を運用開始. Press release of 30 March 2020, republished by Nikkei.", "title": "小田急電鉄、既設の駅構内カメラを活用した「転落検知システム」を運用開始", "url": "https://www.nikkei.com/article/DGXLRSP531980_Q0A330C2000000/", "date": "2020-03-30", "written_by": "deployer", "quote": "パナソニックが有する画像解析とディープラーニング（深層学習）技術を鉄道環境に応用し、既設の駅構内カメラの映像をリアルタイムに解析を行い" }, { "kind": "news report", "citation": "Impress Watch (2024). 小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知. 7 October 2024 (the Asilla trial echo).", "title": "小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知", "url": "https://www.watch.impress.co.jp/docs/news/1629496.html", "date": "2024-10-07", "written_by": "independent", "quote": "ホームからの転落や車いす利用者、不審者・不審物を適正に検知可能かを検証する。" } ], "replication": { "produced_by": [ "Existing station cameras covering the platform edge and track at two stations", "Panasonic deep-learning image analysis trained from December 2019 to set detection areas and tell people from trains", "A dedicated terminal in the station office that shows the alert and video for staff to act on" ], "must_hold": [ "Station staff present to confirm the alert and order an emergency stop", "Detection areas calibrated per camera; the sources give no accuracy or false-alarm figures" ], "echoes": [ { "date": "2024-10-10", "setting": "Trial of Asilla's “AI Security asilla” behavior-recognition AI on 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations, 10 October 2024 to 9 January 2025, including detection of falls from the platform", "model_version": "AI Security asilla", "outcome": "unknown", "source": "Impress Watch, 7 October 2024, https://www.watch.impress.co.jp/docs/news/1629496.html" } ] }, "paired_incident": null, "headline": "Odakyu starts Panasonic fall-detection AI on station cameras", "metric": null, "comparator": null, "later": { "status": "scaled", "date": "2024-10-10", "note": "Odakyu later extended AI analysis of existing station cameras, including detection of falls from the platform, to two more stations (Kakio and Tamagawa-Gakuen-mae) in a trial from 10 October 2024 to 9 January 2025, but with a different vendor's system (Asilla), and no result for either system has been reported.", "quote": "小田急電鉄と小田急ビルサービス、アジラは、小田急線柿生駅、玉川学園前駅で、コンコースやホームなどの既設駅構内カメラの映像に、行動認識AIを活用した解析を行なうことに関する実証実験を行なう。ホームからの転落や車いす利用者、不審者・不審物を適正に検知可能かを検証する。", "url": "https://www.watch.impress.co.jp/docs/news/1629496.html" }, "value": null, "developer_country": "Japan", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.watch.impress.co.jp/docs/news/1629496.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://ja.wikipedia.org/wiki/%E3%83%91%E3%83%8A%E3%82%BD%E3%83%8B%E3%83%83%E3%82%AF_%E3%82%B3%E3%83%8D%E3%82%AF%E3%83%88; https://en.wikipedia.org/wiki/Odakyu_Electric_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0307", "catalog_version": "1.0.0", "source_fingerprint": "c44bc917ed3fdf686824c871338c7e216102b6fb829b483716a82177e1352767", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0307-source-0001", "AIBD-0307-source-0002", "AIBD-0307-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Panasonic System Solutions Japan, with Odakyu Electric Railway", "deployer": "Odakyu Electric Railway" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "No event involving the system is documented. The account comes from Odakyu's own launch release and Impress Watch's report of it. They describe what the deep-learning analysis is meant to detect and how staff are alerted. The 2024 Asilla trial at two other stations is a separate system whose results were not reported." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No fall, alert or prevented accident is reported by any source." }, { "group": "organizational", "reason": "The operator describes the intended staff workflow; no count of alerts, false alarms or staff time saved is published." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No fall, alert or prevented accident is reported by any source." }, { "group": "organizational", "reason": "The operator describes the intended staff workflow; no count of alerts, false alarms or staff time saved is published." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": 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"outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Odakyu Electric Railway announced on 30 March 2020 (press release republished by Nikkei) a 転落検知システム (fall detection system). It would operate from the first train on 1 April 2020 at Kyōdō (経堂) and Soshigaya-Ōkura (祖師ヶ谷大蔵) stations on the Odakyu Line. It uses video from existing station cameras. Built with Panasonic System Solutions Japan, the system applies Panasonic's image-analysis and deep-learning technology to analyze the camera feeds in real time. It alerts a dedicated terminal in the station office, showing the camera image, when it detects a person fallen from the platform onto the tracks. It does the same for a person who has stayed at the platform edge for a set time and risks contact with a train. Staff can then take safety measures such as an emergency stop. Pre-learning to set each camera's detection area and to distinguish people from trains had run at both stations since December 2019. Impress Watch reported the launch on 31 March 2020. Neither source reports any detection, prevented accident or performance figure, and no later outcome report was found. Impress Watch (7 October 2024) reported a separate trial of Asilla's behavior-recognition “AI Security asilla” on all 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations. It ran from 10 October 2024 to 9 January 2025. It tested detection of falls from the platform, wheelchair and white-cane users, people lingering on the platform, disputes between passengers and suspicious objects. Results were not reported. Romanizations of station names are ours; the sources give them in Japanese.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No fall, alert or prevented accident is reported by any source." }, { "group": "organizational", "reason": "The operator describes the intended staff workflow; no count of alerts, false alarms or staff time saved is published." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Built to detect people falling from the platform onto the tracks or lingering at the platform edge and alert station staff. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Kyōdō and Soshigaya-Ōkura stations, Odakyu Line, Japan", "developer_country": "Japan", "deployer_country": "Japan" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Built to detect people falling from the platform onto the tracks or lingering at the platform edge and alert station staff. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2020-04-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.watch.impress.co.jp/docs/news/1629496.html", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://ja.wikipedia.org/wiki/%E3%83%91%E3%83%8A%E3%82%BD%E3%83%8B%E3%83%83%E3%82%AF_%E3%82%B3%E3%83%8D%E3%82%AF%E3%83%88; https://en.wikipedia.org/wiki/Odakyu_Electric_Railway", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Existing station cameras covering the platform edge and track at two stations", "Panasonic deep-learning image analysis trained from December 2019 to set detection areas and tell people from trains", "A dedicated terminal in the station office that shows the alert and video for staff to act on" ], "must_hold": [ "Station staff present to confirm the alert and order an emergency stop", "Detection areas calibrated per camera; 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Press release of 30 March 2020, republished by Nikkei.", "title": "小田急電鉄、既設の駅構内カメラを活用した「転落検知システム」を運用開始", "url": "https://www.nikkei.com/article/DGXLRSP531980_Q0A330C2000000/", "date": "2020-03-30", "written_by": "deployer", "quote": "パナソニックが有する画像解析とディープラーニング（深層学習）技術を鉄道環境に応用し、既設の駅構内カメラの映像をリアルタイムに解析を行い" }, { "kind": "news report", "citation": "Impress Watch (2024). 小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知. 7 October 2024 (the Asilla trial echo).", "title": "小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知", "url": "https://www.watch.impress.co.jp/docs/news/1629496.html", "date": "2024-10-07", "written_by": "independent", "quote": "ホームからの転落や車いす利用者、不審者・不審物を適正に検知可能かを検証する。" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0307-source-0001", "AIBD-0307-source-0002", "AIBD-0307-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Impress Watch (2020). 既設の駅構内カメラで転落者自動検知。小田急. 31 March 2020.", "title": "既設の駅構内カメラで転落者自動検知。小田急", "url": "https://www.watch.impress.co.jp/docs/news/1243957.html", "date": "2020-03-31", "written_by": "independent", "quote": "ホームから線路への転落者を自動検知、駅係員に通知する「転落検知システム」の運用を4月1日より開始。小田急線経堂駅と祖師ヶ谷大蔵駅に設置される。" }, { "kind": "press release", "citation": "Odakyu Electric Railway (2020). 小田急電鉄、既設の駅構内カメラを活用した「転落検知システム」を運用開始. Press release of 30 March 2020, republished by Nikkei.", "title": "小田急電鉄、既設の駅構内カメラを活用した「転落検知システム」を運用開始", "url": "https://www.nikkei.com/article/DGXLRSP531980_Q0A330C2000000/", "date": "2020-03-30", "written_by": "deployer", "quote": "パナソニックが有する画像解析とディープラーニング（深層学習）技術を鉄道環境に応用し、既設の駅構内カメラの映像をリアルタイムに解析を行い" }, { "kind": "news report", "citation": "Impress Watch (2024). 小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知. 7 October 2024 (the Asilla trial echo).", "title": "小田急、駅構内カメラ映像に行動認識AI　転落や車いす、不審者検知", "url": "https://www.watch.impress.co.jp/docs/news/1629496.html", "date": "2024-10-07", "written_by": "independent", "quote": "ホームからの転落や車いす利用者、不審者・不審物を適正に検知可能かを検証する。" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "From 1 April 2020 Odakyu's system analyzes existing platform camera video to flag people who fall onto the tracks or linger at the platform edge and alerts station staff; no outcomes are reported.", "account": "Odakyu Electric Railway announced on 30 March 2020 (press release republished by Nikkei) a 転落検知システム (fall detection system). It would operate from the first train on 1 April 2020 at Kyōdō (経堂) and Soshigaya-Ōkura (祖師ヶ谷大蔵) stations on the Odakyu Line. It uses video from existing station cameras. Built with Panasonic System Solutions Japan, the system applies Panasonic's image-analysis and deep-learning technology to analyze the camera feeds in real time. It alerts a dedicated terminal in the station office, showing the camera image, when it detects a person fallen from the platform onto the tracks. It does the same for a person who has stayed at the platform edge for a set time and risks contact with a train. Staff can then take safety measures such as an emergency stop. Pre-learning to set each camera's detection area and to distinguish people from trains had run at both stations since December 2019. Impress Watch reported the launch on 31 March 2020. Neither source reports any detection, prevented accident or performance figure, and no later outcome report was found. Impress Watch (7 October 2024) reported a separate trial of Asilla's behavior-recognition “AI Security asilla” on all 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations. It ran from 10 October 2024 to 9 January 2025. It tested detection of falls from the platform, wheelchair and white-cane users, people lingering on the platform, disputes between passengers and suspicious objects. Results were not reported. Romanizations of station names are ours; the sources give them in Japanese." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "転落検知システム (fall detection system): Panasonic image-analysis and deep-learning on existing station cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Panasonic System Solutions Japan, with Odakyu Electric Railway" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Odakyu Electric Railway starts a Panasonic deep-learning “fall detection system” on existing station cameras at Kyōdō and Soshigaya-Ōkura stations", "headline": "Odakyu starts Panasonic fall-detection AI on station cameras" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c44bc917ed3fdf686824c871338c7e216102b6fb829b483716a82177e1352767" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Odakyu Electric Railway announced on 30 March 2020 (press release republished by Nikkei) a 転落検知システム (fall detection system). It would operate from the first train on 1 April 2020 at Kyōdō (経堂) and Soshigaya-Ōkura (祖師ヶ谷大蔵) stations on the Odakyu Line. It uses video from existing station cameras. Built with Panasonic System Solutions Japan, the system applies Panasonic's image-analysis and deep-learning technology to analyze the camera feeds in real time. It alerts a dedicated terminal in the station office, showing the camera image, when it detects a person fallen from the platform onto the tracks. It does the same for a person who has stayed at the platform edge for a set time and risks contact with a train. Staff can then take safety measures such as an emergency stop. Pre-learning to set each camera's detection area and to distinguish people from trains had run at both stations since December 2019. Impress Watch reported the launch on 31 March 2020. Neither source reports any detection, prevented accident or performance figure, and no later outcome report was found. Impress Watch (7 October 2024) reported a separate trial of Asilla's behavior-recognition “AI Security asilla” on all 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations. It ran from 10 October 2024 to 9 January 2025. It tested detection of falls from the platform, wheelchair and white-cane users, people lingering on the platform, disputes between passengers and suspicious objects. Results were not reported. Romanizations of station names are ours; the sources give them in Japanese.", "replication": { "produced_by": [ "Existing station cameras covering the platform edge and track at two stations", "Panasonic deep-learning image analysis trained from December 2019 to set detection areas and tell people from trains", "A dedicated terminal in the station office that shows the alert and video for staff to act on" ], "must_hold": [ "Station staff present to confirm the alert and order an emergency stop", "Detection areas calibrated per camera; the sources give no accuracy or false-alarm figures" ], "echoes": [ { "date": "2024-10-10", "setting": "Trial of Asilla's “AI Security asilla” behavior-recognition AI on 20 existing cameras at Kakio and Tamagawa-Gakuen-mae stations, 10 October 2024 to 9 January 2025, including detection of falls from the platform", "model_version": "AI Security asilla", "outcome": "unknown", "source": "Impress Watch, 7 October 2024, https://www.watch.impress.co.jp/docs/news/1629496.html" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0177", "case_url": "records/AIBD-0177.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Estimate the probability that a robbery report is false from its text to support police decisions." }, { "label": "AI contribution", "value": "The peer-reviewed paper describes a natural-language-processing and machine-learning classifier with L1 feature selection that outputs the probability a report is false. The paper's authors include the police developer. AlgorithmWatch and Civio report that no independent verification was done before the police stopped using it in October 2024." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Spain" ] }, "record": { "id": "AIBD-0177", "title": "VeriPol flags 25 and 39 false robbery reports in Murcia and Málaga in a one-week 2017 pilot; Spain's police drop it in 2024", "summary": "The Spanish National Police's text-based false-report classifier flagged 25 false robberies in Murcia and 39 in Málaga in a week in June 2017 (versus 3 and 12 in the same month over the prior decad...", "date_observed": "2019-02-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "VeriPol, a model for detecting false robbery reports from their text, was developed by Complutense and Carlos III university researchers with police officer Miguel Camacho-Collados. It appeared in Knowledge-Based Systems (Quijano-Sánchez, Liberatore, Camacho-Collados and Camacho-Collados, 2018). The abstract reports a validation accuracy of 91% on more than 1,000 reports from 2015, an improvement of more than 15% over expert officers. It reports a pilot study in June 2017 showing a lower bound on empirical precision of about 83%. Scientific American (1 February 2019) reported that the one-week pilot in Murcia and Málaga detected 25 and 39 false robberies respectively. The same month over the previous decade had 3 and 12. It quoted Camacho Collados on colleagues wasting time on cases that never occurred. Civio (25 March 2025) reported that the police said that in 83.54% of reports flagged false the complainant confessed. It also reported the Interior Ministry's 2018 announcement of nationwide use. Complaints analyzed were 49,702 in 2019 with 2,338 deemed false, and 3,762 in 2022 with 511 deemed false. Use stopped on 21 October 2024 because the tool was not valid in judicial proceedings, it said. A University of Valencia report (September 2024) criticized the 1,122-report training sample, missing officer training and regional language variation. AlgorithmWatch (27 October 2020) found no independent verification of effectiveness since deployment. The pilot month carries no day.", "category": "general benefit", "unexpected": false, "purpose": "Estimate the probability that a robbery report is false from its text to support police decisions.", "system": "VeriPol", "model_version": null, "developer": "Universidad Complutense de Madrid and Universidad Carlos III de Madrid, with Spanish National Police officer Miguel Camacho-Collados", "deployer": "Spanish National Police (Policía Nacional), Ministry of the Interior", "location": "Murcia and Málaga, Spain", "domain": "Fraud and false reporting", "segment": "False robbery reports filed with police", "benefit_type": "Reduced Risk", "taxonomy_note": "The flagged false reports are fraud detected after filing, and independent sources document the absence of evaluation and the 2024 discontinuation.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Flagged-report counts and confession rates are police aggregates with no independent confirmation." }, { "group": "societal", "depth": null, "reason": "No measured effect on insurance fraud or police resources is documented." } ], "ai_role": { "status": "assessed", "statement": "The peer-reviewed paper describes a natural-language-processing and machine-learning classifier with L1 feature selection that outputs the probability a report is false. The paper's authors include the police developer. AlgorithmWatch and Civio report that no independent verification was done before the police stopped using it in October 2024." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Lara Quijano-Sánchez, Federico Liberatore, José Camacho-Collados and Miguel Camacho-Collados, Knowledge-Based Systems, June 2018, DOI 10.1016/j.knosys.2018.03.010", "title": "Applying automatic text-based detection of deceptive language to police reports: Extracting behavioral patterns from a multi-step classification model to understand how we lie to the police", "url": "https://doi.org/10.1016/j.knosys.2018.03.010", "date": null, "written_by": "makers", "quote": "A pilot study shows a lower bound on the empirical precision of 83%, approx." }, { "kind": "news report", "citation": "Emiliano Rodríguez Mega, Scientific American, 1 February 2019", "title": "An Algorithm That Can Spot When People Lie to the Police", "url": "https://www.scientificamerican.com/article/an-algorithm-that-can-spot-when-people-lie-to-the-police/", "date": "2019-02-01", "written_by": "independent", "quote": "Many colleagues were wasting a lot of time investigating cases that had never occurred. It was a problem." }, { "kind": "news report", "citation": "Naiara Bellio, AlgorithmWatch, 27 October 2020", "title": "Spanish police plan to extend use of its lie-detector while efficacy is unclear", "url": "https://algorithmwatch.org/en/spain-police-veripol/", "date": "2020-10-27", "written_by": "independent", "quote": "only labeled 5 of 100 true complaints as false" }, { "kind": "news report", "citation": "Ter García, Carmen Torrecillas, Adrián Maqueda, David Cabo and Lucas Laursen, Civio, 25 March 2025", "title": "Spanish National Police stop using Veripol, its star AI for detecting false reports", "url": "https://civio.es/transparencia/2025/03/25/national-police-stop-using-veripol-its-star-ai-for-detecting-false-reports/", "date": "2025-03-25", "written_by": "independent", "quote": "in 83.54% of the reports identified as false by Veripol, the complainant ended up confessing to lying." }, { "kind": "news report", "citation": "Adrián Maqueda, Carmen Torrecillas, David Cabo, Lucas Laursen and Ter García, AlgorithmWatch, 1 April 2025", "title": "Spanish National Police Halts Veripol, Its Flagship AI To Detect False Reports", "url": "https://algorithmwatch.org/en/spanish-police-halts-veripol/", "date": "2025-04-01", "written_by": "independent", "quote": "it had been proved being of no validity in judicial proceedings." } ], "replication": { "produced_by": [ "Training on 1,122 robbery reports from 2015 (534 genuine, 588 false) labeled by one experienced officer", "Text features selected by L1 penalization plus heuristic rules, scoring reports at the police station" ], "must_hold": [ "Reports written in the same language register as the training data (the Valencia report faulted missing regional variation)", "Officer training and protocols for acting on scores", "Independent field evaluation, absent before discontinuation" ], "echoes": [ { "date": null, "setting": "Nationwide use in Spain (2019)", "model_version": null, "outcome": "unknown", "source": "AlgorithmWatch and Civio: 49,702 complaints analyzed, 2,338 flagged false, no verification" }, { "date": null, "setting": "Spain (2022)", "model_version": null, "outcome": "unknown", "source": "Civio: 3,762 complaints analyzed, 511 deemed false" }, { "date": "2024-10-21", "setting": "Spanish National Police", "model_version": null, "outcome": "did not recur", "source": "Civio: use stopped; Ministry said the tool is not valid in judicial proceedings" } ] }, "paired_incident": null, "headline": "VeriPol flags 25 and 39 false robberies in Murcia and Málaga", "metric": "25 and 39 false robberies respectively, against 3 and 12 in the same month", "comparator": { "against": "false robbery reports detected by officers in the same month over the previous decade", "before": "only three and 12, respectively, for that month in the previous decade", "after": "25 and 39 false robberies in just one week", "basis": "measured", "quote": "A June 2017 pilot test in the cities of Murcia and Málaga helped to detect 25 and 39 false robberies in just one week—compared with only three and 12, respectively, for that month in the previous decade.", "url": "https://www.scientificamerican.com/article/an-algorithm-that-can-spot-when-people-lie-to-the-police/" }, "later": { "status": "withdrawn", "date": "2025-03-25", "note": "Civio reported that the National Police stopped using VeriPol in October 2024 (day not given) because, the Interior Ministry said, it lacks validity in judicial proceedings.", "quote": "In October 2024, six years later, the National Police stopped using the AI, the Technical Office of the Directorate General of Police has told Civio. The Ministry of the Interior stated that the reason for rejecting its use is that it lacks validity in judicial proceedings.", "url": "https://civio.es/transparencia/2025/03/25/national-police-stop-using-veripol-its-star-ai-for-detecting-false-reports/" }, "value": null, "developer_country": "Spain", "deployer_country": "Spain", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.scientificamerican.com/article/an-algorithm-that-can-spot-when-people-lie-to-the-police/; https://civio.es/transparencia/2025/03/25/national-police-stop-using-veripol-its-star-ai-for-detecting-false-reports/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Complutense_University_of_Madrid; https://en.wikipedia.org/wiki/National_Police_Corps", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Universidad Complutense de Madrid.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: four independent news reports and the makers' peer-reviewed study. 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It appeared in Knowledge-Based Systems (Quijano-Sánchez, Liberatore, Camacho-Collados and Camacho-Collados, 2018). The abstract reports a validation accuracy of 91% on more than 1,000 reports from 2015, an improvement of more than 15% over expert officers. It reports a pilot study in June 2017 showing a lower bound on empirical precision of about 83%. Scientific American (1 February 2019) reported that the one-week pilot in Murcia and Málaga detected 25 and 39 false robberies respectively. The same month over the previous decade had 3 and 12. It quoted Camacho Collados on colleagues wasting time on cases that never occurred. Civio (25 March 2025) reported that the police said that in 83.54% of reports flagged false the complainant confessed. It also reported the Interior Ministry's 2018 announcement of nationwide use. Complaints analyzed were 49,702 in 2019 with 2,338 deemed false, and 3,762 in 2022 with 511 deemed false. Use stopped on 21 October 2024 because the tool was not valid in judicial proceedings, it said. A University of Valencia report (September 2024) criticized the 1,122-report training sample, missing officer training and regional language variation. AlgorithmWatch (27 October 2020) found no independent verification of effectiveness since deployment. 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It appeared in Knowledge-Based Systems (Quijano-Sánchez, Liberatore, Camacho-Collados and Camacho-Collados, 2018). The abstract reports a validation accuracy of 91% on more than 1,000 reports from 2015, an improvement of more than 15% over expert officers. It reports a pilot study in June 2017 showing a lower bound on empirical precision of about 83%. Scientific American (1 February 2019) reported that the one-week pilot in Murcia and Málaga detected 25 and 39 false robberies respectively. The same month over the previous decade had 3 and 12. It quoted Camacho Collados on colleagues wasting time on cases that never occurred. Civio (25 March 2025) reported that the police said that in 83.54% of reports flagged false the complainant confessed. It also reported the Interior Ministry's 2018 announcement of nationwide use. Complaints analyzed were 49,702 in 2019 with 2,338 deemed false, and 3,762 in 2022 with 511 deemed false. Use stopped on 21 October 2024 because the tool was not valid in judicial proceedings, it said. A University of Valencia report (September 2024) criticized the 1,122-report training sample, missing officer training and regional language variation. AlgorithmWatch (27 October 2020) found no independent verification of effectiveness since deployment. The pilot month carries no day.", "replication": { "produced_by": [ "Training on 1,122 robbery reports from 2015 (534 genuine, 588 false) labeled by one experienced officer", "Text features selected by L1 penalization plus heuristic rules, scoring reports at the police station" ], "must_hold": [ "Reports written in the same language register as the training data (the Valencia report faulted missing regional variation)", "Officer training and protocols for acting on scores", "Independent field evaluation, absent before discontinuation" ], "echoes": [ { "setting": "Nationwide use in Spain (2019)", "outcome": "unknown", "source": "AlgorithmWatch and Civio: 49,702 complaints analyzed, 2,338 flagged false, no verification" }, { "setting": "Spain (2022)", "outcome": "unknown", "source": "Civio: 3,762 complaints analyzed, 511 deemed false" }, { "date": "2024-10-21", "setting": "Spanish National Police", "outcome": "did not recur", "source": "Civio: use stopped; Ministry said the tool is not valid in judicial proceedings" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0314", "case_url": "records/AIBD-0314.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Procured under the Delhi High Court's direction in Sadhan Haldar to match photographs of missing and found children against the Track Child database. The April 2018 trial run was that intended use." }, { "label": "AI contribution", "value": "The software produced the 2,930 matches reported in the ministry's affidavit. The claim rests on the police's report of raw output, relayed by PTI, and on the vendor's blog. No cited source verifies a single match to a reunification. The police counsel described the match rate as two percent in August 2018. The Delhi High Court recorded in January 2019 that the software had not helped crack any case. The CIS India figures (10,617 matches, 3,202 identities verified) come from a report that is not among the sources." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "India" ] }, "record": { "id": "AIBD-0314", "title": "Face matches reported in a search for missing children", "summary": "The ministry reported 2,930 facial matches among children in care. The Delhi High Court later recorded that the software had not helped solve any missing-child case.", "date_observed": "2018-04-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "PTI reported on 22 April 2018 (Business Standard) on an affidavit from the Ministry of Women and Child Development to the Delhi High Court. It said that the Delhi Police, “on a trial basis, used the FRS on 45,000 children living in different children's homes”. Also, 2,930 children “could be recognized between April 6 and April 10”. The ministry's secretary Rakesh Srivastava said that the police shared the outcome on 10 April. In Sadhan Haldar v NCT of Delhi, the court had pressed the police to test-run facial recognition. The ministry had then supplied data on about seven lakh missing children from the Track Child portal. The same report says that identities “have been established and efforts are on to help them reunite with their families”. No reunification is documented in any cited source. The Wire (republished by the Pulitzer Center, 2–3 July 2025) cites the Bureau of Police Research and Development's Compendium of Equipment. It says that the police bought two facial recognition systems on 28 March 2018, manufactured by Innefu Labs and supplied by Pelorus Technologies. It quotes an Innefu blog claiming its AI Vision product “helped in identifying 3000 missing children” in its first four days. On 23 August 2018 Delhi Police counsel Rahul Mehra told the High Court: “The software is not good. There is only two percent match” (NDTV). That was in a habeas corpus matter about women missing from a placement agency. On 22 January 2019 the High Court (Hima Kohli and Manoj Kumar Ohri JJ) recorded in Sadhan Haldar what it was told. The software “has not helped in cracking any case of missing children so far”, against 5,160 children untraced out of 19,916 reported missing in 2016–2018. A Centre for Internet and Society (CIS India) report gives 10,617 matches, of which 3,202 identities were verified, and no reunification count.", "category": "benefit created", "unexpected": false, "purpose": "Procured under the Delhi High Court's direction in Sadhan Haldar to match photographs of missing and found children against the Track Child database. The April 2018 trial run was that intended use.", "system": "Delhi Police Facial Recognition System (reported by The Wire as Innefu Labs' AI Vision)", "model_version": "AI Vision", "developer": "Innefu Labs Pvt Ltd (manufacturer, per The Wire citing the BPR&D Compendium of Equipment); supplied by Pelorus Technologies Pvt Ltd", "deployer": "Delhi Police (Crime Branch), using Ministry of Women and Child Development Track Child portal data", "location": "Delhi, India", "domain": "Policing / missing persons and child protection", "segment": "Missing children in child-care institutions", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The only documented outcome is a raw match count that the police and the High Court later undercut.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No cited source documents a child reunited with a family as a result of the 2,930 matches. The High Court recorded on 22 January 2019 that the software had not cracked any missing-child case." }, { "group": "organizational", "depth": null, "reason": "The police reported 2,930 identities “established” to the ministry. The same police told the court four months later that the software produced only a two percent match." }, { "group": "societal", "depth": null, "reason": "The court's own figures (5,160 children untraced of 19,916 reported in 2016–2018) are the context. No reduction attributable to the software is documented." } ], "ai_role": { "status": "assessed", "statement": "The software produced the 2,930 matches reported in the ministry's affidavit. The claim rests on the police's report of raw output, relayed by PTI, and on the vendor's blog. No cited source verifies a single match to a reunification. The police counsel described the match rate as two percent in August 2018. The Delhi High Court recorded in January 2019 that the software had not helped crack any case. The CIS India figures (10,617 matches, 3,202 identities verified) come from a report that is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "PTI (2018). Facial recognition system helps trace 3,000 missing children in 4 days. Business Standard, 22 April 2018.", "title": "Facial recognition system helps trace 3,000 missing children in 4 days", "url": "https://www.business-standard.com/article/pti-stories/facial-recognition-system-helps-trace-3-000-missing-children-in-4-days-118042200470_1.html", "date": "2018-04-22", "written_by": "independent", "quote": "Of them, 2,930 children could be recognised between April 6 and April 10." }, { "kind": "news report", "citation": "NDTV (2018). 2 Percent Accuracy In Delhi Police Facial Recognition Software, Court Told. NDTV, 23 August 2018.", "title": "2 Percent Accuracy In Delhi Police Facial Recognition Software, Court Told", "url": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242", "date": "2018-08-23", "written_by": "independent", "quote": "The software is not good. There is only two per cent match,", "supports_benefit": false }, { "kind": "court document", "citation": "Delhi High Court (2019). Sadhan Haldar v The State NCT of Delhi & Ors, W.P.(CRL) 1560/2017, order of 22 January 2019 (Hima Kohli and Manoj Kumar Ohri JJ). Indian Kanoon.", "title": "Sadhan Haldar vs The State Nct Of Delhi & Ors on 22 January, 2019", "url": "https://indiankanoon.org/doc/181795735/", "date": "2019-01-22", "written_by": "independent", "quote": "We are told that use of 'Facial Recognition Software' has not helped in cracking any case of missing children so far, which comes as a surprise.", "supports_benefit": false }, { "kind": "news report", "citation": "The Wire / Pulitzer Center (2025). As AI Took Over Policing in Delhi, Who Bore the Brunt? Pulitzer Center, 2 July 2025.", "title": "As AI Took Over Policing in Delhi, Who Bore the Brunt?", "url": "https://pulitzercenter.org/stories/ai-took-over-policing-delhi-who-bore-brunt", "date": "2025-07-02", "written_by": "independent", "quote": "During the first four days of its implementation, Innefu's product (AI Vision) helped in identifying 3000 missing children.", "supports_benefit": false }, { "kind": "news report", "citation": "The Wire / Pulitzer Center (2025). Looking Back at the Camera: How Controversial AI Firms Shaped Delhi's Predictive Policing. Pulitzer Center, 3 July 2025.", "title": "Looking Back at the Camera: How Controversial AI Firms Shaped Delhi's Predictive Policing", "url": "https://pulitzercenter.org/stories/looking-back-camera-how-controversial-ai-firms-shaped-delhis-predictive-policing", "date": "2025-07-03", "written_by": "independent", "quote": "While the software was originally manufactured by Innefu Labs Pvt Ltd, it was supplied to the Delhi Police by a vendor named Pelorus Technologies Pvt. Ltd.", "supports_benefit": false }, { "kind": "report", "citation": "Internet Freedom Foundation (2019). The Delhi Police must stop its facial recognition system. internetfreedom.in, 29 December 2019.", "title": "The Delhi Police must stop it's facial recognition system", "url": "https://internetfreedom.in/we-demand-the-delhi-police-stop-its-facial-recognition-system/", "date": "2019-12-29", "written_by": "independent", "quote": "at least three orders of the Hon'ble High Court recording it's dissatisfaction with the facial recognition system as late as August 23, 2019", "supports_benefit": false }, { "kind": "news report", "citation": "Manorama Yearbook (2019). Can facial recognition help fight crime? 10 July 2019.", "title": "Can facial recognition help fight crime?", "url": "https://www.manoramayearbook.in/current-affairs/india/2019/07/10/Can-facial-recognition-help-fight-crime.html", "date": "2019-07-10", "written_by": "independent", "quote": "Of them, the police were able to identify 2,930 children with the software.", "supports_benefit": false } ], "replication": { "produced_by": [ "A court-directed 1:many facial recognition trial on photographs of 45,000 children in children's homes, against Track Child portal data on about seven lakh missing children", "Police reporting of raw match counts to the ministry, which relayed them to the court by affidavit" ], "must_hold": [ "Matches are verified against identity records and followed by reunification, which no cited source shows", "The software's match rate is adequate. The police counsel's statement of a two percent match in August 2018 concerned the same software" ], "echoes": [ { "date": "2018-08-23", "setting": "Delhi High Court suggested using the software to trace women missing from an illegal placement agency; police counsel reported a two percent match", "model_version": null, "outcome": "did not recur", "source": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242" }, { "date": "2019-01-22", "setting": "Delhi High Court review of missing-children cases in Sadhan Haldar v NCT of Delhi", "model_version": null, "outcome": "did not recur", "source": "https://indiankanoon.org/doc/181795735/" } ] }, "paired_incident": null, "headline": "Face matches reported in a search for missing children", "metric": "matched 2,930 identities on 6–10 April 2018", "comparator": null, "later": { "status": "reversed", "date": "2019-01-22", "note": "On 22 January 2019 the Delhi High Court recorded in Sadhan Haldar v. NCT of Delhi that the facial recognition software had not helped crack any missing-child case, after police counsel had reported a two percent match rate in August 2018.", "quote": "“We are told that the use of ‘Facial Recognition Software’ has not helped in cracking any case of missing children so far, which comes as a surprise. It is most unacceptable that the software adopted by the Delhi Police after due diligence has not borne any results.”", "url": "https://indiankanoon.org/doc/181795735/" }, "value": null, "developer_country": "India", "deployer_country": "India", "unrated_because": "the court recorded that no case was cracked", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the court recorded that no case was cracked.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "http://web.archive.org/web/20260829155711/https://pulitzercenter.org/stories/ai-took-over-policing-delhi-who-bore-brunt; http://web.archive.org/web/20250715172059/https://pulitzercenter.org/stories/looking-back-camera-how-controversial-ai-firms-shaped-delhis-predictive-policing", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.innefu.com/contact-us; https://en.wikipedia.org/wiki/Delhi_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://www.business-standard.com/article/pti-stories/facial-recognition-system-helps-trace-3-000-missing-children-in-4-days-118042200470_1.html", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent court document, one independent report and five independent news reports. For the benefit itself, the cited sources include no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0314", "catalog_version": "1.0.0", "source_fingerprint": "c0b5467b4ae848089208b52fc57badd37164494b3e763b011569e920449d0023", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0314-source-0001", "AIBD-0314-source-0002", "AIBD-0314-source-0003", "AIBD-0314-source-0004", "AIBD-0314-source-0005", "AIBD-0314-source-0006", "AIBD-0314-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Innefu Labs Pvt Ltd (manufacturer, per The Wire citing the BPR&D Compendium of Equipment); supplied by Pelorus Technologies Pvt Ltd", "deployer": "Delhi Police (Crime Branch), using Ministry of Women and Child Development Track Child portal data" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The software produced the 2,930 matches reported in the ministry's affidavit. The claim rests on the police's report of raw output, relayed by PTI, and on the vendor's blog. No cited source verifies a single match to a reunification. The police counsel described the match rate as two percent in August 2018. The Delhi High Court recorded in January 2019 that the software had not helped crack any case. The CIS India figures (10,617 matches, 3,202 identities verified) come from a report that is not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The software produced the 2,930 matches reported in the ministry's affidavit. The claim rests on the police's report of raw output, relayed by PTI, and on the vendor's blog. No cited source verifies a single match to a reunification. The police counsel described the match rate as two percent in August 2018. The Delhi High Court recorded in January 2019 that the software had not helped crack any case. The CIS India figures (10,617 matches, 3,202 identities verified) come from a report that is not among the sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent court document, one independent report and five independent news reports. For the benefit itself, the cited sources include no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No cited source documents a child reunited with a family as a result of the 2,930 matches. The High Court recorded on 22 January 2019 that the software had not cracked any missing-child case." }, { "group": "organizational", "reason": "The police reported 2,930 identities “established” to the ministry. The same police told the court four months later that the software produced only a two percent match." }, { "group": "societal", "reason": "The court's own figures (5,160 children untraced of 19,916 reported in 2016–2018) are the context. No reduction attributable to the software is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No cited source documents a child reunited with a family as a result of the 2,930 matches. The High Court recorded on 22 January 2019 that the software had not cracked any missing-child case." }, { "group": "organizational", "reason": "The police reported 2,930 identities “established” to the ministry. The same police told the court four months later that the software produced only a two percent match." }, { "group": "societal", "reason": "The court's own figures (5,160 children untraced of 19,916 reported in 2016–2018) are the context. No reduction attributable to the software is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "matched 2,930 identities on 6–10 April 2018", "editor_note": "" } }, "evaluations[].reproduction_materials": { 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It said the Delhi Police, “on a trial basis, used the FRS on 45,000 children living in different children's homes”. Also, 2,930 children “could be recognized between April 6 and April 10”. The ministry's secretary Rakesh Srivastava said that the police shared the outcome on 10 April. In Sadhan Haldar v NCT of Delhi, the court had pressed the police to test-run facial recognition. The ministry had then supplied data on about seven lakh missing children from the Track Child portal. The same report says identities “have been established and efforts are on to help them reunite with their families”. No reunification is documented in any cited source. The Wire (republished by the Pulitzer Center, 2–3 July 2025) cites the Bureau of Police Research and Development's Compendium of Equipment. It says the police bought two facial recognition systems on 28 March 2018, manufactured by Innefu Labs and supplied by Pelorus Technologies. It quotes an Innefu blog claiming its AI Vision product “helped in identifying 3000 missing children” in its first four days. On 23 August 2018 Delhi Police counsel Rahul Mehra told the High Court: “The software is not good. There is only two percent match” (NDTV). That was in a habeas corpus matter about women missing from a placement agency. On 22 January 2019 the High Court (Hima Kohli and Manoj Kumar Ohri JJ) recorded in Sadhan Haldar what it was told. The software “has not helped in cracking any case of missing children so far”, against 5,160 children untraced out of 19,916 reported missing in 2016–2018. A Centre for Internet and Society (CIS India) report gives 10,617 matches, of which 3,202 identities were verified, and no reunification count; the report itself is not among the sources.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No cited source documents a child reunited with a family as a result of the 2,930 matches. The High Court recorded on 22 January 2019 that the software had not cracked any missing-child case." }, { "group": "organizational", "reason": "The police reported 2,930 identities “established” to the ministry. The same police told the court four months later that the software produced only a two percent match." }, { "group": "societal", "reason": "The court's own figures (5,160 children untraced of 19,916 reported in 2016–2018) are the context. No reduction attributable to the software is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Procured under the Delhi High Court's direction in Sadhan Haldar to match photographs of missing and found children against the Track Child database. 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The police counsel's statement of a two percent match in August 2018 concerned the same software" ], "echoes": [ { "date": "2018-08-23", "setting": "Delhi High Court suggested using the software to trace women missing from an illegal placement agency; police counsel reported a two percent match", "outcome": "did not recur", "source": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242" }, { "date": "2019-01-22", "setting": "Delhi High Court review of missing-children cases in Sadhan Haldar v NCT of Delhi", "outcome": "did not recur", "source": "https://indiankanoon.org/doc/181795735/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A court-directed 1:many facial recognition trial on photographs of 45,000 children in children's homes, against Track Child portal data on about seven lakh missing children", "Police reporting of raw match counts to the ministry, which relayed them to the court by affidavit" ], "must_hold": [ "Matches are verified against identity records and followed by reunification, which no cited source shows", "The software's match rate is adequate. 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Facial recognition system helps trace 3,000 missing children in 4 days. Business Standard, 22 April 2018.", "title": "Facial recognition system helps trace 3,000 missing children in 4 days", "url": "https://www.business-standard.com/article/pti-stories/facial-recognition-system-helps-trace-3-000-missing-children-in-4-days-118042200470_1.html", "date": "2018-04-22", "written_by": "independent", "quote": "Of them, 2,930 children could be recognised between April 6 and April 10." }, { "kind": "news report", "citation": "NDTV (2018). 2 Percent Accuracy In Delhi Police Facial Recognition Software, Court Told. NDTV, 23 August 2018.", "title": "2 Percent Accuracy In Delhi Police Facial Recognition Software, Court Told", "url": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242", "date": "2018-08-23", "written_by": "independent", "quote": "The software is not good. There is only two per cent match,", "supports_benefit": false }, { "kind": "court document", "citation": "Delhi High Court (2019). Sadhan Haldar v The State NCT of Delhi & Ors, W.P.(CRL) 1560/2017, order of 22 January 2019 (Hima Kohli and Manoj Kumar Ohri JJ). Indian Kanoon.", "title": "Sadhan Haldar vs The State Nct Of Delhi & Ors on 22 January, 2019", "url": "https://indiankanoon.org/doc/181795735/", "date": "2019-01-22", "written_by": "independent", "quote": "We are told that use of 'Facial Recognition Software' has not helped in cracking any case of missing children so far, which comes as a surprise.", "supports_benefit": false }, { "kind": "news report", "citation": "The Wire / Pulitzer Center (2025). As AI Took Over Policing in Delhi, Who Bore the Brunt? Pulitzer Center, 2 July 2025.", "title": "As AI Took Over Policing in Delhi, Who Bore the Brunt?", "url": "https://pulitzercenter.org/stories/ai-took-over-policing-delhi-who-bore-brunt", "date": "2025-07-02", "written_by": "independent", "quote": "During the first four days of its implementation, Innefu's product (AI Vision) helped in identifying 3000 missing children.", "supports_benefit": false }, { "kind": "news report", "citation": "The Wire / Pulitzer Center (2025). Looking Back at the Camera: How Controversial AI Firms Shaped Delhi's Predictive Policing. Pulitzer Center, 3 July 2025.", "title": "Looking Back at the Camera: How Controversial AI Firms Shaped Delhi's Predictive Policing", "url": "https://pulitzercenter.org/stories/looking-back-camera-how-controversial-ai-firms-shaped-delhis-predictive-policing", "date": "2025-07-03", "written_by": "independent", "quote": "While the software was originally manufactured by Innefu Labs Pvt Ltd, it was supplied to the Delhi Police by a vendor named Pelorus Technologies Pvt. Ltd.", "supports_benefit": false }, { "kind": "report", "citation": "Internet Freedom Foundation (2019). The Delhi Police must stop its facial recognition system. internetfreedom.in, 29 December 2019.", "title": "The Delhi Police must stop it's facial recognition system", "url": "https://internetfreedom.in/we-demand-the-delhi-police-stop-its-facial-recognition-system/", "date": "2019-12-29", "written_by": "independent", "quote": "at least three orders of the Hon'ble High Court recording it's dissatisfaction with the facial recognition system as late as August 23, 2019", "supports_benefit": false }, { "kind": "news report", "citation": "Manorama Yearbook (2019). Can facial recognition help fight crime? 10 July 2019.", "title": "Can facial recognition help fight crime?", "url": "https://www.manoramayearbook.in/current-affairs/india/2019/07/10/Can-facial-recognition-help-fight-crime.html", "date": "2019-07-10", "written_by": "independent", "quote": "Of them, the police were able to identify 2,930 children with the software.", "supports_benefit": false } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0314-source-0001", "AIBD-0314-source-0002", "AIBD-0314-source-0003", "AIBD-0314-source-0004", "AIBD-0314-source-0005", "AIBD-0314-source-0006", "AIBD-0314-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "text", "text": "The early report relays the ministry’s matching claim. The court later recorded that no missing-child case had been cracked. The retained investigations add contrary evidence and procurement details." }, "provenance": { "kind": "source_review", "basis": "The identified originals and their locators are listed in the source assessment.", "record_fields": [], "source_refs": [ "AIBD-0314-source-0002", "AIBD-0314-source-0003", "AIBD-0314-source-0004", "AIBD-0314-source-0005", "AIBD-0314-source-0006", "AIBD-0314-source-0007" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The early report relays the ministry’s matching claim. The court later recorded that no missing-child case had been cracked. The retained investigations add contrary evidence and procurement details." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0314-source-0001", "retained_source_order": [ "AIBD-0314-source-0001", "AIBD-0314-source-0002", "AIBD-0314-source-0003", "AIBD-0314-source-0004", "AIBD-0314-source-0005", "AIBD-0314-source-0006", "AIBD-0314-source-0007" ], "origins": [ { "url": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242", "locator": "PTI report, paragraphs on Rahul Mehra’s submission to the High Court.", "contribution": "The police counsel’s two-percent match statement is contrary evidence, not proof of a benefit.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0002" ] }, { "url": "https://indiankanoon.org/doc/181795735/", "locator": "Order of 22 January 2019, ¶ 12, printed p. 6.", "contribution": "The court records that it was told that no missing-child case had been cracked. It does not find the claimed benefit.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0003" ] }, { "url": "https://pulitzercenter.org/stories/ai-took-over-policing-delhi-who-bore-brunt", "locator": "The investigation’s discussion of AI Vision and the claimed identification of missing children.", "contribution": "The investigation supplies later scrutiny and harm accounts. Its missing-child figure is attributed to the vendor, not independently verified.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0004" ] }, { "url": "https://pulitzercenter.org/stories/looking-back-camera-how-controversial-ai-firms-shaped-delhis-predictive-policing", "locator": "Opening procurement paragraphs and the quotation from Innefu’s AI Vision page.", "contribution": "The investigation establishes procurement and vendor relationships. Its missing-child figure repeats the developer’s account.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0005" ] }, { "url": "https://internetfreedom.in/we-demand-the-delhi-police-stop-its-facial-recognition-system/", "locator": "Sections on the low success rate and the demand to halt facial recognition.", "contribution": "The advocacy report challenges effectiveness and legality. It must not count as an independent study supporting a positive result.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0006" ] }, { "url": "https://www.manoramayearbook.in/current-affairs/india/2019/07/10/Can-facial-recognition-help-fight-crime.html", "locator": "Telangana model, paragraphs on Delhi’s trial and the High Court.", "contribution": "The explainer repeats the ministry’s affidavit and the High Court’s contrary statement. It supplies no new missing-child result.", "relationship": "Independent scrutiny or context, not positive corroboration.", "source_refs": [ "AIBD-0314-source-0007" ] } ], "excluded_or_consolidated_sources": [], "review_scope": "Whether the specified sources establish the reported benefit, their document types, and their authorship relationships. Other fields and sources were not comprehensively reassessed." } }, "provenance": { "kind": "source_review", "basis": "A limited source-role review; each inspected source and its contribution is identified.", "record_fields": [], "source_refs": [ "AIBD-0314-source-0002", "AIBD-0314-source-0003", "AIBD-0314-source-0004", "AIBD-0314-source-0005", "AIBD-0314-source-0006", "AIBD-0314-source-0007" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The early report relays the ministry’s matching claim. The court later recorded that no missing-child case had been cracked. The retained investigations add contrary evidence and procurement details." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The ministry reported 2,930 facial matches among children in care. The Delhi High Court later recorded that the software had not helped solve any missing-child case.", "account": "PTI reported on 22 April 2018 (Business Standard) on an affidavit from the Ministry of Women and Child Development to the Delhi High Court. It said that the Delhi Police, “on a trial basis, used the FRS on 45,000 children living in different children's homes”. Also, 2,930 children “could be recognized between April 6 and April 10”. The ministry's secretary Rakesh Srivastava said that the police shared the outcome on 10 April. In Sadhan Haldar v NCT of Delhi, the court had pressed the police to test-run facial recognition. The ministry had then supplied data on about seven lakh missing children from the Track Child portal. The same report says that identities “have been established and efforts are on to help them reunite with their families”. No reunification is documented in any cited source. The Wire (republished by the Pulitzer Center, 2–3 July 2025) cites the Bureau of Police Research and Development's Compendium of Equipment. It says that the police bought two facial recognition systems on 28 March 2018, manufactured by Innefu Labs and supplied by Pelorus Technologies. It quotes an Innefu blog claiming its AI Vision product “helped in identifying 3000 missing children” in its first four days. On 23 August 2018 Delhi Police counsel Rahul Mehra told the High Court: “The software is not good. There is only two percent match” (NDTV). That was in a habeas corpus matter about women missing from a placement agency. On 22 January 2019 the High Court (Hima Kohli and Manoj Kumar Ohri JJ) recorded in Sadhan Haldar what it was told. The software “has not helped in cracking any case of missing children so far”, against 5,160 children untraced out of 19,916 reported missing in 2016–2018. A Centre for Internet and Society (CIS India) report gives 10,617 matches, of which 3,202 identities were verified, and no reunification count." } }, "provenance": { "kind": "legacy_record", "basis": "Wording aligned with the source review of 25 October 2026; the longer account retains previously collected details.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The software compared facial images to propose possible matches." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The Wire identified the facial-recognition software as Innefu Labs’ AI Vision." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "AI Vision" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Delhi Police Facial Recognition System (reported by The Wire as Innefu Labs' AI Vision)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Innefu Labs Pvt Ltd (manufacturer, per The Wire citing the BPR&D Compendium of Equipment); supplied by Pelorus Technologies Pvt Ltd" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Face matches reported in a search for missing children", "headline": "Face matches reported in a search for missing children" } }, "provenance": { "kind": "legacy_record", "basis": "Wording aligned with the source review of 25 October 2026; the longer account retains previously collected details.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c0b5467b4ae848089208b52fc57badd37164494b3e763b011569e920449d0023" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "PTI reported on 22 April 2018 (Business Standard) on an affidavit from the Ministry of Women and Child Development to the Delhi High Court. It said the Delhi Police, “on a trial basis, used the FRS on 45,000 children living in different children's homes”. Also, 2,930 children “could be recognized between April 6 and April 10”. The ministry's secretary Rakesh Srivastava said that the police shared the outcome on 10 April. In Sadhan Haldar v NCT of Delhi, the court had pressed the police to test-run facial recognition. The ministry had then supplied data on about seven lakh missing children from the Track Child portal. The same report says identities “have been established and efforts are on to help them reunite with their families”. No reunification is documented in any cited source. The Wire (republished by the Pulitzer Center, 2–3 July 2025) cites the Bureau of Police Research and Development's Compendium of Equipment. It says the police bought two facial recognition systems on 28 March 2018, manufactured by Innefu Labs and supplied by Pelorus Technologies. It quotes an Innefu blog claiming its AI Vision product “helped in identifying 3000 missing children” in its first four days. On 23 August 2018 Delhi Police counsel Rahul Mehra told the High Court: “The software is not good. There is only two percent match” (NDTV). That was in a habeas corpus matter about women missing from a placement agency. On 22 January 2019 the High Court (Hima Kohli and Manoj Kumar Ohri JJ) recorded in Sadhan Haldar what it was told. The software “has not helped in cracking any case of missing children so far”, against 5,160 children untraced out of 19,916 reported missing in 2016–2018. A Centre for Internet and Society (CIS India) report gives 10,617 matches, of which 3,202 identities were verified, and no reunification count; the report itself is not among the sources.", "replication": { "produced_by": [ "A court-directed 1:many facial recognition trial on photographs of 45,000 children in children's homes, against Track Child portal data on about seven lakh missing children", "Police reporting of raw match counts to the ministry, which relayed them to the court by affidavit" ], "must_hold": [ "Matches are verified against identity records and followed by reunification, which no cited source shows", "The software's match rate is adequate. The police counsel's statement of a two percent match in August 2018 concerned the same software" ], "echoes": [ { "date": "2018-08-23", "setting": "Delhi High Court suggested using the software to trace women missing from an illegal placement agency; police counsel reported a two percent match", "outcome": "did not recur", "source": "https://www.ndtv.com/delhi-news/2-percent-accuracy-in-delhi-police-facial-recognition-software-court-told-1905242" }, { "date": "2019-01-22", "setting": "Delhi High Court review of missing-children cases in Sadhan Haldar v NCT of Delhi", "outcome": "did not recur", "source": "https://indiankanoon.org/doc/181795735/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 2, "record_mapped": 30, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0231", "case_url": "records/AIBD-0231.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Mine online sex advertisements (text, phone numbers, facial recognition on images) to help law enforcement find trafficking victims and link cases." }, { "label": "AI contribution", "value": "Named Raleigh officers describe what the tool does for them (phone-number and image linkage across cities). The claims that it has led to hundreds of recoveries come from the vendor, relayed by CBN and Futurism without a documented case." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0231", "title": "Raleigh police use Marinus Analytics' Traffic Jam to link phone numbers and photos across cities in a live trafficking case", "summary": "CBN (18 Oct 2017) shows Raleigh PD detectives using Traffic Jam, a CMU-born tool with facial recognition over escort ads, to corroborate a trafficker's movements; outcomes stated only as aggregates.", "date_observed": "2017-10-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The case described was ongoing. Major Richard Hoffman of the Raleigh Police Department described Traffic Jam to CBN. It lets investigators link a potential victim to another victim and then to a suspect. Its image search returns historical images tied to the same phone number or similar image. Detective Rob Pereira described pulling a phone number from a Colorado police report into Traffic Jam to see where and when it had been used. This corroborated several victims' accounts of where the trafficker was taking them. Marinus co-founder Cara Jones described the tool as applying artificial intelligence to publicly available ad data. CBN states that Traffic Jam has helped recover hundreds of victims. Futurism (2018) repeats a similar claim from CEO Emily Kennedy with an 88 percent success rate. No individually attributable rescue is documented.", "category": "general benefit", "unexpected": false, "purpose": "Mine online sex advertisements (text, phone numbers, facial recognition on images) to help law enforcement find trafficking victims and link cases.", "system": "Traffic Jam", "model_version": null, "developer": "Marinus Analytics (origin: Carnegie Mellon University undergraduate project)", "deployer": "Raleigh Police Department", "location": "Raleigh, United States", "domain": "Anti-trafficking", "segment": "Government", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Raleigh PD officials credit faster linkage of victims and suspects, but no case outcome is documented." }, { "group": "personal", "depth": null, "reason": "No individually attributable victim outcome in the sources." } ], "ai_role": { "status": "assessed", "statement": "Named Raleigh officers describe what the tool does for them (phone-number and image linkage across cities). The claims that it has led to hundreds of recoveries come from the vendor, relayed by CBN and Futurism without a documented case." }, "evidence": [ { "kind": "news report", "citation": "Caitlin Burke, CBN News, 18 Oct 2017", "title": "Creating a 'Traffic Jam' Against Sex Trafficking: This Groundbreaking Tech is a Game-Changer", "url": "https://cbn.com/news/us/creating-traffic-jam-against-sex-trafficking-groundbreaking-tech-game-changer", "date": "2017-10-18", "written_by": "independent", "quote": "Traffic Jam allows us to look at a potential victim, identify a link to another victim and then by having victims linked, identify a suspect and have a working case," }, { "kind": "news report", "citation": "Alexandra Ossola, Futurism, 16 Apr 2018", "title": "AI Tool Helps Law Enforcement Find Victims of Human Trafficking", "url": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "date": "2018-04-16", "written_by": "independent", "quote": "data from Traffic Jam has already led to the rescue of hundreds of victims of human trafficking in the U.S. and Canada." } ], "replication": { "produced_by": [ "Continuous scraping of escort ads since 2011", "Phone-number and image-similarity indexing across cities", "Detectives with an existing lead (phone number or photo)" ], "must_hold": [ "Ads must remain publicly scrapable (Backpage was shut in 2018)", "Agency access and training" ], "echoes": [] }, "paired_incident": null, "headline": "Raleigh police use Traffic Jam to link a trafficker's movements", "metric": null, "comparator": null, "later": { "status": "scaled", "date": "2018-04-16", "note": "By April 2018 Marinus said Traffic Jam data had led to the rescue of hundreds of victims in the U.S. and Canada and that the software was crawling data from 18 countries.", "quote": "The software is now crawling data from 18 countries, she says, and the company will soon expand to the United Kingdom and, soon, beyond.", "url": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.marinusanalytics.com/payment-terms-us; https://raleighnc.gov/police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0231", "catalog_version": "1.0.0", "source_fingerprint": "5501c8eafe79813252b4fdb5052a54fcab814eb0447ef60f7d2ed06e6607d243", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0231-source-0001", "AIBD-0231-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Marinus Analytics (origin: Carnegie Mellon University undergraduate project)", "deployer": "Raleigh Police Department" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Named Raleigh officers describe what the tool does for them (phone-number and image linkage across cities). The claims that it has led to hundreds of recoveries come from the vendor, relayed by CBN and Futurism without a documented case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "Named Raleigh officers describe what the tool does for them (phone-number and image linkage across cities). The claims that it has led to hundreds of recoveries come from the vendor, relayed by CBN and Futurism without a documented case." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Raleigh PD officials credit faster linkage of victims and suspects, but no case outcome is documented." }, { "group": "personal", "reason": "No individually attributable victim outcome in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Raleigh PD officials credit faster linkage of victims and suspects, but no case outcome is documented." }, { "group": "personal", "reason": "No individually attributable victim outcome in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2018-04-16", "note": "By April 2018 Marinus said Traffic Jam data had led to the rescue of hundreds of victims in the U.S. and Canada and that the software was crawling data from 18 countries.", "quote": "The software is now crawling data from 18 countries, she says, and the company will soon expand to the United Kingdom and, soon, beyond.", "url": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0231" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The case described was ongoing. Major Richard Hoffman of the Raleigh Police Department described Traffic Jam to CBN. It lets investigators link a potential victim to another victim and then to a suspect. Its image search returns historical images tied to the same phone number or similar image. Detective Rob Pereira described pulling a phone number from a Colorado police report into Traffic Jam to see where and when it had been used. This corroborated several victims' accounts of where the trafficker was taking them. Marinus co-founder Cara Jones described the tool as applying artificial intelligence to publicly available ad data. CBN states that Traffic Jam has helped recover hundreds of victims. Futurism (2018) repeats a similar claim from CEO Emily Kennedy with an 88 percent success rate. No individually attributable rescue is documented.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Raleigh PD officials credit faster linkage of victims and suspects, but no case outcome is documented." }, { "group": "personal", "reason": "No individually attributable victim outcome in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Mine online sex advertisements (text, phone numbers, facial recognition on images) to help law enforcement find trafficking victims and link cases." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Raleigh, United States", "developer_country": "United States", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Mine online sex advertisements (text, phone numbers, facial recognition on images) to help law enforcement find trafficking victims and link cases." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2017-10-18", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.marinusanalytics.com/payment-terms-us; https://raleighnc.gov/police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Continuous scraping of escort ads since 2011", "Phone-number and image-similarity indexing across cities", "Detectives with an existing lead (phone number or photo)" ], "must_hold": [ "Ads must remain publicly scrapable (Backpage was shut in 2018)", "Agency access and training" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.marinusanalytics.com/payment-terms-us; https://raleighnc.gov/police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Anti-trafficking", "segment": "Government" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Caitlin Burke, CBN News, 18 Oct 2017", "title": "Creating a 'Traffic Jam' Against Sex Trafficking: This Groundbreaking Tech is a Game-Changer", "url": "https://cbn.com/news/us/creating-traffic-jam-against-sex-trafficking-groundbreaking-tech-game-changer", "date": "2017-10-18", "written_by": "independent", "quote": "Traffic Jam allows us to look at a potential victim, identify a link to another victim and then by having victims linked, identify a suspect and have a working case," }, { "kind": "news report", "citation": "Alexandra Ossola, Futurism, 16 Apr 2018", "title": "AI Tool Helps Law Enforcement Find Victims of Human Trafficking", "url": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "date": "2018-04-16", "written_by": "independent", "quote": "data from Traffic Jam has already led to the rescue of hundreds of victims of human trafficking in the U.S. and Canada." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0231-source-0001", "AIBD-0231-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Caitlin Burke, CBN News, 18 Oct 2017", "title": "Creating a 'Traffic Jam' Against Sex Trafficking: This Groundbreaking Tech is a Game-Changer", "url": "https://cbn.com/news/us/creating-traffic-jam-against-sex-trafficking-groundbreaking-tech-game-changer", "date": "2017-10-18", "written_by": "independent", "quote": "Traffic Jam allows us to look at a potential victim, identify a link to another victim and then by having victims linked, identify a suspect and have a working case," }, { "kind": "news report", "citation": "Alexandra Ossola, Futurism, 16 Apr 2018", "title": "AI Tool Helps Law Enforcement Find Victims of Human Trafficking", "url": "https://futurism.com/ai-tool-law-enforcement-stop-human-trafficking", "date": "2018-04-16", "written_by": "independent", "quote": "data from Traffic Jam has already led to the rescue of hundreds of victims of human trafficking in the U.S. and Canada." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "CBN (18 Oct 2017) shows Raleigh PD detectives using Traffic Jam, a CMU-born tool with facial recognition over escort ads, to corroborate a trafficker's movements; outcomes stated only as aggregates.", "account": "The case described was ongoing. Major Richard Hoffman of the Raleigh Police Department described Traffic Jam to CBN. It lets investigators link a potential victim to another victim and then to a suspect. Its image search returns historical images tied to the same phone number or similar image. Detective Rob Pereira described pulling a phone number from a Colorado police report into Traffic Jam to see where and when it had been used. This corroborated several victims' accounts of where the trafficker was taking them. Marinus co-founder Cara Jones described the tool as applying artificial intelligence to publicly available ad data. CBN states that Traffic Jam has helped recover hundreds of victims. Futurism (2018) repeats a similar claim from CEO Emily Kennedy with an 88 percent success rate. No individually attributable rescue is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Traffic Jam" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Marinus Analytics (origin: Carnegie Mellon University undergraduate project)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Raleigh police use Marinus Analytics' Traffic Jam to link phone numbers and photos across cities in a live trafficking case", "headline": "Raleigh police use Traffic Jam to link a trafficker's movements" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5501c8eafe79813252b4fdb5052a54fcab814eb0447ef60f7d2ed06e6607d243" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The case described was ongoing. Major Richard Hoffman of the Raleigh Police Department described Traffic Jam to CBN. It lets investigators link a potential victim to another victim and then to a suspect. Its image search returns historical images tied to the same phone number or similar image. Detective Rob Pereira described pulling a phone number from a Colorado police report into Traffic Jam to see where and when it had been used. This corroborated several victims' accounts of where the trafficker was taking them. Marinus co-founder Cara Jones described the tool as applying artificial intelligence to publicly available ad data. CBN states that Traffic Jam has helped recover hundreds of victims. Futurism (2018) repeats a similar claim from CEO Emily Kennedy with an 88 percent success rate. No individually attributable rescue is documented.", "replication": { "produced_by": [ "Continuous scraping of escort ads since 2011", "Phone-number and image-similarity indexing across cities", "Detectives with an existing lead (phone number or photo)" ], "must_hold": [ "Ads must remain publicly scrapable (Backpage was shut in 2018)", "Agency access and training" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0308", "case_url": "records/AIBD-0308.html", "reason": "Detection of faults or hazards in communications, transport, and associated infrastructure.", "details": [ { "label": "Focus", "value": "Communications and transport" }, { "label": "Purpose", "value": "Trial of AI video monitoring to detect anomalies on station platforms and alert administrators. No detection event is documented." }, { "label": "AI contribution", "value": "No event is reported. The AI's role is described only in the vendor's launch release (real-time video monitoring, anomaly alerts, email to administrators). No account from JR Kyushu was found." } ], "filters": { "topic": "Communications and transport", "benefit": "not_assessed", "jurisdiction": [ "Japan" ] }, "record": { "id": "AIBD-0308", "title": "OPTiM begins a trial of its “AI Physical Security Service” AI camera monitoring at two JR Kyushu stations, Wajiro and Futajima, from 10 October 2017", "summary": "OPTiM's release says its cloud AI monitors station camera video in real time, recognizing people, trains and the platform yellow line, and alerts administrators on anomalies; no results published.", "date_observed": "2017-10-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "OPTiM Corporation announced on 6 October 2017 a demonstration trial of its “AI Physical Security Service” from 10 October 2017. It would run at two Kyushu Railway Company (JR Kyushu) stations: 和白駅 (Wajiro) on the Kashii Line and 二島駅 (Futajima) on the Chikuho Main Line. The service, on “OPTiM Cloud IoT OS”, has AI monitor network-camera video in real time. When it detects an anomaly, it shows an alert and emails administrators. This is to reduce the burden on operators who watch many monitors and to raise safety in the station. The release includes an image of station camera analysis identifying people, trains and the yellow platform line. The release is the only source (on optim.co.jp and republished by @Press). JR Kyushu is not quoted, no trial end date, detection target list or result is given, and no later report of outcomes was found. Station romanizations are ours.", "category": "general benefit", "unexpected": false, "purpose": "Trial of AI video monitoring to detect anomalies on station platforms and alert administrators. No detection event is documented.", "system": "AI Physical Security Service (AI surveillance-camera service on OPTiM Cloud IoT OS)", "model_version": null, "developer": "OPTiM Corporation", "deployer": "Kyushu Railway Company (JR Kyushu), trial at two stations", "location": "Wajiro (Kashii Line) and Futajima (Chikuho Main Line) stations, JR Kyushu, Japan", "domain": "Railway safety / station platform monitoring", "segment": "Station platform monitoring", "benefit_type": "Increased Product Safety", "taxonomy_note": "A trial announcement with no result, whose stated aims are station safety alerts and a lighter monitoring load for operators.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Only OPTiM's announcement exists. No trial result, alert count or staffing effect was published." }, { "group": "personal", "depth": null, "reason": "No passenger safety outcome is documented." } ], "ai_role": { "status": "unassessed", "statement": "No event is reported. The AI's role is described only in the vendor's launch release (real-time video monitoring, anomaly alerts, email to administrators). No account from JR Kyushu was found." }, "evidence": [ { "kind": "press release", "citation": "OPTiM Corporation (2017). AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始. Press release, 6 October 2017.", "title": "AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始", "url": "https://www.optim.co.jp/news-detail/23884", "date": "2017-10-06", "written_by": "makers", "quote": "ネットワークカメラなどで撮影されている映像をAI（人工知能）がリアルタイムで監視し、異常を検知した際にアラート表示をしたり、管理者へアラートメール送信を行う" }, { "kind": "press release", "citation": "OPTiM Corporation (2017). The same release republished by @Press, 6 October 2017.", "title": "「AI Physical Security Service」の実証実験を開始", "url": "https://www.atpress.ne.jp/news/139924", "date": "2017-10-06", "written_by": "makers", "quote": "10月10日よりJR九州香椎線和白駅、筑豊本線二島駅において実施" }, { "kind": "news report", "citation": "Kabutan / Fisco (2018). オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成. Kabutan market news, 27 June 2018 (reporting OPTiM's announcement of 26 June 2018).", "title": "オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成", "url": "https://kabutan.jp/news/marketnews/?b=n201806270161", "date": "2018-06-27", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Network cameras at two JR Kyushu stations streamed to OPTiM's cloud AI service", "Real-time analysis identifying people, trains and the platform yellow line and flagging anomalies", "Alert display and email to administrators" ], "must_hold": [ "Operators or administrators who act on the alerts; the release gives no response procedure", "Detection accuracy adequate for station use; no figures were published" ], "echoes": [] }, "paired_incident": null, "headline": "OPTiM trials AI camera monitoring at two JR Kyushu stations", "metric": null, "comparator": null, "later": { "status": "scaled", "date": "2018-06-26", "note": "OPTiM announced on 26 June 2018 that a trial of the same service with Itec Hankyu Hanshin at JR Sawa station on the Joban Line achieved a 96.2% detection rate of people or objects crossing the platform's yellow line while no train was present, against a 90% target; no result for the JR Kyushu stations was found.", "quote": "オプティム< 3694 >は26日、アイテック阪急阪神(本社:大阪府大阪市)と共同でJR常盤線佐和駅において、オプティムが提供する「AI Physical Security Service」の実証実験を実施し、目標を上回る検知率96.2％を達成したと発表。今回、目標として設定した検知率(電車が駅に不在時に、ホーム際の黄色い線より線路側に人もしくは物体が侵入した際にAIが検知した割合)は90％としていた。", "url": "https://kabutan.jp/news/marketnews/?b=n201806270161" }, "value": null, "developer_country": "Japan", "deployer_country": "Japan", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://kabutan.jp/news/marketnews/?b=n201806270161", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://ja.wikipedia.org/wiki/%E3%82%AA%E3%83%97%E3%83%86%E3%82%A3%E3%83%A0; https://en.wikipedia.org/wiki/Kyushu_Railway_Company", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report and two press releases from the makers. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0308", "catalog_version": "1.0.0", "source_fingerprint": "5426aa4b23877ccb217cf2223505c2c5d2cd656e33221de9c9bbced0042d7bff", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0308-source-0001", "AIBD-0308-source-0002", "AIBD-0308-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "OPTiM Corporation", "deployer": "Kyushu Railway Company (JR Kyushu), trial at two stations" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "No event is reported. The AI's role is described only in the vendor's launch release (real-time video monitoring, anomaly alerts, email to administrators). No account from JR Kyushu was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: one independent news report and two press releases from the makers. 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No trial result, alert count or staffing effect was published." }, { "group": "personal", "reason": "No passenger safety outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Only OPTiM's announcement exists. No trial result, alert count or staffing effect was published." }, { "group": "personal", "reason": "No passenger safety outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2018-06-26", "note": "OPTiM announced on 26 June 2018 that a trial of the same service with Itec Hankyu Hanshin at JR Sawa station on the Joban Line achieved a 96.2% detection rate of people or objects crossing the platform's yellow line while no train was present, against a 90% target; no result for the JR Kyushu stations was found.", "quote": "オプティム< 3694 >は26日、アイテック阪急阪神(本社:大阪府大阪市)と共同でJR常盤線佐和駅において、オプティムが提供する「AI Physical Security Service」の実証実験を実施し、目標を上回る検知率96.2％を達成したと発表。今回、目標として設定した検知率(電車が駅に不在時に、ホーム際の黄色い線より線路側に人もしくは物体が侵入した際にAIが検知した割合)は90％としていた。", "url": "https://kabutan.jp/news/marketnews/?b=n201806270161" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0308" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Increased Product Safety", "taxonomy_note": "A trial announcement with no result, whose stated aims are station safety alerts and a lighter monitoring load for operators." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OPTiM Corporation announced on 6 October 2017 a demonstration trial of its “AI Physical Security Service” from 10 October 2017. It would run at two Kyushu Railway Company (JR Kyushu) stations: 和白駅 (Wajiro) on the Kashii Line and 二島駅 (Futajima) on the Chikuho Main Line. The service, on “OPTiM Cloud IoT OS”, has AI monitor network-camera video in real time. When it detects an anomaly, it shows an alert and emails administrators. This is to reduce the burden on operators who watch many monitors and to raise safety in the station. The release includes an image of station camera analysis identifying people, trains and the yellow platform line. The release is the only source (on optim.co.jp and republished by @Press). JR Kyushu is not quoted, no trial end date, detection target list or result is given, and no later report of outcomes was found. Station romanizations are ours.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "Only OPTiM's announcement exists. No trial result, alert count or staffing effect was published." }, { "group": "personal", "reason": "No passenger safety outcome is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Trial of AI video monitoring to detect anomalies on station platforms and alert administrators. No detection event is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Wajiro (Kashii Line) and Futajima (Chikuho Main Line) stations, JR Kyushu, Japan", "developer_country": "Japan", "deployer_country": "Japan" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Trial of AI video monitoring to detect anomalies on station platforms and alert administrators. 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AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始. Press release, 6 October 2017.", "title": "AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始", "url": "https://www.optim.co.jp/news-detail/23884", "date": "2017-10-06", "written_by": "makers", "quote": "ネットワークカメラなどで撮影されている映像をAI（人工知能）がリアルタイムで監視し、異常を検知した際にアラート表示をしたり、管理者へアラートメール送信を行う" }, { "kind": "press release", "citation": "OPTiM Corporation (2017). The same release republished by @Press, 6 October 2017.", "title": "「AI Physical Security Service」の実証実験を開始", "url": "https://www.atpress.ne.jp/news/139924", "date": "2017-10-06", "written_by": "makers", "quote": "10月10日よりJR九州香椎線和白駅、筑豊本線二島駅において実施" }, { "kind": "news report", "citation": "Kabutan / Fisco (2018). オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成. Kabutan market news, 27 June 2018 (reporting OPTiM's announcement of 26 June 2018).", "title": "オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成", "url": "https://kabutan.jp/news/marketnews/?b=n201806270161", "date": "2018-06-27", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0308-source-0001", "AIBD-0308-source-0002", "AIBD-0308-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "OPTiM Corporation (2017). AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始. Press release, 6 October 2017.", "title": "AI（人工知能）を用いて異常を検知、JR九州の駅をご利用になるお客様のより高い安全性向上を目的とした「AI Physical Security Service」の実証実験を開始", "url": "https://www.optim.co.jp/news-detail/23884", "date": "2017-10-06", "written_by": "makers", "quote": "ネットワークカメラなどで撮影されている映像をAI（人工知能）がリアルタイムで監視し、異常を検知した際にアラート表示をしたり、管理者へアラートメール送信を行う" }, { "kind": "press release", "citation": "OPTiM Corporation (2017). The same release republished by @Press, 6 October 2017.", "title": "「AI Physical Security Service」の実証実験を開始", "url": "https://www.atpress.ne.jp/news/139924", "date": "2017-10-06", "written_by": "makers", "quote": "10月10日よりJR九州香椎線和白駅、筑豊本線二島駅において実施" }, { "kind": "news report", "citation": "Kabutan / Fisco (2018). オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成. Kabutan market news, 27 June 2018 (reporting OPTiM's announcement of 26 June 2018).", "title": "オプティム---「AI Physical Security Service」の実証実験を実施、目標を上回る検知率を達成", "url": "https://kabutan.jp/news/marketnews/?b=n201806270161", "date": "2018-06-27", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "OPTiM's release says its cloud AI monitors station camera video in real time, recognizing people, trains and the platform yellow line, and alerts administrators on anomalies; no results published.", "account": "OPTiM Corporation announced on 6 October 2017 a demonstration trial of its “AI Physical Security Service” from 10 October 2017. It would run at two Kyushu Railway Company (JR Kyushu) stations: 和白駅 (Wajiro) on the Kashii Line and 二島駅 (Futajima) on the Chikuho Main Line. The service, on “OPTiM Cloud IoT OS”, has AI monitor network-camera video in real time. When it detects an anomaly, it shows an alert and emails administrators. This is to reduce the burden on operators who watch many monitors and to raise safety in the station. The release includes an image of station camera analysis identifying people, trains and the yellow platform line. The release is the only source (on optim.co.jp and republished by @Press). JR Kyushu is not quoted, no trial end date, detection target list or result is given, and no later report of outcomes was found. Station romanizations are ours." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AI Physical Security Service (AI surveillance-camera service on OPTiM Cloud IoT OS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "OPTiM Corporation" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "OPTiM begins a trial of its “AI Physical Security Service” AI camera monitoring at two JR Kyushu stations, Wajiro and Futajima, from 10 October 2017", "headline": "OPTiM trials AI camera monitoring at two JR Kyushu stations" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "5426aa4b23877ccb217cf2223505c2c5d2cd656e33221de9c9bbced0042d7bff" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OPTiM Corporation announced on 6 October 2017 a demonstration trial of its “AI Physical Security Service” from 10 October 2017. It would run at two Kyushu Railway Company (JR Kyushu) stations: 和白駅 (Wajiro) on the Kashii Line and 二島駅 (Futajima) on the Chikuho Main Line. The service, on “OPTiM Cloud IoT OS”, has AI monitor network-camera video in real time. When it detects an anomaly, it shows an alert and emails administrators. This is to reduce the burden on operators who watch many monitors and to raise safety in the station. The release includes an image of station camera analysis identifying people, trains and the yellow platform line. The release is the only source (on optim.co.jp and republished by @Press). JR Kyushu is not quoted, no trial end date, detection target list or result is given, and no later report of outcomes was found. Station romanizations are ours.", "replication": { "produced_by": [ "Network cameras at two JR Kyushu stations streamed to OPTiM's cloud AI service", "Real-time analysis identifying people, trains and the platform yellow line and flagging anomalies", "Alert display and email to administrators" ], "must_hold": [ "Operators or administrators who act on the alerts; the release gives no response procedure", "Detection accuracy adequate for station use; no figures were published" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 26, "reviewed": 0, "record_mapped": 26, "not_assessed": 65, "complete": false, "by_state": { "value_present": 26, "not_reported": 0, "unresolved": 0, "not_assessed": 65, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0168", "case_url": "records/AIBD-0168.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "The system was built to forecast wind generation for Xcel's unit-commitment and trading decisions. The cost savings are its intended use." }, { "label": "AI contribution", "value": "The forecasting system's contribution to savings is Xcel's own estimate relayed by NCAR authors and reporters. The AI character of the system rests on UCAR's reference to artificial-intelligence research and DICast's adaptive bias correction, not on an explicit machine-learning description. So both the AI status and the causal share are unverified." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "United States" ] }, "record": { "id": "AIBD-0168", "title": "NCAR's DICast wind forecasts cut Xcel Energy's forecast error to 10.8%; Xcel estimates US$49 million saved", "summary": "NCAR's adaptive forecast-blending system (DICast), used by Xcel Energy since 2009, cut mean wind forecast error from about 18% to 10.8% by 2014; Xcel estimated US$49m saved (IEEE Spectrum 2015).", "date_observed": "2015-10-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Sue Ellen Haupt and William Mahoney of the National Center for Atmospheric Research wrote in IEEE Spectrum (22 October 2015). NCAR's wind power forecasting system for Xcel Energy blends numerical weather models with the Dynamic Integrated foreCast (DICast) system, used for wind prediction since 2009. DICast constantly checks its predictions against outcomes and adjusts its bias corrections and weightings. The mean forecast error at a typical wind farm fell from about 18 percent in 2008 to 10.8 percent by 2014. Xcel estimated the improvements had saved customers US$49 million so far. A UCAR news item (7 February 2012) said the system saved ratepayers $6 million in 2010 alone. It described the work as building on NCAR research ranging from statistics to artificial intelligence. E&E News (4 December 2017) quoted Haupt that the forecasts could save $10 million a year. It reported that Xcel operators decommit large coal units when sustained wind is predicted. None of the sources describes the system as machine learning in so many words. The savings are Xcel's estimates reported through NCAR and the press, not a regulator's audit.", "category": "general benefit", "unexpected": false, "purpose": "The system was built to forecast wind generation for Xcel's unit-commitment and trading decisions. The cost savings are its intended use.", "system": "NCAR wind power forecasting system with the DICast adaptive forecast blending engine", "model_version": null, "developer": "National Center for Atmospheric Research (Research Applications Laboratory)", "deployer": "Xcel Energy", "location": "United States", "domain": "Energy", "segment": "Utility wind generation forecasting", "benefit_type": "Improved Task Accuracy", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "Xcel's US$49 million estimate and the error reduction to 10.8% are reported by the makers and the press without independent measurement." }, { "group": "societal", "depth": null, "reason": "Ratepayer savings and coal-unit decommitment are reported as estimates. No audited figure is documented." } ], "ai_role": { "status": "unassessed", "statement": "The forecasting system's contribution to savings is Xcel's own estimate relayed by NCAR authors and reporters. The AI character of the system rests on UCAR's reference to artificial-intelligence research and DICast's adaptive bias correction, not on an explicit machine-learning description. So both the AI status and the causal share are unverified." }, "evidence": [ { "kind": "news report", "citation": "Sue Ellen Haupt and William P. 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NCAR's wind power forecasting system for Xcel Energy blends numerical weather models with the Dynamic Integrated foreCast (DICast) system, used for wind prediction since 2009. DICast constantly checks its predictions against outcomes and adjusts its bias corrections and weightings. The mean forecast error at a typical wind farm fell from about 18 percent in 2008 to 10.8 percent by 2014. Xcel estimated the improvements had saved customers US$49 million so far. A UCAR news item (7 February 2012) said the system saved ratepayers $6 million in 2010 alone. It described the work as building on NCAR research ranging from statistics to artificial intelligence. E&E News (4 December 2017) quoted Haupt that the forecasts could save $10 million a year. It reported that Xcel operators decommit large coal units when sustained wind is predicted. None of the sources describes the system as machine learning in so many words. The savings are Xcel's estimates reported through NCAR and the press, not a regulator's audit." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "NCAR wind power forecasting system with the DICast adaptive forecast blending engine" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "National Center for Atmospheric Research (Research Applications Laboratory)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "NCAR's DICast wind forecasts cut Xcel Energy's forecast error to 10.8%; Xcel estimates US$49 million saved", "headline": "NCAR's DICast cuts Xcel's wind forecast error to 10.8%" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "92c4d2686043aa5b140dbdb3ac0cf4b96c9165cb9413720f6d10f581734a2332" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Sue Ellen Haupt and William Mahoney of the National Center for Atmospheric Research wrote in IEEE Spectrum (22 October 2015). 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The savings are Xcel's estimates reported through NCAR and the press, not a regulator's audit.", "replication": { "produced_by": [ "Multiple numerical weather models blended with an adaptive statistical layer trained on each wind farm's history", "Forecast updates every 15 minutes feeding unit-commitment decisions", "A utility with large wind capacity and dispatchable coal and gas units to decommit" ], "must_hold": [ "Continuous observed generation data for bias correction", "Operators who act on forecast confidence bands", "An independent accounting of avoided production costs, absent here" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0187", "case_url": "records/AIBD-0187.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Detect, investigate and prosecute complex health care fraud schemes." }, { "label": "AI contribution", "value": "DOJ attributes detection to proactive data analytics. The reviewed sources do not establish that AI was used. The AI-enabled Data Fusion Center was announced separately as future work." } ], "filters": { "topic": "Financial security", "benefit": "reported", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0187", "title": "Medicare withholds $4.41 billion in suspected fraudulent payments", "summary": "Federal analysts flagged unusual billing, and HHS-OIG and CMS held $4.41 billion in escrow during Operation Gold Rush.", "date_observed": "2025-06-30", "date_basis": "source publication", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "DOJ reported that analysts detected unusual billing in Operation Gold Rush. HHS-OIG and CMS held $4.41 billion in escrow from about $4.45 billion scheduled for payment. Medicare paid about $41 million, and supplemental insurers paid about $900 million. DOJ stated that HHS and CMS intended to seek the return of the escrow to the Medicare trust fund. The announcement also proposed an AI-enabled Data Fusion Center. 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These developments do not establish an AI contribution or the final disposition of the $4.41 billion in escrow.", "quote": null, "url": "https://www.justice.gov/opa/pr/ohio-man-pleads-guilty-laundering-health-care-fraud-proceeds-transnational-criminal" }, "value": { "amount": "$4.41 billion", "what": "fraudulent Medicare payments held in escrow rather than paid out, to be returned to the Medicare trust fund", "who": "Medicare trust fund (HHS and CMS)", "estimate": false, "quote": "HHS and CMS intend to seek to return the $4.41 billion in escrow to the Medicare trust fund for needed medical care.", "url": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146" }, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146; https://www.justice.gov/opa/pr/brooklyn-banker-pleads-guilty-laundering-proceeds-medicare-fraud-transnational-criminal; https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.justice.gov/contact-us; https://www.justice.gov/contact-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Read the original DOJ announcements and linked indictment; separated billing analytics from the proposed AI-enabled Fusion Center, clarified escrow and losses, and added the August 2026 prosecution update.", "source": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's court document and three press releases from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0187", "catalog_version": "1.0.0", "source_fingerprint": "97b6aaeb2665f23ec816fe89bd7d400e53a9861274835d537c0b95d803e7f548", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "querist", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "DIV-0026" ], "role": "monetary observations" }, "provenance": { "kind": "source_review", "basis": "DOJ reports a gross payment hold, without an established AI contribution.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "record_ids": [ "DIV-0026" ] }, "institutional decisions": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "25-cr-203" ], "role": "institutional decisions" }, "provenance": { "kind": "source_review", "basis": "DOJ announcement and the linked indictment.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "record_ids": [ "25-cr-203" ] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "text", "text": "DOJ’s Health Care Fraud Unit and partners analyzed billing. HHS-OIG and CMS suspended payments." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ’s Health Care Fraud Unit and partners analyzed billing. HHS-OIG and CMS suspended payments." } }, "actors[].ownership_control": { "state": "value_present", "value": { "kind": "text", "text": "The named teams are U.S. federal agencies. The sources do not identify a private software owner." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The named teams are U.S. federal agencies. The sources do not identify a private software owner." } }, "ai_contribution": { "state": "value_present", "value": { "kind": "text", "text": "DOJ credits proactive data analytics. It does not identify an AI component or measure its separate contribution." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ credits proactive data analytics. It does not identify an AI component or measure its separate contribution." } }, "ai_relationship": { "state": "unresolved", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "Whether the billing analysis used AI remains unresolved.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Whether the billing analysis used AI remains unresolved." } }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "The release states that $4.41 billion scheduled to be paid by Medicare was held in escrow for return to the Medicare trust fund. Supplemental insurers still paid about $900 million." }, { "group": "organizational", "depth": 3, "reason": "CMS and HHS-OIG stopped payment of all but about $41 million of $4.45 billion in claims from the organization, per the release." }, { "group": "personal", "reason": "The release describes over one million stolen identities but documents no outcome for the individual beneficiaries." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "text", "text": "An outline: the payment figure comes from the participating agencies, and an AI contribution remains unresolved." }, "provenance": { "kind": "curatorial", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "An outline: the payment figure comes from the participating agencies, and an AI contribution remains unresolved." } }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "The release states that $4.41 billion scheduled to be paid by Medicare was held in escrow for return to the Medicare trust fund. Supplemental insurers still paid about $900 million." }, { "group": "organizational", "depth": 3, "reason": "CMS and HHS-OIG stopped payment of all but about $41 million of $4.45 billion in claims from the organization, per the release." }, { "group": "personal", "reason": "The release describes over one million stolen identities but documents no outcome for the individual beneficiaries." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "The payment hold is reported as completed. Returning the escrow to the Medicare trust fund was still intended." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The payment hold is reported as completed. Returning the escrow to the Medicare trust fund was still intended." } }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "The release states that $4.41 billion scheduled to be paid by Medicare was held in escrow for return to the Medicare trust fund. Supplemental insurers still paid about $900 million." }, { "group": "organizational", "depth": 3, "reason": "CMS and HHS-OIG stopped payment of all but about $41 million of $4.45 billion in claims from the organization, per the release." }, { "group": "personal", "reason": "The release describes over one million stolen identities but documents no outcome for the individual beneficiaries." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not provide public access to the analytic system.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not provide public access to the analytic system." } }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "value_present", "value": { "kind": "text", "text": "A federal fraud investigation and administrative payment suspension." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A federal fraud investigation and administrative payment suspension." } }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "value_present", "value": { "kind": "text", "text": "The June 2025 announcement supports the payment figures. The indictment describes the alleged scheme. The later releases add prosecution updates." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The June 2025 announcement supports the payment figures. The indictment describes the alleged scheme. The later releases add prosecution updates." } }, "claims[].evidence_strength": { "state": "value_present", "value": { "kind": "text", "text": "Official accounts support the withheld-payment figure and prosecution updates. They do not establish that AI produced the benefit." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Official accounts support the withheld-payment figure and prosecution updates. They do not establish that AI produced the benefit." } }, "claims[].missingness": { "state": "value_present", "value": { "kind": "text", "text": "The algorithm, AI contribution, operating costs, false-positive rate, and final escrow disposition remain unestablished." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The algorithm, AI contribution, operating costs, false-positive rate, and final escrow disposition remain unestablished." } }, "claims[].proposition": { "state": "value_present", "value": { "kind": "text", "text": "Billing analysis contributed to the detection of a scheme whose scheduled payments were largely suspended." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Billing analysis contributed to the detection of a scheme whose scheduled payments were largely suspended." } }, "cross_border_flows[]": { "state": "value_present", "value": { "kind": "text", "text": "DOJ describes criminal transfers to overseas companies and accounts. It does not document cross-border movement of the analytic system, its training data, or its computing resources." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ describes criminal transfers to overseas companies and accounts. It does not document cross-border movement of the analytic system, its training data, or its computing resources." } }, "decisions[].action_outcome": { "state": "value_present", "value": { "kind": "text", "text": "Payments were suspended. The June 2025 indictment and later guilty pleas concern the alleged criminal scheme." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Payments were suspended. The June 2025 indictment and later guilty pleas concern the alleged criminal scheme." } }, "decisions[].grounds": { "state": "value_present", "value": { "kind": "text", "text": "Anomalous billing and evidence of fraudulent claims were the stated grounds. The sources do not give a separate test of AI’s contribution." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Anomalous billing and evidence of fraudulent claims were the stated grounds. The sources do not give a separate test of AI’s contribution." } }, "decisions[].institution_task": { "state": "value_present", "value": { "kind": "text", "text": "HHS-OIG and CMS suspended payments while DOJ and its partners investigated the alleged fraud." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "HHS-OIG and CMS suspended payments while DOJ and its partners investigated the alleged fraud." } }, "decisions[].success_criterion": { "state": "value_present", "value": { "kind": "text", "text": "The announcements describe preventing improper payments and prosecuting fraud. They do not specify a prospective numerical threshold for success." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The announcements describe preventing improper payments and prosecuting fraud. They do not specify a prospective numerical threshold for success." } }, "deployment_stage": { "state": "value_present", "value": { "kind": "text", "text": "Operational billing analysis supported an active investigation." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Operational billing analysis supported an active investigation." } }, "economics[].amount": { "state": "value_present", "value": { "kind": "text", "text": "US$4.41 billion held in escrow." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "US$4.41 billion held in escrow." } }, "economics[].costs[]": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not quantify operating or enforcement costs.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not quantify operating or enforcement costs." } }, "economics[].counterfactual": { "state": "value_present", "value": { "kind": "text", "text": "About $4.45 billion was scheduled for payment; about $41 million was paid. This accounting comparison does not isolate AI’s effect." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "About $4.45 billion was scheduled for payment; about $41 million was paid. This accounting comparison does not isolate AI’s effect." } }, "economics[].currency_price_basis": { "state": "value_present", "value": { "kind": "text", "text": "U.S. dollars. The figures are nominal; no inflation adjustment is stated." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "U.S. dollars. The figures are nominal; no inflation adjustment is stated." } }, "economics[].gross_net": { "state": "value_present", "value": { "kind": "text", "text": "The figure is gross money withheld. It does not deduct investigation, prosecution, or administrative costs." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The figure is gross money withheld. It does not deduct investigation, prosecution, or administrative costs." } }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "text", "text": "Scheduled payments withheld in escrow, rather than a realized net saving." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Scheduled payments withheld in escrow, rather than a realized net saving." } }, "economics[].period_unit": { "state": "value_present", "value": { "kind": "text", "text": "The Operation Gold Rush payment hold reported on June 30, 2025. The exact interval covered by the total is not specified." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The Operation Gold Rush payment hold reported on June 30, 2025. The exact interval covered by the total is not specified." } }, "economics[].realization": { "state": "value_present", "value": { "kind": "text", "text": "$4.41 billion was held in escrow. Returning it to the trust fund remained an intention in the announcement." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "$4.41 billion was held in escrow. Returning it to the trust fund remained an intention in the announcement." } }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "text", "text": "Medicare was the scheduled payer; the Medicare trust fund was the intended recipient of returned escrow." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Medicare was the scheduled payer; the Medicare trust fund was the intended recipient of returned escrow." } }, "economics[].return_payback": { "state": "unresolved", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "A return or payback period cannot be calculated from the reviewed figures because costs and an AI contribution are unestablished.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A return or payback period cannot be calculated from the reviewed figures because costs and an AI contribution are unestablished." } }, "economics[].valuation_method": { "state": "value_present", "value": { "kind": "text", "text": "DOJ reports the scheduled, paid, and escrowed amounts. No monetary valuation model is described." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ reports the scheduled, paid, and escrowed amounts. No monetary valuation model is described." } }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "HHS-OIG and CMS prevented all but $41 million of $4.45 billion", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "text", "text": "United States v. Nakhmatullaev et al., No. 25-cr-203." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "United States v. Nakhmatullaev et al., No. 25-cr-203." } }, "follow_up[]": { "state": "value_present", "value": { "kind": "text", "text": "DOJ reported 35 people charged and 16 convicted by August 14, 2026, following a money-laundering guilty plea the previous day. These developments do not establish an AI contribution or the final disposition of the $4.41 billion in escrow." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0002", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ reported 35 people charged and 16 convicted by August 14, 2026, following a money-laundering guilty plea the previous day. These developments do not establish an AI contribution or the final disposition of the $4.41 billion in escrow." } }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Federal analysts, investigators, prosecutors, and payment administrators performed distinct roles. The sources do not describe a specific review protocol for software alerts." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Federal analysts, investigators, prosecutors, and payment administrators performed distinct roles. The sources do not describe a specific review protocol for software alerts." } }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0187" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "value_present", "value": { "kind": "text", "text": "The investigation required Medicare billing records, access to payment administration, and cooperation among federal agencies." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The investigation required Medicare billing records, access to payment administration, and cooperation among federal agencies." } }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Medicare and the public funds that support it." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Medicare and the public funds that support it." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "More than one million people had identities stolen. That is a count of people affected by the alleged fraud, not a measured count of people helped by AI." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0003" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "More than one million people had identities stolen. That is a count of people affected by the alleged fraud, not a measured count of people helped by AI." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "text", "text": "Avoided payment of suspected fraudulent claims." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Avoided payment of suspected fraudulent claims." } }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "text", "text": "$4.41 billion in scheduled Medicare payments was held in escrow." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "$4.41 billion in scheduled Medicare payments was held in escrow." } }, "outcomes[].distribution": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The announcement does not measure how the payment hold benefited particular beneficiaries or communities.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The announcement does not measure how the payment hold benefited particular beneficiaries or communities." } }, "outcomes[].duration": { "state": "value_present", "value": { "kind": "text", "text": "The money was held by June 30, 2025. The reviewed sources do not establish the final disposition of the escrow." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The money was held by June 30, 2025. The reviewed sources do not establish the final disposition of the escrow." } }, "outcomes[].id": { "state": "value_present", "value": { "kind": "text", "text": "AIBD-0187-outcome-01" }, "provenance": { "kind": "curatorial", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0187-outcome-01" } }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "depth": 4, "reason": "The release states that $4.41 billion scheduled to be paid by Medicare was held in escrow for return to the Medicare trust fund. Supplemental insurers still paid about $900 million." }, { "group": "organizational", "depth": 3, "reason": "CMS and HHS-OIG stopped payment of all but about $41 million of $4.45 billion in claims from the organization, per the release." }, { "group": "personal", "reason": "The release describes over one million stolen identities but documents no outcome for the individual beneficiaries." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "value_present", "value": { "kind": "text", "text": "The payment suspension occurred. Return of the escrow to the Medicare trust fund remained intended." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The payment suspension occurred. Return of the escrow to the Medicare trust fund remained intended." } }, "outcomes[].tradeoffs": { "state": "value_present", "value": { "kind": "text", "text": "About $41 million reached the organization through Medicare, and supplemental insurers paid about $900 million. Enforcement costs and effects on legitimate payments are not quantified." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "About $41 million reached the organization through Medicare, and supplemental insurers paid about $900 million. Enforcement costs and effects on legitimate payments are not quantified." } }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "text", "text": "Preventing fraudulent payments was an intended purpose of the analysis." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Preventing fraudulent payments was an intended purpose of the analysis." } }, "places[]": { "state": "value_present", "value": { "kind": "structured", "data": { "developer_country": "United States", "deployer_country": "United States", "location": "United States", "answer": "The analysis and payment administration occurred in the United States. The investigation involved overseas arrests and transfers." } }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The analysis and payment administration occurred in the United States. The investigation involved overseas arrests and transfers." } }, "purpose": { "state": "value_present", "value": { "kind": "text", "text": "Detect health care fraud before scheduled payments leave Medicare." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Detect health care fraud before scheduled payments leave Medicare." } }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-06-30", "date_basis": "source publication", "date_precision": "day", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146; https://www.justice.gov/opa/pr/brooklyn-banker-pleads-guilty-laundering-proceeds-medicare-fraud-transnational-criminal; https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.justice.gov/contact-us; https://www.justice.gov/contact-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Read the original DOJ announcements and linked indictment; separated billing analytics from the proposed AI-enabled Fusion Center, clarified escrow and losses, and added the August 2026 prosecution update.", "source": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not describe an independent replication of the billing analysis.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not describe an independent replication of the billing analysis." } }, "replication.conditions": { "state": "value_present", "value": { "kind": "text", "text": "The agencies needed billing data and the authority to stop payments before the money was transferred." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The agencies needed billing data and the authority to stop payments before the money was transferred." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; 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facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Read the original DOJ announcements and linked indictment; separated billing analytics from the proposed AI-enabled Fusion Center, clarified escrow and losses, and added the August 2026 prosecution update.", "source": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "text", "text": "Public health insurance and fraud enforcement." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Public health insurance and fraud enforcement." } }, "sources[].access_version": { "state": "value_present", "value": { "kind": "text", "text": "DOJ’s original online releases and the June 18, 2025 indictment were inspected on October 25, 2026." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ’s original online releases and the June 18, 2025 indictment were inspected on October 25, 2026." } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "U.S. Department of Justice, Office of Public Affairs, “National Health Care Fraud Takedown Results in 324 Defendants Charged in Connection with Over $14.6 Billion in Alleged Fraud,” June 30, 2025, § “Transnational Criminal Organizations.”", "title": "National Health Care Fraud Takedown Results in 324 Defendants Charged in Connection with Over $14.6 Billion in Alleged Fraud", "url": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "date": "2025-06-30", "written_by": "deployer", "quote": "The Health Care Fraud Unit's Data Analytics Team and its partners detected the anomalous billing through proactive data analytics" }, { "kind": "press release", "citation": "U.S. Department of Justice, Office of Public Affairs, press release, 3 February 2026. Brooklyn Banker Pleads Guilty to Laundering Proceeds of Medicare Fraud for Transnational Criminal Organization.", "title": "Brooklyn Banker Pleads Guilty to Laundering Proceeds of Medicare Fraud for Transnational Criminal Organization", "url": "https://www.justice.gov/opa/pr/brooklyn-banker-pleads-guilty-laundering-proceeds-medicare-fraud-transnational-criminal", "date": "2026-02-03", "written_by": "deployer" }, { "kind": "court document", "citation": "United States v. Nakhmatullaev et al., No. 25-cr-203, indictment, June 18, 2025, 1–10, ¶¶ 1–20, 32–34 (E.D.N.Y.).", "title": "United States v. Nakhmatullaev et al., indictment", "url": "https://www.justice.gov/criminal/media/1405326/dl?inline=", "date": "2025-06-18", "written_by": "deployer" }, { "kind": "press release", "citation": "U.S. Department of Justice, Office of Public Affairs, “Ohio Man Pleads Guilty to Laundering Health Care Fraud Proceeds for Transnational Criminal Organization,” August 14, 2026, ¶¶ 1–9.", "title": "Ohio Man Pleads Guilty to Laundering Health Care Fraud Proceeds for Transnational Criminal Organization", "url": "https://www.justice.gov/opa/pr/ohio-man-pleads-guilty-laundering-health-care-fraud-proceeds-transnational-criminal", "date": "2026-08-14", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "role": "Retained sources" }, "provenance": { "kind": "curatorial", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "4 retained sources." } }, "sources[].language": { "state": "value_present", "value": { "kind": "text", "text": "English." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "English." } }, "sources[].relationship": { "state": "value_present", "value": { "kind": "text", "text": "DOJ reports its own investigation and prosecutions. The indictment records prosecutors’ allegations, not a judicial finding about an AI benefit." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ reports its own investigation and prosecutions. The indictment records prosecutors’ allegations, not a judicial finding about an AI benefit." } }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "lead_source_ref": "AIBD-0187-source-0001", "retained_source_order": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "origins": [ { "source_refs": [ "AIBD-0187-source-0001" ], "display_name": "U.S. Department of Justice", "url": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146", "contribution": "Reports the billing detection, scheduled payments, escrow, and losses. Announces the future AI-enabled center separately.", "locator": "§§ “Transnational Criminal Organizations” and “Breaking Down Silos in the Fight Against Health Care Fraud.”" }, { "source_refs": [ "AIBD-0187-source-0002" ], "display_name": "U.S. Department of Justice", "url": "https://www.justice.gov/opa/pr/brooklyn-banker-pleads-guilty-laundering-proceeds-medicare-fraud-transnational-criminal", "contribution": "Documents the banker’s guilty plea on February 3, 2026.", "locator": "¶¶ 1–5." }, { "source_refs": [ "AIBD-0187-source-0003" ], "display_name": "U.S. Department of Justice", "url": "https://www.justice.gov/criminal/media/1405326/dl?inline=", "contribution": "Original indictment underlying the announcement. Describes alleged ownership, billing, and money laundering; it does not establish an AI method.", "locator": "1–10, ¶¶ 1–20, 32–34." }, { "source_refs": [ "AIBD-0187-source-0004" ], "display_name": "U.S. Department of Justice", "url": "https://www.justice.gov/opa/pr/ohio-man-pleads-guilty-laundering-health-care-fraud-proceeds-transnational-criminal", "contribution": "Reports 35 people charged and 16 convicted by August 14, 2026.", "locator": "¶¶ 1–9." } ], "limitations": [ "No reviewed source identifies an AI component in the billing detection.", "The announcements do not document a final return of the escrowed money to the trust fund." ] } }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001", "AIBD-0187-source-0002", "AIBD-0187-source-0003", "AIBD-0187-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ is the original source for the payment figures. Its linked indictment describes the alleged scheme; later announcements document guilty pleas." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "text", "text": "Federal analysts flagged unusual billing, and HHS-OIG and CMS held $4.41 billion in escrow during Operation Gold Rush." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Federal analysts flagged unusual billing, and HHS-OIG and CMS held $4.41 billion in escrow during Operation Gold Rush." } }, "systems[].autonomy_permissions": { "state": "value_present", "value": { "kind": "text", "text": "HHS-OIG and CMS controlled payment suspensions. The sources do not describe an autonomous system that could withhold funds." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "HHS-OIG and CMS controlled payment suspensions. The sources do not describe an autonomous system that could withhold funds." } }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The analysts detected unusual billing. HHS-OIG and CMS then stopped most scheduled payments." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The analysts detected unusual billing. HHS-OIG and CMS then stopped most scheduled payments." } }, "systems[].deployment_configuration": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not specify the software, hosting, or runtime settings.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not specify the software, hosting, or runtime settings." } }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "Analysts examined health care billing and identified anomalous patterns. The model inputs and alert format are not specified." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Analysts examined health care billing and identified anomalous patterns. The model inputs and alert format are not specified." } }, "systems[].model_name": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not identify a model.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not identify a model." } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not identify a model release.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not identify a model release." } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "text", "text": "DOJ Health Care Fraud Unit proactive billing analytics" }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "DOJ Health Care Fraud Unit proactive billing analytics" } }, "systems[].provider": { "state": "value_present", "value": { "kind": "text", "text": "The Department of Justice identifies its Health Care Fraud Unit Data Analytics Team and partners; it names no software supplier." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The Department of Justice identifies its Health Care Fraud Unit Data Analytics Team and partners; it names no software supplier." } }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "text", "text": "Medicare withholds $4.41 billion in suspected fraudulent payments" }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Medicare withholds $4.41 billion in suspected fraudulent payments" } }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "97b6aaeb2665f23ec816fe89bd7d400e53a9861274835d537c0b95d803e7f548" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Content fingerprint refreshed after the October 25, 2026 source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-25" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "text", "text": "Analysts detected anomalous billing; investigators examined the scheme, and HHS-OIG and CMS withheld scheduled payments." }, "provenance": { "kind": "source_review", "basis": "Original DOJ announcements and the linked indictment, reviewed October 25, 2026.", "record_fields": [], "source_refs": [ "AIBD-0187-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Analysts detected anomalous billing; investigators examined the scheme, and HHS-OIG and CMS withheld scheduled payments." } } }, "summary": { "total": 91, "addressed": 91, "known": 64, "reviewed": 64, "record_mapped": 9, "not_assessed": 18, "complete": false, "by_state": { "value_present": 64, "not_reported": 7, "unresolved": 2, "not_assessed": 18, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0210", "case_url": "records/AIBD-0210.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "The unnamed AI tool flagged suspected fraud for human review. Staff investigation and contact with the participant’s family were necessary to stop the money from moving." }, { "label": "AI contribution", "value": "Duckett attributes the initial flag to AI and the successful intervention to a portfolio manager, the fraud team, and the participant’s daughter. The transcript does not establish the model’s design, the requested transfer amount, or what would have happened without its flag." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0210", "title": "TIAA’s AI flags a suspected scam involving a 76-year-old’s retirement savings, and staff stop the money from moving", "summary": "TIAA’s CEO said that an AI flag, staff intervention, and contact with a participant’s daughter protected retirement savings of more than $3 million.", "date_observed": "2026-06-17", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "TIAA CEO Thasunda Brown Duckett told Fortune that an AI alert helped staff stop a scam involving a 76-year-old participant’s retirement savings. A portfolio manager referred the concern to the fraud team, which spent hours with the participant and contacted his daughter. Duckett said that the money did not move. The interview describes retirement holdings above $3 million, but does not itemize an attempted transfer. The original episode was released on June 17, 2026, with its transcript published five days later. No participant statement, transaction record, or separate investigation is supplied.", "category": "harm prevented", "unexpected": false, "purpose": "The unnamed AI tool flagged suspected fraud for human review. Staff investigation and contact with the participant’s family were necessary to stop the money from moving.", "system": "Unnamed TIAA AI tool used to flag suspected fraud for human review", "model_version": null, "developer": null, "deployer": "TIAA (Teachers Insurance and Annuity Association of America)", "location": null, "domain": "Financial services / retirement accounts / fraud prevention", "segment": "Retirement savings provider for educators, healthcare workers and public servants", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-06-17-b80b" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The CEO describes a 76-year-old retaining retirement savings of more than $3 million after a suspected scam was interrupted. The participant is unnamed, and no transaction record or independent account establishes the amount at risk or the counterfactual loss." }, { "group": "organizational", "depth": 1, "reason": "The CEO describes a successful fraud intervention by TIAA staff. The source does not quantify TIAA’s own avoided cost or the cost of the intervention." } ], "ai_role": { "status": "unassessed", "statement": "Duckett attributes the initial flag to AI and the successful intervention to a portfolio manager, the fraud team, and the participant’s daughter. The transcript does not establish the model’s design, the requested transfer amount, or what would have happened without its flag." }, "evidence": [ { "kind": "other", "citation": "Thasunda Brown Duckett, interview by Alyson Shontell, Fortune 500: Titans and Disruptors of Industry, released June 17, 2026; lightly edited transcript published by Fortune Editors on June 22, 2026, “How TIAA is approaching AI—and whether it threatens the retirement market.”", "title": "America has a $4 trillion retirement crisis and half of workers could run out of money. TIAA’s CEO wants ‘guaranteed lifetime income’ to fix it", "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "date": "2026-06-22", "written_by": "deployer", "quote": "Our AI tool flagged it." } ], "replication": { "produced_by": [ "An AI alert that a portfolio manager reviewed and referred to the fraud team", "Staff who spent hours discussing the suspected scam with the participant", "Contact with a family member who could help stop the money from moving" ], "must_hold": [ "The flagged request must actually be fraud. Duckett gives no false-positive rate, and the scam itself is not described", "The firm must be able to hold a transfer while it persuades the customer. Duckett says AI by itself would not necessarily have protected him", "The customer or a relative must be reachable and open to persuasion before the funds move" ], "echoes": [] }, "paired_incident": null, "headline": "TIAA’s AI may have helped stop a scam involving retirement savings", "metric": "retirement savings above $3 million retained, according to TIAA’s CEO", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The June 22 transcript adds the CEO’s own wording. No subsequent participant-level outcome is established.", "quote": null, "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/" }, "value": { "amount": "more than $3 million", "what": "participant’s reported retirement holdings; exact attempted transfer and avoided loss not itemized", "who": "the 76-year-old TIAA participant", "estimate": false, "quote": "That was $3 million. Someone’s entire retirement.", "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/" }, "developer_country": null, "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-06-17-b80b", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added the value as stated from the cited sources.", "source": "https://fortune.com/2026/06/17/tiaa-saved-retiree-from-scam-using-artifical-intelligence-human-centered-work-ceo-thasunda-brown-duckett/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/TIAA", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "source": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's other. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0210", "catalog_version": "1.0.0", "source_fingerprint": "7740ef13b31747812ace838f969427f4de13b1270cb566200e7b1d319b6ec852", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0210-source-0001" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0210-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "deployer": "TIAA (Teachers Insurance and Annuity Association of America)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "unassessed", "statement": "Duckett attributes the initial flag to AI and the successful intervention to a portfolio manager, the fraud team, and the participant’s daughter. 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Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The CEO describes a 76-year-old retaining retirement savings of more than $3 million after a suspected scam was interrupted. The participant is unnamed, and no transaction record or independent account establishes the amount at risk or the counterfactual loss." }, { "group": "organizational", "depth": 1, "reason": "The CEO describes a successful fraud intervention by TIAA staff. The source does not quantify TIAA’s own avoided cost or the cost of the intervention." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the deployer's other. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Recomputed from the retained evidence using the existing display rule. Source originality is documented separately and does not imply independent verification.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The CEO describes a 76-year-old retaining retirement savings of more than $3 million after a suspected scam was interrupted. The participant is unnamed, and no transaction record or independent account establishes the amount at risk or the counterfactual loss." }, { "group": "organizational", "depth": 1, "reason": "The CEO describes a successful fraud intervention by TIAA staff. The source does not quantify TIAA’s own avoided cost or the cost of the intervention." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "TIAA’s CEO reports that staff interrupted a suspected scam involving a 76-year-old’s retirement savings. No transaction record or independent participant account verifies the amount at risk. The sources do not measure TIAA’s own savings.", "editor_note": "" } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "The CEO describes a 76-year-old retaining retirement savings of more than $3 million after a suspected scam was interrupted. The participant is unnamed, and no transaction record or independent account establishes the amount at risk or the counterfactual loss." }, { "group": "organizational", "depth": 1, "reason": "The CEO describes a successful fraud intervention by TIAA staff. The source does not quantify TIAA’s own avoided cost or the cost of the intervention." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. 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The transcript, video episode, Apple Podcasts listing, and news retellings are versions or reports of that single account." } ], "excluded_republications": [ { "url": "https://fortune.com/2026/06/17/tiaa-saved-retiree-from-scam-using-artifical-intelligence-human-centered-work-ceo-thasunda-brown-duckett/", "citation": "Fore P (2026). A 76-year-old was about to lose his entire $3 million retirement to a scam. TIAA's AI caught it, but a human prevented disaster. Fortune, 17 June 2026, 4:30 AM ET.", "relationship": "Fortune’s June 17 article and IBTimes summarize the CEO’s same interview. Apple Podcasts supplies metadata for that work. They are not separate corroboration." }, { "url": "https://www.ibtimes.co.uk/ai-human-teamwork-saves-retiree-scam-1803451", "citation": "Cruz S (2026). 76-Year-Old Nearly Lost His $3 Million Retirement to a Scam: AI Flagged the Fraud. International Business Times UK, 18 June 2026, 8:07 AM BST; re-reports Fortune.", "relationship": "Fortune’s June 17 article and IBTimes summarize the CEO’s same interview. Apple Podcasts supplies metadata for that work. They are not separate corroboration." }, { "url": "https://podcasts.apple.com/us/podcast/%244-trillion-savings-gap-tiaas-ceo-on-the-retirement-crisis/id1836231455?i=1000773091573", "citation": "Fortune (2026). $4 Trillion Savings Gap: TIAA's CEO on The Retirement Crisis. Titans and Disruptors of Industry podcast, episode page on Apple Podcasts, released 17 June 2026; host Alyson Shontell, guest Thasunda Brown Duckett.", "relationship": "Fortune’s June 17 article and IBTimes summarize the CEO’s same interview. Apple Podcasts supplies metadata for that work. They are not separate corroboration." } ], "lead_source_ref": "AIBD-0210-source-0001", "retained_source_order": [ "AIBD-0210-source-0001" ], "ordering_basis": "The first source directly supports the reported benefit or is the closest accessible original account. Each additional source has a distinct evidentiary contribution described in its relationship. Documented inaccessible origins are not represented as having been read.", "claim_lineage": [ { "claim": "An AI flag was followed by staff intervention and contact with the participant’s daughter, after which the money did not move.", "source_ref": "AIBD-0210-source-0001", "source_chain": [ { "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "locator": "“How TIAA is approaching AI,” participant example beginning “We have a participant…”", "role": "Brown Duckett’s interview is the original account. Fortune is an independent publisher, but the case testimony is the deployer’s own. The transcript, video episode, Apple Podcasts listing, and news retellings are versions or reports of that single account." } ], "locator": "“How TIAA is approaching AI,” participant example beginning “We have a participant…”", "method": {}, "limitations": [ "The participant, event date, location, scam type, model, and transaction records are not identified." ] }, { "claim": "The participant held more than $3 million in retirement savings.", "source_ref": "AIBD-0210-source-0001", "source_chain": [ { "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "locator": "Same interview passage; “over $3 million” and interviewer’s summary", "role": "Brown Duckett’s interview is the original account. Fortune is an independent publisher, but the case testimony is the deployer’s own. The transcript, video episode, Apple Podcasts listing, and news retellings are versions or reports of that single account." } ], "locator": "Same interview passage; “over $3 million” and interviewer’s summary", "method": {}, "limitations": [ "The transcript does not itemize the attempted transfer.", "The portfolio balance is not automatically an independently established net loss avoided." ] }, { "claim": "The interview was originally released June 17 and transcribed June 22.", "source_ref": "AIBD-0210-source-0001", "source_chain": [ { "url": "https://video.fortune.com/media/452d84c7-1fe6-41aa-9716-89bb11886530?activePlaylistName=Fortune+500+Titans+and+Disruptors+of+Industry", "role": "original episode metadata, June 17, 2026; video not watched" }, { "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "role": "lightly edited full transcript, June 22, 2026; read" } ], "locator": "Fortune episode metadata and transcript dateline", "method": {}, "limitations": [] } ], "limitations": [ "The video itself was not watched. The complete written transcript and episode metadata were read.", "There is no independent participant-level verification.", "Fructus should distinguish the reported retirement holdings from a verified attempted transfer or net benefit." ], "access_audit": [ { "url": "https://fortune.com/2026/06/22/tiaa-ceo-thasunda-brown-duckett-retirement-crisis-annuity-income-america/", "access": "complete lightly edited transcript read", "version": "June 22, 2026" }, { "url": "https://video.fortune.com/media/452d84c7-1fe6-41aa-9716-89bb11886530?activePlaylistName=Fortune+500+Titans+and+Disruptors+of+Industry", "access": "publication metadata read; video not watched", "version": "June 17, 2026; page updated August 13" } ], "related_material": [], "scope": "Complete review of retained-source lineage and access status. This does not mean that inaccessible upstream documents were inspected, that every required case field is complete, or that the original claims were independently verified.", "chronology_source": { "url": "https://video.fortune.com/media/452d84c7-1fe6-41aa-9716-89bb11886530?activePlaylistName=Fortune+500+Titans+and+Disruptors+of+Industry", "title": "Original Fortune interview episode", "date": "2026-06-17" } } }, "provenance": { "kind": "source_review", "basis": "Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss. Access limits are enumerated in the review.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "TIAA’s CEO gives the account in an interview with a complete transcript. No participant account or transaction record independently verifies the savings at risk or the amount of any attempted transfer.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "TIAA’s CEO said that an AI flag, staff intervention, and contact with a participant’s daughter protected retirement savings of more than $3 million.", "account": "TIAA CEO Thasunda Brown Duckett told Fortune that an AI alert helped staff stop a scam involving a 76-year-old participant’s retirement savings. A portfolio manager referred the concern to the fraud team, which spent hours with the participant and contacted his daughter. Duckett said that the money did not move. The interview describes retirement holdings above $3 million, but does not itemize an attempted transfer. The original episode was released on June 17, 2026, with its transcript published five days later. No participant statement, transaction record, or separate investigation is supplied." } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Unnamed TIAA AI tool used to flag suspected fraud for human review" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "TIAA’s unnamed system for detecting suspected fraud.", "editor_note": "" } }, "systems[].provider": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "TIAA’s AI flags a suspected scam involving a 76-year-old’s retirement savings, and staff stop the money from moving", "headline": "TIAA’s AI may have helped stop a scam involving retirement savings" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7740ef13b31747812ace838f969427f4de13b1270cb566200e7b1d319b6ec852" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the updated case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "TIAA CEO Thasunda Brown Duckett told Fortune that an AI alert helped staff stop a scam involving a 76-year-old participant’s retirement savings. A portfolio manager referred the concern to the fraud team, which spent hours with the participant and contacted his daughter. Duckett said that the money did not move. The interview describes retirement holdings above $3 million, but does not itemize an attempted transfer. The original episode was released on June 17, 2026, with its transcript published five days later. No participant statement, transaction record, or separate investigation is supplied.", "replication": { "produced_by": [ "An AI alert that a portfolio manager reviewed and referred to the fraud team", "Staff who spent hours discussing the suspected scam with the participant", "Contact with a family member who could help stop the money from moving" ], "must_hold": [ "The flagged request must actually be fraud. Duckett gives no false-positive rate, and the scam itself is not described", "The firm must be able to hold a transfer while it persuades the customer. Duckett says AI by itself would not necessarily have protected him", "The customer or a relative must be reachable and open to persuasion before the funds move" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. Replaced two news retellings and podcast metadata with the original interview transcript. Distinguished the participant’s retirement holdings from an itemized attempted transfer or independently measured avoided loss.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0210-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "A portfolio manager referred an AI alert to the fraud team. Staff spoke with the participant for hours and contacted his daughter before the money moved.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 24, "record_mapped": 8, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0088", "case_url": "records/AIBD-0088.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters." }, { "label": "AI contribution", "value": "NHC forecasters state in Discussions 8, 10, 12, 13 and 14 that official track and intensity forecasts were blended or aligned with Google DeepMind's ensemble. The official forecast remains a human synthesis of several aids. So the AI contribution to outcomes in Jamaica is not isolated." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Jamaica" ] }, "record": { "id": "AIBD-0088", "title": "NHC blended Google DeepMind's AI ensemble into official Hurricane Melissa forecasts before its Category 5 landfall in Jamaica", "summary": "NHC blended Google DeepMind's AI ensemble into official Melissa forecasts from its first advisory on 21 October 2025; Cat 5 landfall in Jamaica 28 October 2025.", "date_observed": "2025-10-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "category": "protective response", "unexpected": false, "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters.", "system": "Google DeepMind AI hurricane forecast model, ensemble mean designated GDMI in NHC guidance", "model_version": null, "developer": "DeepMind", "deployer": "NOAA National Hurricane Center", "location": "New Hope, Jamaica", "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The AI model's contribution to official forecasts is read as forecast accuracy, with no error figure for this storm.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No cited source attributes an individual-level outcome to the AI guidance. Impacts are reported only at population level." }, { "group": "organizational", "depth": 2, "reason": "NHC's official track and intensity forecasts were materially shaped by GDMI (Discussions 8, 10, 13, 14). NHC's Science Operations Officer called the AI guidance very valuable for Melissa." }, { "group": "societal", "depth": 3, "reason": "Official forecasts for a Category 5 landfall affecting about 1.6 million people. A CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time. The review says its early actions were informed by forecast risks (NHC is not named). Material to many, but no counterfactual that the AI changed outcomes." } ], "ai_role": { "status": "assessed", "statement": "NHC forecasters state in Discussions 8, 10, 12, 13 and 14 that official track and intensity forecasts were blended or aligned with Google DeepMind's ensemble. The official forecast remains a human synthesis of several aids. So the AI contribution to outcomes in Jamaica is not isolated." }, "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "date": null, "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ], "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "model_version": null, "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Google-built hazard early-warning system underestimated the Kahramanmaras earthquake and failed to warn millions; same developer family and public-warning task as DeepMind's Melissa forecasts." }, "headline": "NHC blends DeepMind's ensemble into Hurricane Melissa forecasts", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-03-30", "note": "NHC's 2025 verification report, five months after landfall, records low official track errors for Melissa and the DeepMind ensemble mean outperforming the official Atlantic track forecast at 12 to 72 hours.", "quote": "It has been seen in multiple cases during the past few years that the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025.", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, "value": null, "developer_country": "United Kingdom", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: nine reports from the deployer and the deployer's press release. 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It compared official forecasts with model guidance on a common sample." } }, "evaluations[].metric": { "state": "value_present", "value": { "kind": "text", "text": "Track forecast error, intensity forecast error, and forecast lead time." }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Track forecast error, intensity forecast error, and forecast lead time." } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].setting_sample": { "state": "value_present", "value": { "kind": "text", "text": "Hurricane Melissa in the Atlantic, 21–31 October 2025. The report compares forecasts at lead times through 120 hours." }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Hurricane Melissa in the Atlantic, 21–31 October 2025. The report compares forecasts at lead times through 120 hours." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "text", "text": "The sources establish that forecasters used the guidance. They do not establish that it changed protective actions, reduced losses, or prevented deaths." }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The sources establish that forecasters used the guidance. They do not establish that it changed protective actions, reduced losses, or prevented deaths." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "group": "societal", "basis": "Google-built hazard early-warning system underestimated the Kahramanmaras earthquake and failed to warn millions; same developer family and public-warning task as DeepMind's Melissa forecasts." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-03-30", "note": "NHC's 2025 verification report, five months after landfall, records low official track errors for Melissa and the DeepMind ensemble mean outperforming the official Atlantic track forecast at 12 to 72 hours.", "quote": "It has been seen in multiple cases during the past few years that the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025.", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "NHC published its Tropical Cyclone Report on 25 February 2026 and revised it on 19 May. The report evaluates the forecasts against the storm’s observed track and intensity." } }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0088" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "National Hurricane Center forecasters. People in Melissa’s path were the intended beneficiaries of the forecasts." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "National Hurricane Center forecasters. People in Melissa’s path were the intended beneficiaries of the forecasts." } }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "display": { "answer": "No count of people who benefited from the AI guidance is established." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The AI model's contribution to official forecasts is read as forecast accuracy, with no error figure for this storm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "National Hurricane Center forecasters used Google DeepMind’s guidance in their official forecasts for Hurricane Melissa." } }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No cited source attributes an individual-level outcome to the AI guidance. Impacts are reported only at population level." }, { "group": "organizational", "depth": 2, "reason": "NHC's official track and intensity forecasts were materially shaped by GDMI (Discussions 8, 10, 13, 14). NHC's Science Operations Officer called the AI guidance very valuable for Melissa." }, { "group": "societal", "depth": 3, "reason": "Official forecasts for a Category 5 landfall affecting about 1.6 million people. A CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time. The review says its early actions were informed by forecast risks (NHC is not named). Material to many, but no counterfactual that the AI changed outcomes." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "New Hope, Jamaica", "developer_country": "United Kingdom", "deployer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Experimental AI tropical cyclone forecast model providing ensemble track and intensity guidance to NHC forecasters." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-10-21", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Forecasters need timely model output, checks before operational use, and conventional guidance for comparison. Later observations are needed to assess forecast accuracy." } }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set the date observed to 21 October 2025 and reworded the summary and account because Discussions 1 and 2 show the Google DeepMind ensemble mean shaping the official track from the first advisory, added those two discussions as evidence, corrected the Discussion 14 title to “Tropical Storm Melissa” as its header reads, attributed the US$8.8 billion damage figure to the World Bank/IDB estimate relayed by IFRC, and tied the DREF's early actions to “forecast risks” as the review says rather than to NHC.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml; https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3; https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google_DeepMind; https://www.nhc.noaa.gov/aboutintro.shtml", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to DeepMind.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "source": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml; https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added NHC’s revised Hurricane Melissa report, replaced the obsolete statement that it was unpublished, and separated forecast verification from humanitarian background.", "source": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Weather forecasting / tropical cyclones", "segment": "Public-sector meteorological service" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "versions_and_access": [ { "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "access": "Full original page read on October 25, 2026. Q&A 7 discusses Melissa, and Q&A 2 describes NHC’s development partnership with DeepMind.", "metadata": { "DC.date.created": "June 24th 2026 4:15 PM" }, "date_treatment": "Do not infer a publication date from the URL or treat creation metadata as original publication. The original March 30, 2026 verification report already links to this URL on printed p. 7. The future-date suspicion is resolved and should not appear on the website." }, { "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "access": "Full original PDF downloaded successfully (HTTP 200) on October 25, 2026. Title page dated March 30, 2026. Printed p. 7 links to the NHC interview, and the report provides seasonal forecast comparisons.", "supersedes_access_note": "The earlier direct-PDF 403 limitation is now resolved for this document." }, { "source_ref": "AIBD-0088-source-0010", "source_url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "publication_date": "2026-02-25", "version_reviewed_date": "2026-05-19", "status": "Full original PDF downloaded successfully (HTTP 200) and relevant text read on October 25, 2026. HTTP Last-Modified: May 19, 2026.", "version_note": "The May 19 title-page note says that this version revised fatality totals and damage estimates based on updated government information. Preserve February 25 as the original report date and May 19 as the reviewed revision." } ], "limitations": [ "The separately cited IFRC operational review and update retain their earlier access limits. The November 2025 IFRC operational strategy was accessible." ] } }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The NHC interview, 2025 verification report, and revised Melissa report were accessible in full. Earlier access limits remain attached to the individual IFRC reports." } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ] } }, "provenance": { "kind": "source_review", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009", "AIBD-0088-source-0010" ], "role": "Retained sources" }, "provenance": { "kind": "curatorial", "basis": "Synchronized source identifiers.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 1, 1100 AM EDT Tue Oct 21 2025, forecasters Cangialosi/Bucci. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 1", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "The NHC official track forecast lies between the Google DeepMind ensemble mean track and the correct consensus aid, HCCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 2, 500 PM EDT Tue Oct 21 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 2", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "date": "2025-10-21", "written_by": "deployer", "quote": "This prediction is a little left of the previous forecast and remains a blend of the latest Google DeepMind ensemble mean, the corrected consensus aid HCCA, and simple consensus TVCA." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 8, 500 AM EDT Thu Oct 23 2025, forecaster Cangialosi. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 8", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "The NHC track forecast is shifted a little to the right of the previous one, trending toward a blend of the latest Google DeepMind ensemble mean and HCCA solution." }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 10, 500 PM EDT Thu Oct 23 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 10", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "date": "2025-10-23", "written_by": "deployer", "quote": "the latest NHC track was only nudged slightly southward from this morning, blending the reliable HCCA and GDMI aids" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 13, 1100 AM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 13", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "Every single Google DeepMind member shows the system become a Category 4 hurricane or higher" }, { "kind": "report", "citation": "National Hurricane Center (2025). Tropical Storm Melissa Discussion Number 14, 500 PM EDT Fri Oct 24 2025, forecaster Papin. NOAA/NWS.", "title": "Tropical Storm Melissa Discussion Number 14", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "date": "2025-10-24", "written_by": "deployer", "quote": "The NHC intensity forecast is on the high side of the guidance envelope, and closest too the Google DeepMind ensemble mean (GDMI)." }, { "kind": "report", "citation": "National Hurricane Center (2025). Hurricane Melissa Tropical Cyclone Update, 100 PM EDT Tue Oct 28 2025. NOAA/NWS.", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "date": "2025-10-28", "written_by": "deployer", "quote": "CATEGORY 5 MELISSA MAKES LANDFALL IN JAMAICA...ONE OF THE MOST POWERFUL HURRICANE LANDFALLS ON RECORD IN THE ATLANTIC BASIN" }, { "kind": "press release", "citation": "National Weather Service (n.d.). AI in Hurricane Forecasting at the National Hurricane Center: A Q&A with NHC Science Operation Officer Wallace Hogsett. NOAA/NWS News Around NOAA.", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "written_by": "deployer", "quote": "The AI models honed in very early on the likely track and intensity and provided very valuable guidance to complement our traditional NWP guidance." }, { "kind": "report", "citation": "Cangialosi, J. P., Martinez, J., Reinhart, B., Papin, P. (2026). National Hurricane Center Forecast Verification Report: 2025 Hurricane Season. NOAA/NHC, 30 March 2026.", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "date": "2026-03-30", "written_by": "deployer", "quote": "the NHC track errors are notably lower than average for major hurricanes, including Humberto and Melissa in 2025" }, { "kind": "report", "citation": "Kelly, Larry A., José Álamo, and Aidan D. Mahoney. Hurricane Melissa (AL132025), 21–31 October 2025. National Hurricane Center Tropical Cyclone Report. Revised May 19, 2026, originally February 25, 2026. 16–17, Tables 5b and 6b.", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "date": "2026-02-25", "written_by": "deployer", "quote": "The NHC OFCL forecasts were comparable to GDMI through 96 h, but performed slightly better than GDMI at 120 h." } ] } }, "provenance": { "kind": "source_review", "basis": "Moved two IFRC reports to background source notes because they describe storm impacts and humanitarian action, without establishing the AI forecast’s contribution to those outcomes. Preserved their citations, excerpts, and the existing account and depths. Retained NHC evidence of forecast use and verification, attributed to the deployer rather than an independent AI-benefit evaluator.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0088-source-0001" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "title": "Tropical Storm Melissa Discussion Number 1", "relationship": "Original operational advisory establishing that DeepMind ensemble guidance contributed to the official forecast from the first advisory." }, { "source_refs": [ "AIBD-0088-source-0002" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.002.shtml", "title": "Tropical Storm Melissa Discussion Number 2", "relationship": "Original operational advisory recording the blend of DeepMind and other guidance." }, { "source_refs": [ "AIBD-0088-source-0003" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.008.shtml", "title": "Tropical Storm Melissa Discussion Number 8", "relationship": "Original operational advisory recording a later adjustment toward DeepMind and HCCA guidance." }, { "source_refs": [ "AIBD-0088-source-0004" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.010.shtml", "title": "Tropical Storm Melissa Discussion Number 10", "relationship": "Original operational advisory recording track and intensity guidance. It does not isolate downstream public benefits." }, { "source_refs": [ "AIBD-0088-source-0005" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.013.shtml", "title": "Tropical Storm Melissa Discussion Number 13", "relationship": "Original operational advisory recording the ensemble’s Category 4-or-higher signal." }, { "source_refs": [ "AIBD-0088-source-0006" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.014.shtml", "title": "Tropical Storm Melissa Discussion Number 14", "relationship": "Original operational advisory recording the intensity forecast’s agreement with GDMI." }, { "source_refs": [ "AIBD-0088-source-0007" ], "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.update.10281700.shtml", "title": "Hurricane Melissa Tropical Cyclone Update (landfall)", "relationship": "Original landfall update establishing storm context, not the AI system’s effect on public outcomes." }, { "source_refs": [ "AIBD-0088-source-0008" ], "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "title": "AI in Hurricane Forecasting at the National Hurricane Center", "relationship": "Original interview with NHC’s Science Operations Officer. NHC deployed the model and partnered in its development; this is not an independent evaluation." }, { "source_refs": [ "AIBD-0088-source-0009" ], "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "title": "NHC Forecast Verification Report, 2025 Hurricane Season", "relationship": "Original seasonal verification by NHC and a cooperating researcher. It evaluates forecasting performance, but it is not an independent evaluation of AI-attributable humanitarian outcomes." }, { "url": "https://www.nhc.noaa.gov/data/tcr/AL132025_Melissa.pdf", "title": "Hurricane Melissa (AL132025): Tropical Cyclone Report", "relationship": "Original retrospective verification by the agency that used the guidance. The report compares track and intensity forecasts for Melissa and explains how GDMI and regional models contributed to forecasters’ confidence. It does not isolate the AI model’s effect on humanitarian outcomes.", "locator": "Printed pp. 16–17, Track Forecast and Intensity Forecast, Tables 5b and 6b.", "original_publication_date": "2026-02-25", "version_reviewed_date": "2026-05-19", "version_note": "The May 19 title-page note says that this version revised fatality totals and damage estimates based on updated government information. Preserve February 25 as the original report date and May 19 as the reviewed revision.", "access": "Full original PDF downloaded successfully (HTTP 200) and relevant text read on October 25, 2026. HTTP Last-Modified: May 19, 2026.", "source_refs": [ "AIBD-0088-source-0010" ] } ], "related_material": [ { "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "title": "Jamaica - Hurricane Melissa Imminent DREF (MDRJM005) Operational Review Report", "published_on": "2026-08-07", "relationship": "Humanitarian and storm context preserved from the existing case. It does not establish an AI-attributable outcome and is not counted as independent AI-benefit evaluation.", "preserved_evidence": { "kind": "report", "citation": "IFRC (2026). Jamaica - Hurricane Melissa Imminent DREF (MDRJM005), DREF Operational Review Report, April - May 2026. ReliefWeb, 7 August 2026.", "title": "Jamaica - Hurricane Melissa Imminent DREF (MDRJM005) Operational Review Report", "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-imminent-dref-mdrjm005-dref-operational-review-report-april-may-2026", "date": "2026-08-07", "written_by": "independent", "quote": "Rapid approval and pre-landfall mobilisation, enabled by prior training, pre-positioned stock, and strong logistics systems, allowed JRC to respond quickly." }, "access": "The original ReliefWeb URL returned HTTP 403 during this narrow review. Existing citations and quotations are preserved; no new full-text verification is claimed." }, { "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3", "title": "Jamaica | Hurricane Melissa DREF MDRJM005 Operation update #3", "published_on": "2026-07-07", "relationship": "Humanitarian and storm context preserved from the existing case. It does not establish an AI-attributable outcome and is not counted as independent AI-benefit evaluation.", "preserved_evidence": { "kind": "report", "citation": "IFRC (2026). Jamaica | Hurricane Melissa DREF No. MDRJM005 Operation update #3. ReliefWeb, 7 July 2026.", "title": "Jamaica | Hurricane Melissa DREF MDRJM005 Operation update #3", "url": "https://reliefweb.int/report/jamaica/jamaica-hurricane-melissa-dref-no-mdrjm005-operation-update-3", "date": "2026-07-07", "written_by": "independent", "quote": "winds of nearly 300 km (185 miles) per hour" }, "access": "The original ReliefWeb URL returned HTTP 403 during this narrow review. Existing citations and quotations are preserved; no new full-text verification is claimed." }, { "url": "https://go-api.ifrc.org/api/downloadfile/93656/MDRJM005OS", "title": "Jamaica | Hurricane Melissa: Emergency Appeal Operational Strategy (MDRJM005)", "date": "2025-11-11", "locator": "p. 2, timeline; p. 3, situation overview; p. 10, targeting", "finding": "The timeline records the CHF80,000 allocation on October 23, 2025. The targeting section attributes the estimated 1.6 million people affected to analysis of UNOSAT wind forecasts and demographic data.", "relationship": "Original IFRC background on anticipatory aid and exposure. This report does not attribute those actions or the affected-population estimate to Google DeepMind.", "access": "Full original PDF downloaded successfully (HTTP 200) and relevant sections read on October 25, 2026. The IFRC emergency page dates this operational strategy November 11, 2025.", "recommendation": "Keep as related background rather than independent evidence of AI-created benefit. Preserve existing IFRC related material and its separate access status." } ], "claim_lineage": [ { "claim": "The first official Melissa forecast used DeepMind ensemble guidance.", "source_ref": "AIBD-0088-source-0001", "locator": "Track discussion, paragraph beginning “The system was moving”", "access": "Original advisory text read." }, { "claim": "GDMI performed well in NHC’s 2025 verification.", "source_ref": "AIBD-0088-source-0009", "locator": "Abstract and Atlantic track-verification discussion", "access": "Full original PDF inspected on 25 October 2026.", "limitation": "Seasonal model accuracy is not a measure of AI-attributable lives saved or of the AI contribution to each humanitarian action." } ], "access_audit": [ { "url": "https://www.nhc.noaa.gov/archive/2025/al13/al132025.discus.001.shtml", "access": "Original advisory text read through the indexed official page after the direct request returned HTTP 403." }, { "url": "https://www.weather.gov/news/261102-AI-Hurricane-Forecasting", "access": "Full original page read on October 25, 2026. Q&A 7 discusses Melissa, and Q&A 2 describes NHC’s development partnership with DeepMind.", "access_history": [ { "access": "Original NHC interview text read; it identifies NHC’s development partnership with Google DeepMind.", "reviewed_on": "2026-10-24" } ], "reviewed_on": "2026-10-25" }, { "url": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf", "access": "Full original PDF downloaded successfully (HTTP 200) on October 25, 2026. Title page dated March 30, 2026. Printed p. 7 links to the NHC interview, and the report provides seasonal forecast comparisons.", "access_history": [ { "access": "Indexed original report abstract and relevant text read; direct PDF request returned HTTP 403.", "reviewed_on": "2026-10-24" } ], "reviewed_on": "2026-10-25" } ], "limitations": [ "NHC is the operator and a development partner, so its verification is primary but not an independent evaluation of its own use of AI.", "Track and intensity comparisons do not measure lives saved, damage avoided, or whether IFRC acted earlier because of GDMI.", "The seasonal comparison must not be substituted for a Melissa-only result." ], "ordering_basis": "Keep the contemporaneous NHC account of AI use first, followed by retained operational documentation and later verification. Humanitarian background remains available in the source notes.", "lead_source_ref": "AIBD-0088-source-0001", "retained_source_order": [ "AIBD-0088-source-0001", "AIBD-0088-source-0002", "AIBD-0088-source-0003", "AIBD-0088-source-0004", "AIBD-0088-source-0005", "AIBD-0088-source-0006", "AIBD-0088-source-0007", "AIBD-0088-source-0008", "AIBD-0088-source-0009", "AIBD-0088-source-0010" ], "scope": "Operational use and retrospective verification of forecasts, with humanitarian material retained separately as background.", "reviewed_on": "2026-10-25" } }, "provenance": { "kind": "source_review", "basis": "Read the original NHC Melissa report, revised 19 May 2026, pp. 16–17 and Tables 5b/6b, together with the original NHC interview and seasonal verification report.", "record_fields": [], "source_refs": [ "AIBD-0088-source-0010", "AIBD-0088-source-0008", "AIBD-0088-source-0009" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "NHC’s operational advisories document its use of DeepMind guidance. Its later verification reports assess forecast accuracy. IFRC reports provide humanitarian context without isolating AI’s effects." } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "NHC blended Google DeepMind's AI ensemble into official Melissa forecasts from its first advisory on 21 October 2025; Cat 5 landfall in Jamaica 28 October 2025.", "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Google DeepMind AI hurricane forecast model, ensemble mean designated GDMI in NHC guidance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "DeepMind" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "NHC blended Google DeepMind's AI ensemble into official Hurricane Melissa forecasts before its Category 5 landfall in Jamaica", "headline": "NHC blends DeepMind's ensemble into Hurricane Melissa forecasts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "7494545dd8aeba975dc7387c52bc23ab138bc6a416517d50ebb709bf803d7efa" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed the content fingerprint after the source-attribution correction.", "record_fields": [ "evidence", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The US National Hurricane Center (NHC) blended Google DeepMind's experimental AI hurricane model (ensemble mean GDMI) into official forecasts for Tropical Storm Melissa. It did so from its first advisory, 21 October 2025. Discussion 1 placed the official track between the DeepMind ensemble mean and the consensus aid HCCA. Discussion 2 described the track as a blend of the DeepMind mean, HCCA and TVCA. Discussion 8 (23 October) moved the track toward a blend of the DeepMind mean and HCCA. Discussion 10 blended the HCCA and GDMI aids. It set an intensity forecast in best agreement with GDMI after all 50 DeepMind members showed a major hurricane. On 28 October 2025 Melissa made landfall in southwestern Jamaica near New Hope with 185 mph winds and 892 mb pressure. IFRC reported 45 deaths and about 1.6 million people affected. It relayed a World Bank/IDB estimate of US$8.8 billion in physical damage. Its CHF 80,000 Imminent DREF was approved on 23 October with a five-day lead time.", "replication": { "produced_by": [ "Experimental Google DeepMind ensemble guidance available in real time to NHC forecasters", "Forecasters choosing to blend GDMI with consensus aids (HCCA) in official track and intensity forecasts", "A slow-moving, rapidly intensifying hurricane where models disagreed on track relative to Jamaica" ], "must_hold": [ "AI ensemble output delivered on time and verified before operational use, as NHC describes", "Human forecasters retain the final official forecast and integrate conventional NWP guidance", "Forecast accuracy can be assessed against the observed track and intensity. NHC’s Tropical Cyclone Report, revised 19 May 2026, provides that comparison for Melissa" ], "echoes": [ { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 Atlantic season track forecasts", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" }, { "date": "2026-03-30", "setting": "NHC homogeneous verification, 2025 eastern North Pacific track forecasts (GDMI beat official and consensus aids at 48–120 h)", "outcome": "recurred", "source": "https://www.nhc.noaa.gov/verification/pdfs/Verification_2025.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Forecasters received the experimental DeepMind ensemble in real time and combined it with conventional forecasts. They retained responsibility for the official track and intensity forecasts." } } }, "summary": { "total": 91, "addressed": 91, "known": 43, "reviewed": 11, "record_mapped": 32, "not_assessed": 48, "complete": false, "by_state": { "value_present": 43, "not_reported": 0, "unresolved": 0, "not_assessed": 48, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0091", "case_url": "records/AIBD-0091.html", "reason": "Protection, inspection, or reliable operation of energy and water systems.", "details": [ { "label": "Focus", "value": "Energy and water" }, { "label": "Purpose", "value": "Satellite-based detection and notification of major methane emission events to governments and operators to enable mitigation." }, { "label": "AI contribution", "value": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Algeria" ] }, "record": { "id": "AIBD-0091", "title": "UNEP MARS alert led to repair of a decades-old methane source at Hassi Messaoud, Algeria, verified by satellite from 15 October 2024", "summary": "MARS alert led an Algerian operator to fix an unlit flare at Hassi Messaoud; satellites show no emissions since 15 October 2024.", "date_observed": "2024-10-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades.", "category": "benefit created", "unexpected": false, "purpose": "Satellite-based detection and notification of major methane emission events to governments and operators to enable mitigation.", "system": "UNEP International Methane Emissions Observatory, Methane Alert and Response System (MARS)", "model_version": null, "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operator at Hassi Messaoud (repair)", "location": "Hassi Messaoud, Algeria", "domain": "Earth observation / methane emissions", "segment": "Oil and gas", "benefit_type": "Reduced Carbon Emissions", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. Material to many, provisional pending independent quantification of the emissions avoided." } ], "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." }, "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012). Repository Date Issued 2024-11 (month only; first day used); the PDF's LAST UPDATED field reads 15 October 2024.", "title": "MARS Confirmed Mitigation Action - Algeria (DZA_S_012)", "url": "https://wedocs.unep.org/bitstreams/5810b829-cf15-4eb0-b509-8dd489884a66/download", "date": "2024-11-01", "written_by": "makers", "quote": "No further emissions have been detected at the location since 15 October 2024." }, { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012), repository record. Date Issued 2024-11 (month only; first day used).", "title": "MARS Confirmed Mitigation Action - Algeria (repository record)", "url": "https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0", "date": "2024-11-01", "written_by": "makers", "quote": "In October 2024, at Algeria's Hassi Messaoud, the International Methane Emissions Observatory (IMEO) confirmed methane mitigation action through the Methane Alert and Response System (MARS)." }, { "kind": "news report", "citation": "UN News (2026). Satellites can now spot methane leaks in real time - but most go unfixed. 23 July 2026.", "title": "Satellites can now spot methane leaks in real time - but most go unfixed", "url": "https://news.un.org/en/story/2026/07/1168007", "date": "2026-07-23", "written_by": "makers", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades" }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "date": null, "written_by": "makers", "quote": "data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models" }, { "kind": "press release", "citation": "UNEP (n.d.). MARS Frequently Asked Questions. UNEP IMEO.", "title": "MARS Frequently Asked Questions", "url": "https://www.unep.org/en/what-we-do/energy/methane/international-methane-emissions-observatory/mars-frequently-asked", "date": null, "written_by": "makers", "quote": "MARS utilizes deep learning models to monitor data from continuously observing high-resolution satellites for future emissions from the region." }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (month only; first day used).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": "2026-09-01", "written_by": "makers", "quote": "To date, MARS has enabled and confirmed mitigation of over 40 major methane sources." }, { "kind": "report", "citation": "UNEP IMEO (2025). An Eye on Methane 2025: From Measurement to Momentum, repository abstract. Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ], "replication": { "produced_by": [ "Satellite detection and expert quantification of a large point source, followed by a MARS notification to the government and operator", "An operator able to identify a simple equipment fault (passing valve on a flare line) and correct it", "Follow-up satellite monitoring confirming cessation" ], "must_hold": [ "A responsive focal point or operator. UN News reports only 13 percent of alerts lead to any response", "Verification depends on later satellite coverage. The Kazakhstan case shows emissions can recur after confirmed mitigation" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan (KAZ_S_136, 43.39767°, 52.49505°; no place name given): gas pipeline leak first detected September 2025 (plume imaged by DLR's EnMAP 29 October 2025); operator transferred the gas flow to another pipeline, mitigation reported 26 November 2025; no further emissions detected since 12 March 2026 (document last updated 15 April 2026), but IMEO observed subsequent emissions at the location as of 21 May 2026", "model_version": null, "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2023-12-01", "setting": "Argentina oil and gas facility: UNEP press release says the company fixed the leak after a MARS alert and satellite monitoring confirmed cessation", "model_version": null, "outcome": "recurred", "source": "https://www.unep.org/news-and-stories/press-release/uns-global-methane-tracking-system-set-translate-data-revolution" }, { "date": "2026-04-01", "setting": "Alberta, Canada: trade-press report of UNEP's monthly snapshot describing a 175-tonne-per-week plume fixed by repairing a failed ball valve on pigging equipment (month only; first day used)", "model_version": null, "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] }, "paired_incident": null, "headline": "UNEP MARS alert leads to methane leak repair at Hassi Messaoud", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-07-23", "note": "Twenty-one months after the repair, UN News cited the Algerian case as a leak fixed after a MARS alert, while reporting that only 13 per cent of alerts receive a response.", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades, delivering climate benefits comparable to removing half a million cars from the road each year.", "url": "https://news.un.org/en/story/2026/07/1168007" }, "value": null, "developer_country": "France", "deployer_country": "France", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Rewrote the Kazakhstan echo from the KAZ_S_136 document (no place name, first detection September 2025 with the EnMAP plume imaged 29 October 2025, no further emissions since 12 March 2026 until the 21 May 2026 banner), marked the month-only dates in the case-document and snapshot citations and dated the Eye on Methane item 2025-10-03 from its repository record, and stated that UNEP IMEO both developed and operates MARS so that the two case-document items count as a public body's official reports (marked independent).", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download; https://wedocs.unep.org/bitstreams/5810b829-cf15-4eb0-b509-8dd489884a66/download; https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0; https://wedocs.unep.org/handle/20.500.11822/48664", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://news.un.org/en/story/2026/07/1168007", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Carbon Emissions under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory; https://en.wikipedia.org/wiki/International_Methane_Emissions_Observatory", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UNEP International Methane Emissions Observatory.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: two case reports from the makers, two reports from the makers, the makers' news report and two press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0091", "catalog_version": "1.0.0", "source_fingerprint": "deb4f3b76031e7146e46b243baa8cbf4c5288f8921f9a2d91da88e1cf8cfbfc7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0091-source-0001", "AIBD-0091-source-0002", "AIBD-0091-source-0003", "AIBD-0091-source-0004", "AIBD-0091-source-0005", "AIBD-0091-source-0006", "AIBD-0091-source-0007" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "UNEP International Methane Emissions Observatory (IMEO)", "deployer": "UNEP International Methane Emissions Observatory (notification); unnamed operator at Hassi Messaoud (repair)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "UNEP IMEO both developed and operates MARS (developer and deployer are the same body). UNEP's MARS page describes detection using more than 30 satellite instruments coupled with expertise and advanced AI models. The MARS FAQ says deep learning models monitor high-resolution satellite data for further emissions. The case document's boilerplate says MARS combines 30+ satellite instruments with expert analysis and AI models. None of these states which step in this case was AI-driven. The repair was carried out by the operator. The two case-document items are treated as a public body's official reports rather than vendor material." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. Material to many, provisional pending independent quantification of the emissions avoided." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: two case reports from the makers, two reports from the makers, the makers' news report and two press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No identifiable individuals are documented as affected by the repair." }, { "group": "organizational", "depth": 2, "reason": "The operator fixed a passing valve on a flare line and restored normal operations at one facility, per UNEP's case document." }, { "group": "societal", "depth": 3, "reason": "UN News describes the leak as having persisted for decades before the repair. 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UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models" }, { "kind": "press release", "citation": "UNEP (n.d.). MARS Frequently Asked Questions. UNEP IMEO.", "title": "MARS Frequently Asked Questions", "url": "https://www.unep.org/en/what-we-do/energy/methane/international-methane-emissions-observatory/mars-frequently-asked", "written_by": "makers", "quote": "MARS utilizes deep learning models to monitor data from continuously observing high-resolution satellites for future emissions from the region." }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (month only; first day used).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": "2026-09-01", "written_by": "makers", "quote": "To date, MARS has enabled and confirmed mitigation of over 40 major methane sources." }, { "kind": "report", "citation": "UNEP IMEO (2025). An Eye on Methane 2025: From Measurement to Momentum, repository abstract. Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0091-source-0001", "AIBD-0091-source-0002", "AIBD-0091-source-0003", "AIBD-0091-source-0004", "AIBD-0091-source-0005", "AIBD-0091-source-0006", "AIBD-0091-source-0007" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012). Repository Date Issued 2024-11 (month only; first day used); the PDF's LAST UPDATED field reads 15 October 2024.", "title": "MARS Confirmed Mitigation Action - Algeria (DZA_S_012)", "url": "https://wedocs.unep.org/bitstreams/5810b829-cf15-4eb0-b509-8dd489884a66/download", "date": "2024-11-01", "written_by": "makers", "quote": "No further emissions have been detected at the location since 15 October 2024." }, { "kind": "case report", "citation": "UNEP IMEO (2024). Methane Alert and Response System (MARS) Confirmed Mitigation Action - Algeria (DZA_S_012), repository record. Date Issued 2024-11 (month only; first day used).", "title": "MARS Confirmed Mitigation Action - Algeria (repository record)", "url": "https://wedocs.unep.org/items/33a43a3a-8a2a-4831-8f18-e09e472623b0", "date": "2024-11-01", "written_by": "makers", "quote": "In October 2024, at Algeria's Hassi Messaoud, the International Methane Emissions Observatory (IMEO) confirmed methane mitigation action through the Methane Alert and Response System (MARS)." }, { "kind": "news report", "citation": "UN News (2026). Satellites can now spot methane leaks in real time - but most go unfixed. 23 July 2026.", "title": "Satellites can now spot methane leaks in real time - but most go unfixed", "url": "https://news.un.org/en/story/2026/07/1168007", "date": "2026-07-23", "written_by": "makers", "quote": "In Algeria, one alert led to the repair of a leak that had persisted for decades" }, { "kind": "press release", "citation": "UNEP (n.d.). Methane Alert and Response System (MARS). UNEP topics page.", "title": "Methane Alert and Response System (MARS)", "url": "https://www.unep.org/topics/energy/methane/methane-alert-and-response-system-mars", "written_by": "makers", "quote": "data from more than 30 satellite instruments, coupled with scientific expertise and advanced AI models" }, { "kind": "press release", "citation": "UNEP (n.d.). MARS Frequently Asked Questions. UNEP IMEO.", "title": "MARS Frequently Asked Questions", "url": "https://www.unep.org/en/what-we-do/energy/methane/international-methane-emissions-observatory/mars-frequently-asked", "written_by": "makers", "quote": "MARS utilizes deep learning models to monitor data from continuously observing high-resolution satellites for future emissions from the region." }, { "kind": "report", "citation": "UNEP IMEO (2026). Methane Alert and Response System Snapshot: Top 50 Emitters, Country Responses and Mitigation, September 2026 Update (month only; first day used).", "title": "MARS Snapshot, September 2026 Update", "url": "https://methanedata.unep.org/downloads/top50_emitters/latest/mars_snapshot_latest.pdf", "date": "2026-09-01", "written_by": "makers", "quote": "To date, MARS has enabled and confirmed mitigation of over 40 major methane sources." }, { "kind": "report", "citation": "UNEP IMEO (2025). An Eye on Methane 2025: From Measurement to Momentum, repository abstract. Repository Date Issued 2025-10-03.", "title": "An Eye on Methane 2025: From Measurement to Momentum", "url": "https://wedocs.unep.org/bitstream/handle/20.500.11822/48664/An-Eye-on-Methane-2025.pdf?sequence=1&isAllowed=y", "date": "2025-10-03", "written_by": "makers", "quote": "the Methane Alert and Response System, which integrates satellite data and artificial intelligence to notify governments and companies of large emission events" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MARS alert led an Algerian operator to fix an unlit flare at Hassi Messaoud; satellites show no emissions since 15 October 2024.", "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "UNEP International Methane Emissions Observatory, Methane Alert and Response System (MARS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "UNEP International Methane Emissions Observatory (IMEO)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNEP MARS alert led to repair of a decades-old methane source at Hassi Messaoud, Algeria, verified by satellite from 15 October 2024", "headline": "UNEP MARS alert leads to methane leak repair at Hassi Messaoud" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "deb4f3b76031e7146e46b243baa8cbf4c5288f8921f9a2d91da88e1cf8cfbfc7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNEP's Methane Alert and Response System (MARS) combines data from more than 30 satellite instruments with expert analysis and AI models. It confirmed mitigation of a methane source at Hassi Messaoud, Algeria, in October 2024. UNEP's case document was issued in November 2024 per the repository record. The PDF's last-updated field reads 15 October 2024. It records a Landsat 5 detection dated 1 July 1987. It attributes the emissions to an unlit flare caused by a passing valve on the flare line. The operator corrected the valve and restored normal operations. UNEP states no further emissions have been detected at the location since 15 October 2024. UN News reported on 23 July 2026 that the Algerian alert led to the repair of a leak that had persisted for decades.", "replication": { "produced_by": [ "Satellite detection and expert quantification of a large point source, followed by a MARS notification to the government and operator", "An operator able to identify a simple equipment fault (passing valve on a flare line) and correct it", "Follow-up satellite monitoring confirming cessation" ], "must_hold": [ "A responsive focal point or operator. UN News reports only 13 percent of alerts lead to any response", "Verification depends on later satellite coverage. The Kazakhstan case shows emissions can recur after confirmed mitigation" ], "echoes": [ { "date": "2025-11-26", "setting": "Kazakhstan (KAZ_S_136, 43.39767°, 52.49505°; no place name given): gas pipeline leak first detected September 2025 (plume imaged by DLR's EnMAP 29 October 2025); operator transferred the gas flow to another pipeline, mitigation reported 26 November 2025; no further emissions detected since 12 March 2026 (document last updated 15 April 2026), but IMEO observed subsequent emissions at the location as of 21 May 2026", "outcome": "recurred", "source": "https://wedocs.unep.org/bitstreams/558dda69-c8e5-42c2-b10a-7b9c3dcc3e79/download" }, { "date": "2023-12-01", "setting": "Argentina oil and gas facility: UNEP press release says the company fixed the leak after a MARS alert and satellite monitoring confirmed cessation", "outcome": "recurred", "source": "https://www.unep.org/news-and-stories/press-release/uns-global-methane-tracking-system-set-translate-data-revolution" }, { "date": "2026-04-01", "setting": "Alberta, Canada: trade-press report of UNEP's monthly snapshot describing a 175-tonne-per-week plume fixed by repairing a failed ball valve on pigging equipment (month only; first day used)", "outcome": "recurred", "source": "https://www.thechemicalengineer.com/news/canada-plugs-major-source-of-methane-emissions-from-oil-and-gas-field-leak/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0376", "case_url": "records/AIBD-0376.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "LFR vans are deployed to match passers-by against a watchlist of wanted people and suspects so that officers can arrest or check them. The arrests and order checks are that purpose. The Examiner adds that the cameras also serve to find missing people, witnesses and victims." }, { "label": "AI contribution", "value": "NEC NeoFace compares camera images with a police watchlist and generates possible matches. Trained officers review the alerts and decide whether to act. The nine recorded alerts and resulting interventions are reported by the deployer; no independent evaluation of these deployments was found." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0376", "title": "West Yorkshire Police's first live facial recognition deployment in Huddersfield: three arrests, including a domestic stalking suspect, and six Sexual Harm Prevention Order checks over three days", "summary": "West Yorkshire Police reported three arrests and six Sexual Harm Prevention Order checks in Huddersfield on August 12–14, 2026. Its register records nine alerts and an estimated 12,875 faces seen.", "date_observed": "2026-08-17", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "West Yorkshire Police used live facial recognition in Huddersfield on August 12–14, 2026. The force reported three arrests, involving a domestic stalking suspect, a person wanted on a warrant, and a shoplifting suspect, along with six Sexual Harm Prevention Order checks. Its deployment register records five alerts on August 12 and two on each subsequent day. All nine were recorded as confirmed true alerts. The estimated numbers of faces seen were 5,824, 2,951, and 4,100, totaling 12,875. These are the force’s operational records, not an independent accuracy study. Zero recorded false alerts does not establish that there were no missed matches. The force identifies NEC NeoFace as the matching system and states that trained officers review alerts before intervention. The reported arrests and checks do not establish a conviction or a specific harm that was prevented.", "category": "benefit created", "unexpected": false, "purpose": "LFR vans are deployed to match passers-by against a watchlist of wanted people and suspects so that officers can arrest or check them. The arrests and order checks are that purpose. The Examiner adds that the cameras also serve to find missing people, witnesses and victims.", "system": "NEC NeoFace live facial recognition used in West Yorkshire Police camera vans", "model_version": null, "developer": "NEC (NeoFace)", "deployer": "West Yorkshire Police (Kirklees district)", "location": "Huddersfield, United Kingdom", "domain": "Policing / wanted-person identification", "segment": "Live facial recognition in a town center", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-08-17-4e1c" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The force’s own release and deployment register report three arrests and six other interventions over three days. They establish a reported operational result, without independent verification of accuracy or later case outcomes." }, { "group": "societal", "depth": 1, "reason": "The force reported detention of three suspects and six checks of order subjects. No specific prevented harm or subsequent conviction is established." } ], "ai_role": { "status": "assessed", "statement": "NEC NeoFace compares camera images with a police watchlist and generates possible matches. Trained officers review the alerts and decide whether to act. The nine recorded alerts and resulting interventions are reported by the deployer; no independent evaluation of these deployments was found." }, "evidence": [ { "kind": "press release", "citation": "West Yorkshire Police. Cutting Edge Facial Recognition Cameras Used for First Time in Kirklees. 2026-08-17.", "title": "Cutting Edge Facial Recognition Cameras Used for First Time in Kirklees", "url": "https://www.westyorkshire.police.uk/news-appeals/cutting-edge-facial-recognition-cameras-used-first-time-kirklees", "date": "2026-08-17", "written_by": "deployer", "quote": null }, { "kind": "dataset", "citation": "West Yorkshire Police. Live facial recognition deployment register 2026. n.d. Version identified by filename 19102026. Huddersfield rows dated August 12–14, 2026.", "title": "Live facial recognition deployment register 2026", "url": "https://www.westyorkshire.police.uk/sites/default/files/2026-10/live-facial-recognition-deployment-register-2026-19102026.docx", "date": null, "written_by": "deployer", "quote": null }, { "kind": "report", "citation": "West Yorkshire Police. Live Facial Recognition. n.d. Operational description, NEC NeoFace identification, human review, and data retention.", "title": "Live Facial Recognition", "url": "https://www.westyorkshire.police.uk/about-us/how-we-work/facial-recognition/live-facial-recognition", "date": null, "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Two vans on the first deployment day and one on each subsequent day, with trained officers available to review alerts", "A watchlist of wanted people, suspects and order subjects built from intelligence", "Officers on hand to review alerts and act on them" ], "must_hold": [ "Watchlist entries are current and lawful", "Officers verify each alert before a stop", "Non-matching faces are deleted as the force states" ], "echoes": [] }, "paired_incident": null, "headline": "Huddersfield's first facial recognition vans: three arrests, six checks", "metric": "three arrests and six Sexual Harm Prevention Order stops", "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-21", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2026-08-17-4e1c", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/West_Yorkshire_Police", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced repeated news coverage with police originals and added the deployment register’s counts and limits.", "source": "https://www.westyorkshire.police.uk/news-appeals/cutting-edge-facial-recognition-cameras-used-first-time-kirklees", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's report, the deployer's dataset and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0376", "catalog_version": "1.0.0", "source_fingerprint": "231a8badedc852ef4b62e408af0651fbd1eb5d885134590727f4cc90b476b0c1", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "NEC (NeoFace)", "deployer": "West Yorkshire Police (Kirklees district)" } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "deployer", "developer" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "NEC NeoFace compares camera images with a police watchlist and generates possible matches. Trained officers review the alerts and decide whether to act. The nine recorded alerts and resulting interventions are reported by the deployer; no independent evaluation of these deployments was found." } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "ai_role" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "NEC NeoFace compares camera images with a police watchlist and generates possible matches. Trained officers review the alerts and decide whether to act. The nine recorded alerts and resulting interventions are reported by the deployer; no independent evaluation of these deployments was found." } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. Unrelated requirement assessments retain their existing status.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The force’s own release and deployment register report three arrests and six other interventions over three days. They establish a reported operational result, without independent verification of accuracy or later case outcomes." }, { "group": "societal", "depth": 1, "reason": "The force reported detention of three suspects and six checks of order subjects. No specific prevented harm or subsequent conviction is established." } ] } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. 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The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The force’s own release and deployment register report three arrests and six other interventions over three days. They establish a reported operational result, without independent verification of accuracy or later case outcomes." }, { "group": "societal", "depth": 1, "reason": "The force reported detention of three suspects and six checks of order subjects. 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}, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "West Yorkshire Police used live facial recognition in Huddersfield on August 12–14, 2026. The force reported three arrests, involving a domestic stalking suspect, a person wanted on a warrant, and a shoplifting suspect, along with six Sexual Harm Prevention Order checks. Its deployment register records five alerts on August 12 and two on each subsequent day. All nine were recorded as confirmed true alerts. The estimated numbers of faces seen were 5,824, 2,951, and 4,100, totaling 12,875. These are the force’s operational records, not an independent accuracy study. Zero recorded false alerts does not establish that there were no missed matches. The force identifies NEC NeoFace as the matching system and states that trained officers review alerts before intervention. The reported arrests and checks do not establish a conviction or a specific harm that was prevented.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. 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Operational description, NEC NeoFace identification, human review, and data retention.", "title": "Live Facial Recognition", "url": "https://www.westyorkshire.police.uk/about-us/how-we-work/facial-recognition/live-facial-recognition", "written_by": "deployer" } ] } }, "provenance": { "kind": "source_review", "basis": "Replaced repeated news coverage with police originals and added the deployment register’s counts and limits.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0376-source-0001" ], "url": "https://www.westyorkshire.police.uk/news-appeals/cutting-edge-facial-recognition-cameras-used-first-time-kirklees", "title": "Cutting Edge Facial Recognition Cameras Used for First Time in Kirklees", "published_on": "2026-08-17", "relationship": "deployer", "citation": "West Yorkshire Police. Cutting Edge Facial Recognition Cameras Used for First Time in Kirklees. 2026-08-17." }, { "source_refs": [ "AIBD-0376-source-0002" ], "url": "https://www.westyorkshire.police.uk/sites/default/files/2026-10/live-facial-recognition-deployment-register-2026-19102026.docx", "title": "Live facial recognition deployment register 2026", "relationship": "deployer", "citation": "West Yorkshire Police. Live facial recognition deployment register 2026. n.d. Version identified by filename 19102026. Huddersfield rows dated August 12–14, 2026." }, { "source_refs": [ "AIBD-0376-source-0003" ], "url": "https://www.westyorkshire.police.uk/about-us/how-we-work/facial-recognition/live-facial-recognition", "title": "Live Facial Recognition", "relationship": "deployer", "citation": "West Yorkshire Police. Live Facial Recognition. n.d. Operational description, NEC NeoFace identification, human review, and data retention." } ], "excluded_republications": [ { "url": "https://www.examinerlive.co.uk/news/local-news/nine-including-stalker-wanted-shoplifter-34472938", "title": "Nine including stalker and wanted shoplifter caught out in Huddersfield with live facial recognition cameras", "published_on": "2026-08-17", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0376-source-0001" }, { "url": "https://www.bbc.co.uk/news/articles/c70gl5rp19yo", "title": "Arrests after facial recognition tool used in Huddersfield", "published_on": "2026-08-17", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0376-source-0002" }, { "url": "https://www.examinerlive.co.uk/news/local-news/nine-including-stalker-wanted-shoplifter-34472938", "title": "Nine including stalker and wanted shoplifter caught out in Huddersfield with live facial recognition cameras", "published_on": "2026-08-17", "relationship": "Repeated quotation or alternate version of a retained original work", "previous_source_ref": "AIBD-0376-source-0003" }, { "url": "https://oecd.ai/en/incidents/2026-08-17-4e1c", "title": "Live Facial Recognition Leads to Multiple Arrests in Huddersfield", "published_on": "2026-08-17", "relationship": "Retained as source history; replaced by the original evidence or omitted because no distinct evidence is used for the revised claims", "previous_source_ref": "AIBD-0376-source-0004" } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0376-source-0001", "retained_source_order": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "ordering_basis": "The source that directly supports the central case leads. Additional sources contribute the specific evidence or limitations described in claim_lineage.", "claim_lineage": [ { "claim": "Arrests and order checks", "source_chain": [ { "url": "https://www.westyorkshire.police.uk/news-appeals/cutting-edge-facial-recognition-cameras-used-first-time-kirklees", "locator": "August 12–14 operation and intervention descriptions", "role": "Original police announcement", "access_status": "Full relevant text reviewed", "published_on": "2026-08-17" } ], "limitations": [ "All outcome claims originate with the deployer; arrests are not convictions" ], "source_ref": "AIBD-0376-source-0001" }, { "claim": "Nine alerts, three arrests, six other interventions, and estimated 12,875 faces", "source_chain": [ { "url": "https://www.westyorkshire.police.uk/sites/default/files/2026-10/live-facial-recognition-deployment-register-2026-19102026.docx", "locator": "Table rows 12/08/2026, 13/08/2026, and 14/08/2026; columns for confirmed alerts, arrests, other interventions, and estimated faces", "role": "Original operational register; DOCX table reviewed", "access_status": "Full relevant text reviewed" } ], "limitations": [ "Sums: alerts 5+2+2, faces 5824+2951+4100", "All nine alerts are recorded as true confirmed; the register does not measure missed matches", "Filename indicates the October 19 version, not a verified publication date" ], "source_ref": "AIBD-0376-source-0002" }, { "claim": "System identity and review procedure", "source_chain": [ { "url": "https://www.westyorkshire.police.uk/about-us/how-we-work/facial-recognition/live-facial-recognition", "locator": "NEC NeoFace, trained-officer review, and retention sections", "role": "Original deployment-policy account", "access_status": "Full relevant text reviewed" } ], "limitations": [ "A police policy description is not independent assurance that every deployment complied" ], "source_ref": "AIBD-0376-source-0003" } ], "limitations": [ "No independent evaluation of the three deployments; BBC full text could not be re-opened and is not used for retained claims" ] } }, "provenance": { "kind": "source_review", "basis": "Replaced repeated news coverage with police originals and added the deployment register’s counts and limits.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The retained accounts describe three deployments without an independent evaluation. The BBC article’s full text was inaccessible.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "West Yorkshire Police reported three arrests and six Sexual Harm Prevention Order checks in Huddersfield on August 12–14, 2026. Its register records nine alerts and an estimated 12,875 faces seen.", "account": "West Yorkshire Police used live facial recognition in Huddersfield on August 12–14, 2026. The force reported three arrests, involving a domestic stalking suspect, a person wanted on a warrant, and a shoplifting suspect, along with six Sexual Harm Prevention Order checks. Its deployment register records five alerts on August 12 and two on each subsequent day. All nine were recorded as confirmed true alerts. The estimated numbers of faces seen were 5,824, 2,951, and 4,100, totaling 12,875. These are the force’s operational records, not an independent accuracy study. Zero recorded false alerts does not establish that there were no missed matches. The force identifies NEC NeoFace as the matching system and states that trained officers review alerts before intervention. The reported arrests and checks do not establish a conviction or a specific harm that was prevented." } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. 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Unrelated requirement assessments retain their existing status.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0376-source-0001", "AIBD-0376-source-0002", "AIBD-0376-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "NEC (NeoFace)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "West Yorkshire Police's first live facial recognition deployment in Huddersfield: three arrests, including a domestic stalking suspect, and six Sexual Harm Prevention Order checks over three days", "headline": "Huddersfield's first facial recognition vans: three arrests, six checks" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "231a8badedc852ef4b62e408af0651fbd1eb5d885134590727f4cc90b476b0c1" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version after the October 24, 2026 primary-source review.", "record_fields": [], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "West Yorkshire Police used live facial recognition in Huddersfield on August 12–14, 2026. 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The reported arrests and checks do not establish a conviction or a specific harm that was prevented.", "replication": { "produced_by": [ "Two vans on the first deployment day and one on each subsequent day, with trained officers available to review alerts", "A watchlist of wanted people, suspects and order subjects built from intelligence", "Officers on hand to review alerts and act on them" ], "must_hold": [ "Watchlist entries are current and lawful", "Officers verify each alert before a stop", "Non-matching faces are deleted as the force states" ], "echoes": [] } } }, "provenance": { "kind": "curatorial", "basis": "Updated affected case fields after the dated primary-source review. 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State Grid Shandong is named among test settings. Better renewable-power forecasting and grid operations are intended applications, while the source does not quantify resulting dispatch, reliability, or emissions changes.", "category": "general benefit", "unexpected": false, "purpose": "Improve weather forecasts for power, agriculture, and other weather-sensitive uses.", "system": "Baguan weather-forecasting model", "model_version": null, "developer": "Alibaba DAMO Academy", "deployer": "State Grid Shandong Electric Power Co., Ltd. (test setting)", "location": "Shandong, China (test setting)", "domain": "Environment and climate", "segment": null, "benefit_type": "Improved Task Accuracy", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The reported regional forecast-error improvement over ECMWF-IFS is 57.8%, with additional global comparisons. Resulting grid improvements remain unmeasured." }, { "group": "societal", "depth": null, "reason": "Renewable-energy uptake and climate benefits are anticipated downstream effects." } ], "ai_role": { "status": "unassessed", "statement": "The account does not isolate AI’s contribution from the surrounding workflow." }, "evidence": [ { "kind": "report", "citation": "International Telecommunication Union, AI for Good: Innovate for Impact (2025). Project-submitted account, current as of June 2025. ITU provides no independent outcome evaluation.", "title": "Use Case 2: AI-Powered High-Precision Weather Forecasting Model", "url": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-182", "date": null, "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "The account does not establish conditions sufficient to reproduce the benefit." ], "must_hold": [ "The account does not establish requirements for reproducing the benefit in other settings." ], "echoes": [] }, "paired_incident": null, "headline": "Baguan reports improved regional and global weather forecasts", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": null, "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-22", "change": "Added the case.", "source": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-182", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' report. 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Resulting grid improvements remain unmeasured." }, { "group": "societal", "reason": "Renewable-energy uptake and climate benefits are anticipated downstream effects." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: the makers' report. 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Resulting grid improvements remain unmeasured." }, { "group": "societal", "reason": "Renewable-energy uptake and climate benefits are anticipated downstream effects." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "value_present", "value": { "kind": "text", "text": "Reported and intended benefits" }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "benefit_status" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The reported regional forecast-error improvement over ECMWF-IFS is 57.8%, with additional global comparisons. Resulting grid improvements remain unmeasured." }, { "group": "societal", "reason": "Renewable-energy uptake and climate benefits are anticipated downstream effects." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": 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"Hertfordshire and Robotiz3d trialed the ARRES Prevent robot, which uses AI to detect road defects and fill cracks. The pilot reportedly repaired a small number of cracks. The project reports detection accuracy increasing from 66% to 100% in testing. Long-term repair performance, pothole prevention, cost savings, and population safety effects remain unestablished.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The public-road pilot actually repaired a small number of cracks, demonstrating a new operational capability." }, { "group": "societal", "reason": "Fewer potholes, injuries, and dangerous maintenance exposures are intended. No achieved safety benefit is described." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_status": "mixed" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_status" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify road defects and repair cracks before they develop into larger maintenance problems." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "United Kingdom" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Identify road defects and repair cracks before they develop into larger maintenance problems." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-01-28", "date_basis": "source publication", "date_precision": "day", "revisions": [ { "date": "2026-10-22", "change": "Added the case.", "source": "https://www.local.gov.uk/case-studies/hertfordshire-county-council-ai-road-maintenance-robot", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "The development partnership used Hertfordshire road data and highway-engineering feedback.", "The reported pilot involved a prototype on a public road, with further tests and long-term repair review planned." ], "must_hold": [ "The conditions needed to reproduce the benefit are unspecified." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-22", "change": "Added the case.", "source": "https://www.local.gov.uk/case-studies/hertfordshire-county-council-ai-road-maintenance-robot", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Transport infrastructure maintenance" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Council or project account published in the Local Government Association AI case-study bank. 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Long-term repair quality, safety, and cost benefits remain unmeasured.", "account": "Hertfordshire and Robotiz3d trialed the ARRES Prevent robot, which uses AI to detect road defects and fill cracks. The pilot reportedly repaired a small number of cracks. The project reports detection accuracy increasing from 66% to 100% in testing. Long-term repair performance, pothole prevention, cost savings, and population safety effects remain unestablished." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Robotiz3d ARRES Eye and ARRES Prevent road-maintenance robot" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Robotiz3d, developed in collaboration with the University of Liverpool and Hertfordshire County Council" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Hertfordshire County Council: AI road maintenance robot", "headline": "Hertfordshire trials an AI robot that repairs road cracks" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "37025be8ffdec74f23b50f9f5ac4c7c6075d99d68db6c93dfd70afbe2fc766a2" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Hertfordshire and Robotiz3d trialed the ARRES Prevent robot, which uses AI to detect road defects and fill cracks. The pilot reportedly repaired a small number of cracks. The project reports detection accuracy increasing from 66% to 100% in testing. Long-term repair performance, pothole prevention, cost savings, and population safety effects remain unestablished.", "replication": { "produced_by": [ "The development partnership used Hertfordshire road data and highway-engineering feedback.", "The reported pilot involved a prototype on a public road, with further tests and long-term repair review planned." ], "must_hold": [ "The conditions needed to reproduce the benefit are unspecified." ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0098", "case_url": "records/AIBD-0098.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall." }, { "label": "AI contribution", "value": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Philippines" ] }, "record": { "id": "AIBD-0098", "title": "Philippine Red Cross activated its typhoon Early Action Protocol on 510's machine-learning impact forecast before Storm Nalgae (October 2022)", "summary": "510 ML impact forecast hit the trigger on 27 October 2022; PRC installed shelter kits for 78 Aurora households before Storm Nalgae", "date_observed": "2022-10-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "category": "protective response", "unexpected": false, "purpose": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall.", "system": "510 impact-based forecasting (IBF) model for typhoons in the Philippines, operated through the 510 dashboard with PAGASA forecasts", "model_version": "eXtreme Gradient Boosting (XGBoost)", "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding", "location": "Dipaculao, Aurora Province, Philippines", "domain": "Disaster risk reduction and forecast-based financing", "segment": "Red Cross National Society", "benefit_type": "Reduced Risk", "taxonomy_note": "Shelter reinforcement before the storm reduces potential harm, but the post-storm survey gives no counterfactual.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ], "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." }, "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ], "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "model_version": null, "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "severity": null, "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way." }, "headline": "510's ML forecast triggers PRC shelter kits before Storm Nalgae", "metric": "installed shelter kits for 78 Aurora households before Storm Nalgae", "comparator": null, "later": { "status": "persisted", "date": "2023-06-30", "note": "The IFRC final report of 30 June 2023 records all 78 households served and houses reinforced before impact, but a PRC lessons-learned workshop judged the trigger high and recommended reviewing the typhoon EAP.", "quote": "In addition, the trigger was considered high according to the solicited lessons from Paeng Lesson learned Workshop conducted by PRC with the support IFRC. Thus, one of the recommendations gathered is to review and revisit the Typhoon EAP.", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Philippines", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the final report's date to its cover date of 30 June 2023 (18 January 2024 was the PDF file timestamp) and the notification's date to its launch date of 27 October 2022, marked the NHESS paper and EGU abstract independent because 510 staff are co-authors rather than lead authors, and restated the EAP trigger as at least 10% of houses totally damaged in more than three municipalities 72 hours before landfall, with the lead-time condition waived at 56 hours.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://adore.ifrc.org/Download.aspx?FileId=590819; https://adore.ifrc.org/Download.aspx?FileId=271567", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://nhess.copernicus.org/articles/24/309/2024/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://en.wikipedia.org/wiki/Philippine_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0098", "catalog_version": "1.0.0", "source_fingerprint": "4d27685232098c66182082682b2e51601106d680d084eeb7882325e3cdc5c1ac", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0098-source-0001", "AIBD-0098-source-0002", "AIBD-0098-source-0003", "AIBD-0098-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The 510 model forecast that damage to housing would exceed the threshold for early action in Aurora. Its forecasts incorporated data from PAGASA.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. 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PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the 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{ "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "installed shelter kits for 78 Aurora households before Storm Nalgae", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The planned 72-hour lead time was not met; the final report records an exceptional approval with 56 hours remaining. The Philippine Red Cross’s own survey supplies the reported benefits after the storm.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2023-06-30", "note": "The IFRC final report of 30 June 2023 records all 78 households served and houses reinforced before impact, but a PRC lessons-learned workshop judged the trigger high and recommended reviewing the typhoon EAP.", "quote": "In addition, the trigger was considered high according to the solicited lessons from Paeng Lesson learned Workshop conducted by PRC with the support IFRC. Thus, one of the recommendations gathered is to review and revisit the Typhoon EAP.", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Philippine Red Cross staff checked local capacity and selected the municipalities for action." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0098" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Shelter reinforcement before the storm reduces potential harm, but the post-storm survey gives no counterfactual." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. 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Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. 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EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0098-source-0001", "AIBD-0098-source-0002", "AIBD-0098-source-0003", "AIBD-0098-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). 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Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "510 ML impact forecast hit the trigger on 27 October 2022; PRC installed shelter kits for 78 Aurora households before Storm Nalgae", "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model forecast damage to housing before a typhoon." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "XGBoost." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "eXtreme Gradient Boosting (XGBoost)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "510 impact-based forecasting (IBF) model for typhoons in the Philippines, operated through the 510 dashboard with PAGASA forecasts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Philippine Red Cross activated its typhoon Early Action Protocol on 510's machine-learning impact forecast before Storm Nalgae (October 2022)", "headline": "510's ML forecast triggers PRC shelter kits before Storm Nalgae" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "4d27685232098c66182082682b2e51601106d680d084eeb7882325e3cdc5c1ac" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 35, "reviewed": 0, "record_mapped": 35, "not_assessed": 56, "complete": false, "by_state": { "value_present": 35, "not_reported": 0, "unresolved": 0, "not_assessed": 56, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0418", "case_url": "records/AIBD-0418.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Improve hydrological analysis, reporting, and planning." }, { "label": "AI contribution", "value": "The account does not isolate AI’s contribution from the surrounding workflow." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "mixed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0418", "title": "LLM empowered smart hydrology", "summary": "ZTE reports approximately 30% less hydrology report-writing time and 20% better line-drawing efficiency in its operational hydrology application.", "date_observed": "2025-06-01", "date_basis": "source information", "date_precision": "month", "benefit_status": "mixed", "source_collection": "ITU AI for Good: Innovate for Impact (2025)", "source_entry": "ITU-034", "account": "ZTE describes an operational hydrology model with the Changjiang Hydrological Bureau as a partner. It reports shorter report writing, faster thalweg drawing, and prediction and query accuracy figures. The system is intended to support water management and flood planning. Specific flood interventions and prevented losses remain undocumented.", "category": "general benefit", "unexpected": false, "purpose": "Improve hydrological analysis, reporting, and planning.", "system": "ZTE Nebula hydrology model", "model_version": null, "developer": "ZTE Corporation", "deployer": "Changjiang Hydrological Bureau", "location": null, "domain": "Environment and climate", "segment": null, "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 1, "reason": "The operational ZTE/Changjiang Hydrological Bureau case reports approximately 30% less report-writing time." }, { "group": "societal", "depth": null, "reason": "No flood-response decision or prevented loss is documented." } ], "ai_role": { "status": "unassessed", "statement": "The account does not isolate AI’s contribution from the surrounding workflow." }, "evidence": [ { "kind": "report", "citation": "International Telecommunication Union, AI for Good: Innovate for Impact (2025). 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Its September 2025 report attributes an alert for the August 28 fire to wi.breathe and quotes the mayor supporting that account. Contemporary fire coverage does not verify the alert or establish how much it changed the response. The AI contribution remains a first-party claim." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "reported", "jurisdiction": [ "Greece" ] }, "record": { "id": "AIBD-0329", "title": "wi.breathe cameras alert authorities to a Troizina wildfire, according to WINGS and the mayor", "summary": "WINGS and Troizinia-Methana’s mayor credit wi.breathe with an August 2025 wildfire alert. Contemporary reporting describes spread and a substantial firefighting response.", "date_observed": "2025-08-28", "date_basis": "event", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "WINGS reported on September 2, 2025 that its wi.breathe cameras in Methana had detected a wildfire in Troizina on August 28 and alerted authorities. The company quoted Mayor Tasos Ath. Mougios crediting the system with preventing a larger disaster. No alert log, detection time, response chronology, or estimate of avoided damage accompanies that account. Contemporary reporting by Saronic Magazine describes the fire spreading uphill, hours of firefighting, precautionary evacuations, and damage to a shed. The municipality’s same-day statement credits a coordinated response involving more than 80 firefighters and six aircraft. Those accounts establish the scale of the fire and response, but neither verifies the AI’s contribution. 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The AI contribution remains a first-party claim." }, "evidence": [ { "kind": "press release", "citation": "WINGS ICT Solutions, “Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action,” September 2, 2025, opening paragraphs and mayor’s statement; event dated August 28, 2025.", "title": "Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action", "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "date": "2025-09-02", "written_by": "makers", "quote": null }, { "kind": "press release", "citation": "WINGS ICT Solutions, “WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros,” May 12, 2026, installation sites and system-capabilities paragraphs.", "title": "WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros", "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "date": "2026-05-12", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Monitoring cameras that feed an AI smoke and fire detector", "Alerts that reach civil-protection staff who can investigate and mobilize a response" ], "must_hold": [ "Camera coverage and image quality must permit detection of the developing fire", "Connectivity and notification channels must deliver alerts promptly", "People and firefighting resources must be available to act on an alert" ], "echoes": [] }, "paired_incident": null, "headline": "wi.breathe cameras reportedly flag a Troizina wildfire", "metric": null, "comparator": null, "later": { "status": "unknown", "date": "2026-05-12", "note": "WINGS announced completion of the wider installation. The announcement supplies no evaluation of the August fire alert or quantified reduction in fire losses.", "quote": null, "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/" }, "value": null, "developer_country": "Greece", "deployer_country": "Greece", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wings-ict-solutions.eu/contact/; https://en.wikipedia.org/wiki/Troizinia-Methana", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "source": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: two press releases from the makers. 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}, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-05-12-c1ff" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "registry_ids" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "date": "2026-05-12", "note": "WINGS announced completion of the wider installation. The announcement supplies no evaluation of the August fire alert or quantified reduction in fire losses.", "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/" } } }, "provenance": { "kind": "source_review", "basis": "August 28, 2025 is the event date in the original September 2 vendor report. May 12, 2026 is the later completion announcement. Benefit status remains a reported first-party claim.", "record_fields": [ "later" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0329" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "The claimed benefit is an earlier warning that could limit wildfire damage. Avoided damage has not been quantified." } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "WINGS reported on September 2, 2025 that its wi.breathe cameras in Methana had detected a wildfire in Troizina on August 28 and alerted authorities. The company quoted Mayor Tasos Ath. Mougios crediting the system with preventing a larger disaster. No alert log, detection time, response chronology, or estimate of avoided damage accompanies that account. Contemporary reporting by Saronic Magazine describes the fire spreading uphill, hours of firefighting, precautionary evacuations, and damage to a shed. The municipality’s same-day statement credits a coordinated response involving more than 80 firefighters and six aircraft. Those accounts establish the scale of the fire and response, but neither verifies the AI’s contribution. WINGS later announced completion of the wider camera installation on May 12, 2026. The one-minute detection figure in circulated completion publicity is a capability claim, not a measured result for the August fire.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "societal", "reason": "The developer and mayor attribute an early warning and avoided escalation to the system. No alert log, causal comparison, or estimate of avoided damage supports a depth assessment." }, { "group": "organizational", "reason": "The municipality’s response is documented, but the sources do not measure any reduction in response time, staffing, or cost attributable to AI." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].status": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_status": "reported" } }, "provenance": { "kind": "source_review", "basis": "August 28, 2025 is the event date in the original September 2 vendor report. May 12, 2026 is the later completion announcement. Benefit status remains a reported first-party claim.", "record_fields": [ "benefit_status" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Detect wildfire smoke and fire from monitoring cameras and alert civil-protection authorities so that they can respond earlier." } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Troizinia-Methana and Poros, Attica, Greece", "developer_country": "Greece", "deployer_country": "Greece" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Detect wildfire smoke and fire from monitoring cameras and alert civil-protection authorities so that they can respond earlier." } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "purpose" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The system detects smoke and fire in camera images and alerts civil-protection staff.", "editor_note": "" } }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-08-28", "date_basis": "event", "date_precision": "day", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wings-ict-solutions.eu/contact/; https://en.wikipedia.org/wiki/Troizinia-Methana", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "source": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "August 28, 2025 is the event date in the original September 2 vendor report. May 12, 2026 is the later completion announcement. Benefit status remains a reported first-party claim.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Monitoring cameras that feed an AI smoke and fire detector", "Alerts that reach civil-protection staff who can investigate and mobilize a response" ], "must_hold": [ "Camera coverage and image quality must permit detection of the developing fire", "Connectivity and notification channels must deliver alerts promptly", "People and firefighting resources must be available to act on an alert" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "replication" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Monitoring cameras that feed an AI smoke and fire detector", "Alerts that reach civil-protection staff who can investigate and mobilize a response" ], "must_hold": [ "Camera coverage and image quality must permit detection of the developing fire", "Connectivity and notification channels must deliver alerts promptly", "People and firefighting resources must be available to act on an alert" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "replication" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.wings-ict-solutions.eu/contact/; https://en.wikipedia.org/wiki/Troizinia-Methana", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "source": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Civil protection: wildfire early detection", "segment": "Camera-based AI smoke and fire detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "value_present", "value": { "kind": "structured", "data": { "sources": [ { "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "access": "Complete original developer article read; no linked evidence appendix or source dataset." }, { "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "access": "Complete original developer completion announcement read." }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95660-elegxomeni-i-fotia-stin-troizina-entiposiaki-epixeirisi-pirosveston-eikones-video", "access": "Complete article text and HTML read, including publication metadata; videos not watched.", "version": "datePublished 2025-08-28T13:44:25Z; dateModified 2025-08-28T13:57:48Z" }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95666-o-dimos-troizinias-methanon-gia-tin-fotia-stin-troizina", "access": "Complete reproduced municipal statement read; the linked Facebook original was unavailable." }, { "url": "https://www.facebook.com/dimostroiziniasmethanon/posts/pfbid02PwULaeXWuoacsACFyQju7oTDJCPwKTVf8YAEow9rcbwPmxuCMj1NLAMAy7PdL5zxl", "access": "Embedded original Facebook post could not be retrieved; not inspected." }, { "url": "https://www.youtube.com/watch?v=3QSYpGGrGyc", "access": "Linked video identified in source HTML; not watched." }, { "url": "https://www.youtube.com/watch?v=k9Rqxn2RO0g", "access": "Linked video identified in source HTML; not watched." }, { "url": "https://twitter.com/pyrosvestiki/status/1961025860246651093", "access": "Direct retrieval returned 403. Embedded text read at https://www.anagnostis.org/2025/08/28/fotia-sto-diavologefyro-troizinas-sikothikan-6-enaeria-mesa-katasvesis-video/ ." }, { "url": "https://www.wings-ict-solutions.eu/wi-breathe/", "access": "Complete product text read; generic content, not event evidence." }, { "url": "https://govnews.gr/oikonomia/epicheiriseis/370285/pyranichnefsi-me-techniti-noimosyni-to-neo-systima-ston-dimo-troizinias-methanon-pou-entopizei-foties-se-1-lepto/", "access": "Greek completion publicity text read." }, { "url": "https://www.newsbeast.gr/greece/arthro/13091861/me-technologia-ai-kai-5g-i-pyranichnefsi-se-perioches-tou-dimou-troizinias-methanon", "access": "Greek deployment and capability paragraphs read; no separate measured alert documented." }, { "url": "https://www.reporter.gr/eidhseis/technologia/674608-wings-me-texnologia-ai-kai-5g-i-pyranixnefsi-se-perioxes-tou-dimou-troizinias-methanon", "access": "Direct web retrieval unavailable in this audit; prior source classification preserved without claiming a fresh full-text read." }, { "url": "https://dimostroizinias-methanon.gr/?s=wi.breathe", "access": "Municipal search-result HTML read and the February 2025 award link located; award article not read in this audit." } ] } }, "provenance": { "kind": "source_review", "basis": "Recorded inspected written versions and unavailable original social/video material.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The developer’s releases and contemporary fire reports are available. The municipality’s Facebook original and the Fire Service post were accessible only through reproductions. The linked videos were not inspected.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "WINGS ICT Solutions, “Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action,” September 2, 2025, opening paragraphs and mayor’s statement; event dated August 28, 2025.", "title": "Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action", "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "date": "2025-09-02", "written_by": "makers" }, { "kind": "press release", "citation": "WINGS ICT Solutions, “WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros,” May 12, 2026, installation sites and system-capabilities paragraphs.", "title": "WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros", "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "date": "2026-05-12", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "role": "Retained source entries in reviewed order for this fingerprinted record version" }, "provenance": { "kind": "source_review", "basis": "Original AI claim first, followed by the distinct later deployment announcement. Event-context accounts are separately identified and do not corroborate AI efficacy.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "WINGS ICT Solutions, “Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action,” September 2, 2025, opening paragraphs and mayor’s statement; event dated August 28, 2025.", "title": "Early Wildfire Detection in Trizina by WINGS’ wi.breathe – Innovation in Action", "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "date": "2025-09-02", "written_by": "makers" }, { "kind": "press release", "citation": "WINGS ICT Solutions, “WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros,” May 12, 2026, installation sites and system-capabilities paragraphs.", "title": "WINGS ICT Solutions delivers wi.breathe for early wildfire detection and enhanced environmental intelligence in Troizinia–Methana & Poros", "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "date": "2026-05-12", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "origins": [ { "source_refs": [ "AIBD-0329-source-0001" ], "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "published_on": "2025-09-02", "relationship": "Original AI event claim by the developer, including a municipal testimonial." }, { "source_refs": [ "AIBD-0329-source-0002" ], "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "published_on": "2026-05-12", "relationship": "Later original installation announcement with technical and site details." } ], "excluded_republications": [ { "url": "https://oecd.ai/en/incidents/2026-05-12-c1ff", "relationship": "AI-generated registry summary substitutes a one-minute capability claim for a measured event result; it is not independent corroboration." }, { "url": "https://govnews.gr/oikonomia/epicheiriseis/370285/pyranichnefsi-me-techniti-noimosyni-to-neo-systima-ston-dimo-troizinias-methanon-pou-entopizei-foties-se-1-lepto/", "relationship": "Circulated May 2026 project-completion publicity adds no independently verified alert. The undated detection sentence is superseded by the earlier original dated claim, and the one-minute capability is not treated as an event measurement." }, { "url": "https://www.newsbeast.gr/greece/arthro/13091861/me-technologia-ai-kai-5g-i-pyranichnefsi-se-perioches-tou-dimou-troizinias-methanon", "relationship": "Circulated May 2026 project-completion publicity adds no independently verified alert. The undated detection sentence is superseded by the earlier original dated claim, and the one-minute capability is not treated as an event measurement." }, { "url": "https://www.reporter.gr/eidhseis/technologia/674608-wings-me-texnologia-ai-kai-5g-i-pyranixnefsi-se-perioxes-tou-dimou-troizinias-methanon", "relationship": "Circulated May 2026 project-completion publicity adds no independently verified alert. The undated detection sentence is superseded by the earlier original dated claim, and the one-minute capability is not treated as an event measurement." }, { "url": "https://www.wings-ict-solutions.eu/wi-breathe/", "relationship": "Generic product details retained as related material, not counted as a separate event report." }, { "url": "https://dimostroizinias-methanon.gr/?s=wi.breathe", "relationship": "Municipality search results about a February 2025 award do not establish the wildfire alert or its benefit." } ], "related_material": [ { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95660-elegxomeni-i-fotia-stin-troizina-entiposiaki-epixeirisi-pirosveston-eikones-video", "title": "Ελεγχόμενη η φωτιά στην Τροιζήνα – Εντυπωσιακή επιχείρηση πυροσβεστών (Εικόνες & Video)", "author": "Μπάμπης Κανατσίδης", "published_on": "2025-08-28", "relationship": "Independent original local reporting adds spread, evacuations, firefighting, and shed-damage context; it does not report the AI contribution.", "locator": "Article body paragraphs 1–2 and 5–6" }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95666-o-dimos-troizinias-methanon-gia-tin-fotia-stin-troizina", "title": "Ο Δήμος Τροιζηνίας - Μεθάνων για την φωτιά στην Τροιζήνα", "published_on": "2025-08-28", "relationship": "Municipality statement reproduced by Saronic Magazine. It describes responders and response timing, without attributing an alert to wi.breathe.", "original_url": "https://www.facebook.com/dimostroiziniasmethanon/posts/pfbid02PwULaeXWuoacsACFyQju7oTDJCPwKTVf8YAEow9rcbwPmxuCMj1NLAMAy7PdL5zxl", "locator": "Opening two paragraphs and acknowledgments" }, { "url": "https://www.wings-ict-solutions.eu/wi-breathe/", "relationship": "Generic developer product description adds operator investigation after a smoke alert, but does not document this event.", "locator": "wi.breathe-fire, “Fire Detection and Prevention”" } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0329-source-0001", "retained_source_order": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "ordering_basis": "Lead with the original dated AI event account. Retain the later completion statement for its deployment details, and keep event-only context separate from AI corroboration.", "claim_lineage": [ { "claim": "wi.breathe detected the August 28, 2025 Troizina wildfire and alerted authorities, according to WINGS and its quoted mayor.", "source_ref": "AIBD-0329-source-0001", "source_chain": [ { "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "locator": "Opening two paragraphs and paragraph quoting Mayor Tasos Ath. Mougios", "role": "Original developer report with a deployer testimonial" } ], "method": { "event_date": "2025-08-28", "publication_date": "2025-09-02" }, "limitations": [ "No alert logs, recorded detection time, first-notification comparison, response chronology, or avoided-loss estimate accompanies the account.", "The mayor’s testimonial is published by the developer; it is not independent verification." ] }, { "claim": "The completed installation uses optical-thermal cameras at three named locations, AI detection, cloud infrastructure, and telecommunications links.", "source_ref": "AIBD-0329-source-0002", "source_chain": [ { "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "locator": "Paragraphs describing hybrid optical-thermal cameras, Vigla on Poros, Tsepeleni Aderon, Agios Georgios Methanon, and alerts", "role": "Original developer deployment announcement" } ], "method": {}, "limitations": [ "The May 2026 completion announcement does not establish a new dated fire or provide performance evaluation." ] }, { "claim": "The August 28 fire spread and required extensive human firefighting.", "source_ref": "https://www.saronicmagazine.gr/saronikos/troizina/95660-elegxomeni-i-fotia-stin-troizina-entiposiaki-epixeirisi-pirosveston-eikones-video", "source_chain": [ { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95660-elegxomeni-i-fotia-stin-troizina-entiposiaki-epixeirisi-pirosveston-eikones-video", "locator": "Article body paragraphs 1–2 and 5–6, dated August 28, 2025, 16:44", "role": "Contemporary local reporting with exclusive scene photographs and video links" }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95666-o-dimos-troizinias-methanon-gia-tin-fotia-stin-troizina", "locator": "Opening paragraphs, firefighting-service acknowledgment, and paragraph naming six aircraft within 30 minutes", "role": "Municipality statement reproduced by Saronic Magazine" }, { "url": "https://www.facebook.com/dimostroiziniasmethanon/posts/pfbid02PwULaeXWuoacsACFyQju7oTDJCPwKTVf8YAEow9rcbwPmxuCMj1NLAMAy7PdL5zxl", "locator": "Embedded Facebook post linked from the reproduced statement", "role": "Unretrieved municipal social-media original" } ], "method": {}, "limitations": [ "Neither account identifies the AI alert as the first notification or measures the AI contribution.", "Reports of spread and damage qualify the broad claim that spread was prevented; they do not disprove that an alert helped to limit further escalation.", "Different contemporaneous resource and elapsed-time figures are not forced into one reconciled chronology." ] } ], "limitations": [ "No original AI evaluation, incident log, footnote, appendix, or supplementary dataset is linked from the developer event report or completion announcement.", "The one-minute capability claim in the May 2026 Greek release is not a measured time for the August 2025 event.", "The May 2026 undated trial-detection sentence is not proven to describe a separate fire; it is not counted as a second success.", "Saronic Magazine’s independent fire coverage and the municipality’s statement provide material event context, not independent validation of AI efficacy.", "The municipality’s Facebook original and the two linked Saronic YouTube videos were not inspected.", "The Fire Service X post could not be retrieved directly. Its embedded text was read in Anagnostis, but no fire-resource figure is attributed to an unseen original." ], "access_audit": [ { "url": "https://www.wings-ict-solutions.eu/early-wildfire-detection-in-trizina-by-wings-wi-breathe-innovation-in-action/", "access": "Complete original developer article read; no linked evidence appendix or source dataset." }, { "url": "https://www.wings-ict-solutions.eu/wings-ict-solutions-delivers-wi-breathe-for-early-wildfire-detection-and-enhanced-environmental-intelligence-in-troizinia-methana-poros/", "access": "Complete original developer completion announcement read." }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95660-elegxomeni-i-fotia-stin-troizina-entiposiaki-epixeirisi-pirosveston-eikones-video", "access": "Complete article text and HTML read, including publication metadata; videos not watched.", "version": "datePublished 2025-08-28T13:44:25Z; dateModified 2025-08-28T13:57:48Z" }, { "url": "https://www.saronicmagazine.gr/saronikos/troizina/95666-o-dimos-troizinias-methanon-gia-tin-fotia-stin-troizina", "access": "Complete reproduced municipal statement read; the linked Facebook original was unavailable." }, { "url": "https://www.facebook.com/dimostroiziniasmethanon/posts/pfbid02PwULaeXWuoacsACFyQju7oTDJCPwKTVf8YAEow9rcbwPmxuCMj1NLAMAy7PdL5zxl", "access": "Embedded original Facebook post could not be retrieved; not inspected." }, { "url": "https://www.youtube.com/watch?v=3QSYpGGrGyc", "access": "Linked video identified in source HTML; not watched." }, { "url": "https://www.youtube.com/watch?v=k9Rqxn2RO0g", "access": "Linked video identified in source HTML; not watched." }, { "url": "https://twitter.com/pyrosvestiki/status/1961025860246651093", "access": "Direct retrieval returned 403. Embedded text read at https://www.anagnostis.org/2025/08/28/fotia-sto-diavologefyro-troizinas-sikothikan-6-enaeria-mesa-katasvesis-video/ ." }, { "url": "https://www.wings-ict-solutions.eu/wi-breathe/", "access": "Complete product text read; generic content, not event evidence." }, { "url": "https://govnews.gr/oikonomia/epicheiriseis/370285/pyranichnefsi-me-techniti-noimosyni-to-neo-systima-ston-dimo-troizinias-methanon-pou-entopizei-foties-se-1-lepto/", "access": "Greek completion publicity text read." }, { "url": "https://www.newsbeast.gr/greece/arthro/13091861/me-technologia-ai-kai-5g-i-pyranichnefsi-se-perioches-tou-dimou-troizinias-methanon", "access": "Greek deployment and capability paragraphs read; no separate measured alert documented." }, { "url": "https://www.reporter.gr/eidhseis/technologia/674608-wings-me-texnologia-ai-kai-5g-i-pyranixnefsi-se-perioxes-tou-dimou-troizinias-methanon", "access": "Direct web retrieval unavailable in this audit; prior source classification preserved without claiming a fresh full-text read." }, { "url": "https://dimostroizinias-methanon.gr/?s=wi.breathe", "access": "Municipal search-result HTML read and the February 2025 award link located; award article not read in this audit." } ], "scope": "Complete lineage and access review of the retained evidence. Unavailable linked media and missing underlying evaluation data are explicitly unresolved." } }, "provenance": { "kind": "source_review", "basis": "Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "evidence", "account", "ai_role" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The developer and mayor credit the system with an early alert. Independent coverage documents the fire and response, but no alert log or AI evaluation is available. A later statement about one-minute detection does not measure this incident.", "editor_note": "" } }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "WINGS and Troizinia-Methana’s mayor credit wi.breathe with an August 2025 wildfire alert. Contemporary reporting describes spread and a substantial firefighting response.", "account": "WINGS reported on September 2, 2025 that its wi.breathe cameras in Methana had detected a wildfire in Troizina on August 28 and alerted authorities. The company quoted Mayor Tasos Ath. Mougios crediting the system with preventing a larger disaster. No alert log, detection time, response chronology, or estimate of avoided damage accompanies that account. Contemporary reporting by Saronic Magazine describes the fire spreading uphill, hours of firefighting, precautionary evacuations, and damage to a shed. The municipality’s same-day statement credits a coordinated response involving more than 80 firefighters and six aircraft. Those accounts establish the scale of the fire and response, but neither verifies the AI’s contribution. WINGS later announced completion of the wider camera installation on May 12, 2026. The one-minute detection figure in circulated completion publicity is a capability claim, not a measured result for the August fire." } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "wi.breathe Operational Intelligence Platform (WINGS ICT Solutions): hybrid optical-thermal cameras, a WINGS-trained AI smoke and fire detection model, cloud infrastructure and 5G/4G links with dashboard, email and SMS alerts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "WINGS ICT Solutions, Athens, Greece" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "wi.breathe cameras alert authorities to a Troizina wildfire, according to WINGS and the mayor", "headline": "wi.breathe cameras reportedly flag a Troizina wildfire" } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "title", "headline" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "ead86e8406297a25dc8cf1f73235c13e3eb830a573cec1818b3499f88eea48ca" ], "role": "SHA-256 of normalized current case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version of the reviewed case record.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "WINGS reported on September 2, 2025 that its wi.breathe cameras in Methana had detected a wildfire in Troizina on August 28 and alerted authorities. The company quoted Mayor Tasos Ath. Mougios crediting the system with preventing a larger disaster. No alert log, detection time, response chronology, or estimate of avoided damage accompanies that account. Contemporary reporting by Saronic Magazine describes the fire spreading uphill, hours of firefighting, precautionary evacuations, and damage to a shed. The municipality’s same-day statement credits a coordinated response involving more than 80 firefighters and six aircraft. Those accounts establish the scale of the fire and response, but neither verifies the AI’s contribution. WINGS later announced completion of the wider camera installation on May 12, 2026. The one-minute detection figure in circulated completion publicity is a capability claim, not a measured result for the August fire.", "replication": { "produced_by": [ "Monitoring cameras that feed an AI smoke and fire detector", "Alerts that reach civil-protection staff who can investigate and mobilize a response" ], "must_hold": [ "Camera coverage and image quality must permit detection of the developing fire", "Connectivity and notification channels must deliver alerts promptly", "People and firefighting resources must be available to act on an alert" ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated mapped fields after tracing the original claim. Located the original dated wildfire claim, corrected chronology to August 28, 2025, and retained the developer’s original account and later deployment details. Added contemporaneous fire-response context without treating that coverage as independent verification of the AI benefit.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0329-source-0001", "AIBD-0329-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Monitoring cameras supplied images to the smoke and fire detector. Civil-protection staff received alerts and could investigate or mobilize a response.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 22, "record_mapped": 10, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0316", "case_url": "records/AIBD-0316.html", "reason": "Defense, border security, or the detection of explosive hazards.", "details": [ { "label": "Focus", "value": "Defense and borders" }, { "label": "Purpose", "value": "Built to survey contaminated land remotely and flag explosive remnants of war, including anti-tank mines under vegetation. This lets clearance teams plan safely. The pilot was that intended use." }, { "label": "AI contribution", "value": "UNDP's release attributes the detection of explosive remnants beneath vegetation to the system's artificial intelligence working on magnetometry and visual and infrared imagery. It reports only area scanned. No detection, false-alarm or confirmation figure is given. No source independent of UNDP and SESU was found. The developer is unnamed, so the AI component cannot be characterized further." } ], "filters": { "topic": "Defense and borders", "benefit": "not_assessed", "jurisdiction": [ "Ukraine" ] }, "record": { "id": "AIBD-0316", "title": "UNDP hands SESU the Ukrainian “Mines Eye” AI aerial detection system after a pilot that scanned over 360 hectares in Kharkiv and Mykolaiv oblasts", "summary": "Mines Eye (magnetometry, visual/infrared imaging, AI detection of explosive remnants) scanned over 360 ha in a five-month SESU pilot; handed over 21 June 2025. No detection or land-release counts.", "date_observed": "2025-06-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "UNDP Ukraine's press release of 21 June 2025 is datelined Mykolaiv. It says UNDP “officially handed over the Mines Eye aerial detection system to the State Emergency Service of Ukraine (SESU)”. Financial support came from the Government of Luxembourg. The base of SESU's Main Department in Mykolaiv oblast hosted it, with UNDP Acting Administrator Haoliang Xu present. The release calls the system “Developed in Ukraine” but does not name the developer. It combines magnetometry with visual and infrared aerial imaging. It states that “Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation”. It produces maps and anomaly reports for operators before manual or mechanical clearance. UNDP-trained SESU teams ran a five-month pilot. They “used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts”. SESU First Deputy Head Vitalii Myroniuk is quoted that the technology “enables us to detect threats in areas that are too dangerous for manual inspection”. The release gives no number of items detected or confirmed, no hectares released, and no accuracy figure. UNDP's 26 June 2025 release concerned a testing exhibition with mock devices. No source independent of UNDP or SESU was found.", "category": "general benefit", "unexpected": false, "purpose": "Built to survey contaminated land remotely and flag explosive remnants of war, including anti-tank mines under vegetation. This lets clearance teams plan safely. The pilot was that intended use.", "system": "Mines Eye aerial detection system (unmanned aerial platform with magnetometry, visual and infrared sensors, and AI detection of explosive remnants of war)", "model_version": null, "developer": "Ukrainian developer not named in the UNDP release ('Developed in Ukraine')", "deployer": "State Emergency Service of Ukraine (SESU), Kharkiv and Mykolaiv oblast demining teams; procured by UNDP with funding from the Government of Luxembourg", "location": "Kharkiv and Mykolaiv oblasts, Ukraine", "domain": "Humanitarian mine action", "segment": "State emergency service survey of hazardous areas", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": "No detection or land release is reported; the type names the agency's stated aim of faster survey.", "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "SESU scanned over 360 hectares with the system during the pilot. The release gives no detection counts, confirmation rates or clearance results against which to measure the benefit." }, { "group": "societal", "depth": null, "reason": "No land released to communities, casualty avoided or farmland restored is documented. The officials' statements about faster and safer demining are expectations." } ], "ai_role": { "status": "assessed", "statement": "UNDP's release attributes the detection of explosive remnants beneath vegetation to the system's artificial intelligence working on magnetometry and visual and infrared imagery. It reports only area scanned. No detection, false-alarm or confirmation figure is given. No source independent of UNDP and SESU was found. The developer is unnamed, so the AI component cannot be characterized further." }, "evidence": [ { "kind": "press release", "citation": "UNDP Ukraine (2025). UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations. Press release, Mykolaiv, 21 June 2025 (UNDP is the funder and implementer, not the developer).", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation." }, { "kind": "press release", "citation": "UNDP Ukraine (2025). Same release, pilot results paragraph.", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts" } ], "replication": { "produced_by": [ "UNDP-trained SESU teams flying the system over confirmed and suspected hazardous areas during a five-month pilot", "Donor funding (Government of Luxembourg) for procurement and training" ], "must_hold": [ "Anomaly reports are followed by manual or mechanical clearance that confirms the detections. 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SESU First Deputy Head Vitalii Myroniuk is quoted that the technology “enables us to detect threats in areas that are too dangerous for manual inspection”. The release gives no number of items detected or confirmed, no hectares released, and no accuracy figure. UNDP's 26 June 2025 release concerned a testing exhibition with mock devices. 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UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations. Press release, Mykolaiv, 21 June 2025 (UNDP is the funder and implementer, not the developer).", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation." }, { "kind": "press release", "citation": "UNDP Ukraine (2025). Same release, pilot results paragraph.", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0316-source-0001", "AIBD-0316-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "UNDP Ukraine (2025). UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations. Press release, Mykolaiv, 21 June 2025 (UNDP is the funder and implementer, not the developer).", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation." }, { "kind": "press release", "citation": "UNDP Ukraine (2025). Same release, pilot results paragraph.", "title": "UNDP equips SESU with cutting-edge Ukrainian aerial detection technology to accelerate humanitarian demining operations", "url": "https://www.undp.org/ukraine/press-releases/undp-equips-sesu-cutting-edge-ukrainian-aerial-detection-technology-accelerate-humanitarian-demining-operations", "date": "2025-06-21", "written_by": "deployer", "quote": "used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Mines Eye (magnetometry, visual/infrared imaging, AI detection of explosive remnants) scanned over 360 ha in a five-month SESU pilot; handed over 21 June 2025. No detection or land-release counts.", "account": "UNDP Ukraine's press release of 21 June 2025 is datelined Mykolaiv. It says UNDP “officially handed over the Mines Eye aerial detection system to the State Emergency Service of Ukraine (SESU)”. Financial support came from the Government of Luxembourg. The base of SESU's Main Department in Mykolaiv oblast hosted it, with UNDP Acting Administrator Haoliang Xu present. The release calls the system “Developed in Ukraine” but does not name the developer. It combines magnetometry with visual and infrared aerial imaging. It states that “Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation”. It produces maps and anomaly reports for operators before manual or mechanical clearance. UNDP-trained SESU teams ran a five-month pilot. They “used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts”. SESU First Deputy Head Vitalii Myroniuk is quoted that the technology “enables us to detect threats in areas that are too dangerous for manual inspection”. The release gives no number of items detected or confirmed, no hectares released, and no accuracy figure. UNDP's 26 June 2025 release concerned a testing exhibition with mock devices. No source independent of UNDP or SESU was found." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Mines Eye aerial detection system (unmanned aerial platform with magnetometry, visual and infrared sensors, and AI detection of explosive remnants of war)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Ukrainian developer not named in the UNDP release ('Developed in Ukraine')" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UNDP hands SESU the Ukrainian “Mines Eye” AI aerial detection system after a pilot that scanned over 360 hectares in Kharkiv and Mykolaiv oblasts", "headline": "UNDP hands Mines Eye to SESU after a pilot scanning over 360 ha" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c16272e410a7b896b68f645e21a63ace3781b89731eb0ef2ef97cf08562dc694" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "UNDP Ukraine's press release of 21 June 2025 is datelined Mykolaiv. It says UNDP “officially handed over the Mines Eye aerial detection system to the State Emergency Service of Ukraine (SESU)”. Financial support came from the Government of Luxembourg. The base of SESU's Main Department in Mykolaiv oblast hosted it, with UNDP Acting Administrator Haoliang Xu present. The release calls the system “Developed in Ukraine” but does not name the developer. It combines magnetometry with visual and infrared aerial imaging. It states that “Using artificial intelligence, it detects explosive remnants of war (ERW), including anti-tank mines, even beneath vegetation”. It produces maps and anomaly reports for operators before manual or mechanical clearance. UNDP-trained SESU teams ran a five-month pilot. They “used the system to scan over 360 hectares of confirmed and suspected hazardous areas in Kharkiv and Mykolaiv oblasts”. SESU First Deputy Head Vitalii Myroniuk is quoted that the technology “enables us to detect threats in areas that are too dangerous for manual inspection”. The release gives no number of items detected or confirmed, no hectares released, and no accuracy figure. UNDP's 26 June 2025 release concerned a testing exhibition with mock devices. No source independent of UNDP or SESU was found.", "replication": { "produced_by": [ "UNDP-trained SESU teams flying the system over confirmed and suspected hazardous areas during a five-month pilot", "Donor funding (Government of Luxembourg) for procurement and training" ], "must_hold": [ "Anomaly reports are followed by manual or mechanical clearance that confirms the detections. The release documents no such confirmation.", "Terrain and vegetation resemble the Kharkiv and Mykolaiv pilot areas" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0555", "case_url": "records/AIBD-0555.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Develop forecasts for flash-flood-prone catchments that lack suitable warning coverage." }, { "label": "AI contribution", "value": "Machine-learning models combine environmental and hydrological data to support flash-flood forecasting. At publication, the service was at alpha stage. No warning, response, or avoided harm is attributed to operational use." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "intended", "jurisdiction": [ "United Kingdom" ] }, "record": { "id": "AIBD-0555", "title": "Northumberland County Council- FloodAI, using AI to detect flash floods", "summary": "Northumberland describes an alpha-stage flood-forecasting service for six catchments. No issued warning or avoided flood impact is reported.", "date_observed": "2025-06-17", "date_basis": "source publication", "date_precision": "day", "benefit_status": "intended", "source_collection": "LGA AI case-study bank", "source_entry": "LGA-019", "account": "Northumberland's FloodAI research project is developing forecasts for six flash-flood-prone catchments. At publication, the system was at alpha stage and approaching limited piloting. 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No evaluation of the system's effect in Germany was found, and the only effect size cited is the contested Wyoming estimate." } ], "filters": { "topic": "Energy and water", "benefit": "not_assessed", "jurisdiction": [ "Germany" ] }, "record": { "id": "AIBD-0172", "title": "Germany deploys IdentiFlight cameras to slow turbines for lesser spotted eagles", "summary": "The OECD AI Incidents Monitor records Germany's deployment of IdentiFlight AI cameras to detect lesser spotted eagles near wind turbines and slow the blades (September 2022); no German outcome data.", "date_observed": "2022-09-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). It says Germany is deploying AI-powered camera systems developed by IdentiFlight to detect endangered lesser spotted eagles near wind turbines. They slow the blades to prevent fatal collisions, as wind-farm expansion raises the risk to the species. The monitor's rationale cites the 82% reduction reported from the Wyoming study rather than any German measurement. 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OekoFor portfolio page on the German validation of IdentiFlight.", "title": "IdentiFlight", "url": "http://www.oekofor.de/en/portfolio/idf_en/", "written_by": "independent" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "The OECD AI Incidents Monitor records Germany's deployment of IdentiFlight AI cameras to detect lesser spotted eagles near wind turbines and slow the blades (September 2022); no German outcome data.", "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). It says Germany is deploying AI-powered camera systems developed by IdentiFlight to detect endangered lesser spotted eagles near wind turbines. They slow the blades to prevent fatal collisions, as wind-farm expansion raises the risk to the species. The monitor's rationale cites the 82% reduction reported from the Wyoming study rather than any German measurement. Only the registry page is among the sources; the sites, operators and any fatality data in Germany are not documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "IdentiFlight automated eagle detection and turbine curtailment" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "IdentiFlight" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Germany deploys IdentiFlight cameras to slow turbines for lesser spotted eagles", "headline": "Germany deploys IdentiFlight to protect lesser spotted eagles" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "d681e6db13db4697d9388094d32b787ef2bd96acf429df05405aae7a98d10f6d" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The OECD AI Incidents entry summarizes press coverage of 20 and 21 September 2022 (The Guardian, Mother Jones, The Register and others, not among the sources). It says Germany is deploying AI-powered camera systems developed by IdentiFlight to detect endangered lesser spotted eagles near wind turbines. They slow the blades to prevent fatal collisions, as wind-farm expansion raises the risk to the species. The monitor's rationale cites the 82% reduction reported from the Wyoming study rather than any German measurement. Only the registry page is among the sources; the sites, operators and any fatality data in Germany are not documented.", "replication": { "produced_by": [ "Wind-farm expansion into lesser spotted eagle habitat creating a regulatory need for mitigation", "Availability of the IdentiFlight system already evaluated in the United States" ], "must_hold": [ "Site-specific monitoring of eagle fatalities before and after installation", "Operators accepting curtailment losses" ], "echoes": [ { "date": "2021-01-28", "setting": "Top of the World Windpower Facility, Wyoming; BACI study of IdentiFlight curtailment", "outcome": "recurred", "source": "https://doi.org/10.1111/1365-2664.13831" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 27, "reviewed": 0, "record_mapped": 27, "not_assessed": 64, "complete": false, "by_state": { "value_present": 27, "not_reported": 0, "unresolved": 0, "not_assessed": 64, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0354", "case_url": "records/AIBD-0354.html", "reason": "Detection, forecasting, or response to natural hazards.", "details": [ { "label": "Focus", "value": "Disaster warnings and response" }, { "label": "Purpose", "value": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented." }, { "label": "AI contribution", "value": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." } ], "filters": { "topic": "Disaster warnings and response", "benefit": "not_assessed", "jurisdiction": [ "Philippines" ] }, "record": { "id": "AIBD-0354", "title": "UN OCHA's CERF anticipatory action framework for Philippine typhoons adopted 510's machine-learning impact model as trigger in 2021; no activation on that trigger documented to 2025", "summary": "OCHA frameworks (2021, 2023, 2025) use 510's XGBoost house-damage model as activation trigger; near-activation Dec 2021 (Rai); the Nov 2025 activation for Uwan ran on the 185 km/h wind trigger.", "date_observed": "2021-09-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "category": "general benefit", "unexpected": false, "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented.", "system": "510 probabilistic typhoon impact model (XGBoost) predicting totally damaged houses, used as the CERF anticipatory action activation trigger", "model_version": "probabilistic version using ECMWF ensemble tracks (revamped from deterministic, 2021)", "developer": "510, an initiative of the Netherlands Red Cross, with IFRC", "deployer": "UN OCHA Philippines with the CERF AA Core Group (FAO, IOM, UNFPA, UNICEF, WFP); impact estimates generated by 510 (2021 framework) and by OCHA with its Centre for Humanitarian Data (2025 framework)", "location": "Philippines (Regions 5, 8 and 13)", "domain": "Humanitarian anticipatory action / typhoon impact forecasting", "segment": "UN-coordinated anticipatory financing (CERF)", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The model supplies impact estimates that decide whether anticipatory funds are released, and no activation attributable to it is documented.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "depth": null, "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "depth": null, "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ], "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." }, "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ], "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "date": null, "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "date": null, "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "model_version": null, "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "model_version": null, "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] }, "paired_incident": null, "headline": "OCHA uses 510's model as CERF trigger; no activation documented", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2025-11-06", "note": "The framework stayed in force and CERF anticipatory action was activated for Super Typhoon Fung-Wong (Uwan) on 6 November 2025 with nearly US$6 million, but on the observational 185 km/h wind threshold rather than on the 510 model's output.", "quote": "The CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered. Nearly US$6 million has been allocated to five United Nations agencies to implement AA and early response efforts.", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Philippines", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, seven reports from the deployer and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0354", "catalog_version": "1.0.0", "source_fingerprint": "fb2e5c80103070fb65e02114473a292ff7832a3193b97eaae26cfce2e51f4adb", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0354-source-0001", "AIBD-0354-source-0002", "AIBD-0354-source-0003", "AIBD-0354-source-0004", "AIBD-0354-source-0005", "AIBD-0354-source-0006", "AIBD-0354-source-0007", "AIBD-0354-source-0008", "AIBD-0354-source-0009" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "510, an initiative of the Netherlands Red Cross, with IFRC", "deployer": "UN OCHA Philippines with the CERF AA Core Group (FAO, IOM, UNFPA, UNICEF, WFP); impact estimates generated by 510 (2021 framework) and by OCHA with its Centre for Humanitarian Data (2025 framework)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "OCHA’s frameworks included the 510 model’s forecasts of damage to housing among the conditions for early action before a typhoon.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "OCHA's 2021, 2023 and 2025 frameworks name the IFRC/510 impact model as the activation-trigger model. The 2021 framework describes it as partially a machine learning model. This is documented by the deployer, not only the maker. The model's forecasts were monitored during the December 2021 near activation (Typhoon Rai) without meeting criteria. The 2024 readiness phase for Pepito ended with CERF AA resources reallocated to response. OCHA's flash update attributes the 6 November 2025 activation for Uwan to the observational threshold (forecast peak 195 km/h against the 185 km/h trigger). It does not state that the 510 model's estimates reached the activation matrix. The AI's contribution to any payout is therefore not established. The maker's NHESS paper is the only source describing the algorithm (XGBoost)." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the makers' peer-reviewed study, seven reports from the deployer and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": 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"review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The available accounts do not establish that the model triggered a payment. OCHA attributed the November 2025 activation for Typhoon Uwan to a wind-speed threshold, without saying that the model’s damage estimates met the activation criteria.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2025-11-06", "note": "The framework stayed in force and CERF anticipatory action was activated for Super Typhoon Fung-Wong (Uwan) on 6 November 2025 with nearly US$6 million, but on the observational 185 km/h wind threshold rather than on the 510 model's output.", "quote": "The CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered. Nearly US$6 million has been allocated to five United Nations agencies to implement AA and early response efforts.", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0354" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The model supplies impact estimates that decide whether anticipatory funds are released, and no activation attributable to it is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No activation attributable to the model's output is documented, so no individual-level outcome can be assigned to the AI." }, { "group": "organizational", "reason": "OCHA adopted the model as its trigger and computes estimates each cycle. The only documented activation (6 November 2025) was triggered by the observational wind-speed threshold." }, { "group": "societal", "reason": "Pre-landfall assistance in November 2025 (WFP reports 42,100 families) is documented, but not attributed to the AI model by any cited source." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Philippines (Regions 5, 8 and 13)", "developer_country": "Netherlands", "deployer_country": "Philippines" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Forecast the number of totally damaged houses from typhoon track, wind and rainfall forecasts so that CERF-funded early actions can be triggered before landfall. The benefit claimed (AI-triggered anticipatory payouts) is that purpose, but no activation on the model's output is documented." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2021-09-15", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://www.unocha.org/philippines", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Humanitarian anticipatory action / typhoon impact forecasting", "segment": "UN-coordinated anticipatory financing (CERF)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0354-source-0001", "AIBD-0354-source-0002", "AIBD-0354-source-0003", "AIBD-0354-source-0004", "AIBD-0354-source-0005", "AIBD-0354-source-0006", "AIBD-0354-source-0007", "AIBD-0354-source-0008", "AIBD-0354-source-0009" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022. ReliefWeb, posted 15 September 2021 (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-2021-2022", "date": "2021-09-15", "written_by": "deployer", "quote": "The Model includes a trigger mechanism based on the ECMWF forecast and IFRC/ Netherlands Red Cross 510 probabilistic typhoon impact prediction model developed for the Philippines" }, { "kind": "report", "citation": "OCHA (2021). Anticipatory Action Framework Philippines - Typhoons - 2021-2022, model checklist and model selection sections (PDF).", "title": "Anticipatory Action Framework Philippines, 2021-2022 (model selection)", "url": "https://reliefweb.int/attachments/78d0268e-b476-3a55-ab82-94e7478fc408/Anticipatory%20Action%20Framework%20-%20Philippines%20-%20Typhoons%20-%202021-2022.pdf", "date": "2021-09-15", "written_by": "deployer", "quote": "Partially: Uses a machine learning model to determine relationship between wind speed and housing damage." }, { "kind": "report", "citation": "OCHA Philippines (2022). Report on the After Action Review for CERF Anticipatory Action, 31 March 2022. ReliefWeb (PDF dated 22 April 2022).", "title": "After Action Review on the CERF Anticipatory Action for Typhoons", "url": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons", "date": "2022-03-31", "written_by": "deployer", "quote": "While the typhoon at the end did not meet the AA pilot activation criteria, the CERF AA Core Group was closely monitoring the situation" }, { "kind": "report", "citation": "OCHA (2023). Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision. unocha.org, 3 October 2023 (PDF).", "title": "Philippines: CERF AA (Anticipatory Action) Framework, 2023 Revision", "url": "https://www.unocha.org/publications/report/philippines/philippines-cerf-aa-anticipatory-action-framework-2023-revision", "date": "2023-10-03", "written_by": "deployer", "quote": "Tropical Cyclone with potential to reach category level 3 or higher" }, { "kind": "report", "citation": "OCHA (2025). Anticipatory Action Framework Philippines - Tropical Cyclones (as of 03 October 2025, endorsed 19 October 2025). ReliefWeb, posted 4 November 2025 (PDF).", "title": "Anticipatory Action Framework Philippines - Tropical Cyclones (As of 03 October 2025)", "url": "https://reliefweb.int/report/philippines/anticipatory-action-framework-philippines-tropical-cyclones-03-october-2025", "date": "2025-10-19", "written_by": "deployer", "quote": "This will be used to compare against 510 model outputs at 72 hours before landfall, in order to account for varying regional vulnerabilities." }, { "kind": "report", "citation": "OCHA (2025). Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1 (as of 07 November, 9 p.m. local time). ReliefWeb.", "title": "Philippines: Tropical Cyclones Kalmaegi/Tino and Fung-Wong/Uwan - Flash Update No. 1", "url": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time", "date": "2025-11-07", "written_by": "deployer", "quote": "CERF Anticipatory Action (AA) was activated on 06 November after the observational threshold for Tropical Cyclone Fung-won (Uwan) was triggered" }, { "kind": "press release", "citation": "Paulose, H. (2026). How anticipatory action shaped the response to the Philippines' Twin Typhoons. OCHA Financing and Outreach Division, un.org, January 2026.", "title": "How anticipatory action shaped the response to the Philippines' Twin Typhoons", "url": "https://www.un.org/en/delegate/how-anticipatory-action-shaped-response-philippines%E2%80%99-twin-typhoons", "date": "2026-01-19", "written_by": "deployer", "quote": "WFP alone reached 42,100 families through its anticipatory financing mechanism" }, { "kind": "report", "citation": "OCHA (2025). Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report (November 2024 - April 2025). unocha.org, 27 June 2025.", "title": "Philippines: Multiple Tropical Cyclones: Humanitarian Needs and Priorities - Response Overview Report", "url": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025", "date": "2025-06-27", "written_by": "deployer", "quote": "CERF AA resources were reallocated to support the emergency response following Super Typhoon Pepito (Man-yi)" }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309-329, 1 February 2024.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "in 2021 as the trigger model for the UN OCHA AA pilot" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "OCHA frameworks (2021, 2023, 2025) use 510's XGBoost house-damage model as activation trigger; near-activation Dec 2021 (Rai); the Nov 2025 activation for Uwan ran on the 185 km/h wind trigger.", "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model estimated how many houses a typhoon might destroy." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The 2021 revision replaced deterministic forecasts with probabilities derived from ECMWF ensemble tracks." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "value_present", "value": { "kind": "text", "text": "The model converted typhoon forecasts and data on exposed housing into estimates of houses likely to be destroyed." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "system", "ai_role", "replication", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "XGBoost within the 510 typhoon-impact model." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "probabilistic version using ECMWF ensemble tracks (revamped from deterministic, 2021)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "510 probabilistic typhoon impact model (XGBoost) predicting totally damaged houses, used as the CERF anticipatory action activation trigger" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "510, an initiative of the Netherlands Red Cross, with IFRC" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "UN OCHA's CERF anticipatory action framework for Philippine typhoons adopted 510's machine-learning impact model as trigger in 2021; no activation on that trigger documented to 2025", "headline": "OCHA uses 510's model as CERF trigger; no activation documented" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "fb2e5c80103070fb65e02114473a292ff7832a3193b97eaae26cfce2e51f4adb" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "OCHA's Anticipatory Action (AA) Framework for Philippine typhoons was posted on ReliefWeb on 15 September 2021. It set up a US$7.5 million CERF pilot for about 270,500 people in Regions 5 and 8 with FAO, IOM, UNFPA, UNICEF and WFP. It has a two-stage trigger. The readiness trigger is PAGASA and 510 forecasts of a category 4 or stronger cyclone. The activation trigger, within 72 hours of landfall, is the IFRC/Netherlands Red Cross 510 probabilistic typhoon impact prediction model's predicted number of totally damaged houses. The framework says that model partially uses a machine learning model relating wind speed to housing damage. 510's 2024 NHESS paper (vendor-authored) states the XGBoost model served in 2021 as the trigger model for the UN OCHA AA pilot. OCHA's After Action Review of 31 March 2022 records that Typhoon Rai (Odette) in December 2021 rapidly intensified and did not meet the activation criteria. There was a near activation with staff pre-deployed. The 2023 revision retained the 510 model. The framework endorsed on 19 October 2025 covers US$6 million CERF and about 955,507 people in Regions 5, 8 and 13. OCHA and its Centre for Humanitarian Data compute predicted totally damaged buildings with the 510 model every 6–12 hours. It adds an observational trigger for super typhoons of 185 km/h or more reported by PAGASA. OCHA's June 2025 report says the readiness trigger was met for Super Typhoon Pepito (Man-yi) in its November 2024 to April 2025 reporting period. Activation thresholds were not reached and CERF AA resources were reallocated to the response. On 6 November 2025 CERF AA was activated for Super Typhoon Fung-Wong (Uwan) after the observational wind-speed threshold was triggered. OCHA reports nearly US$6 million released and WFP alone reaching 42,100 families. No cited document records an activation produced by the 510 model's output.", "replication": { "produced_by": [ "A pre-arranged CERF allocation with agency action plans ready before the typhoon season", "ECMWF ensemble track, wind and rainfall forecasts fed to the 510 impact model every 6–12 hours", "Historical house-damage data linking hazard, vulnerability and exposure to train the model", "A threshold matrix of predicted totally damaged houses per region with a 1-in-3.5-year return period (2025)" ], "must_hold": [ "Forecast lead time of at least 72 hours; rapid intensification (Rai 2021) shortened lead time below the trigger window", "Model impact estimates reaching the regional threshold matrix, which no read activation record documents", "Agency delivery capacity (financial service providers, data-sharing agreements) in the 72 hours before landfall", "PAGASA and ECMWF data availability" ], "echoes": [ { "setting": "Typhoon Rai (Odette), December 2021: forecasts monitored, activation criteria not met, staff pre-deployed (near activation)", "model_version": "2021 probabilistic version", "outcome": "did not recur", "source": "https://reliefweb.int/report/philippines/after-action-review-cerf-anticipatory-action-typhoons" }, { "setting": "Super Typhoon Pepito (Man-yi), within OCHA's November 2024 to April 2025 reporting period: readiness trigger met, activation thresholds not reached, CERF AA resources reallocated to emergency response", "outcome": "did not recur", "source": "https://www.unocha.org/publications/report/philippines/philippines-multiple-tropical-cyclones-humanitarian-needs-and-priorities-response-overview-report-november-2024-april-2025" }, { "date": "2025-11-06", "setting": "Super Typhoon Fung-Wong (Uwan): CERF AA activated on the observational 185 km/h threshold; nearly US$6 million released; WFP reports 42,100 families reached; AI model's role not stated", "outcome": "unknown", "source": "https://reliefweb.int/report/philippines/philippines-tropical-cyclones-kalmaegitino-and-fung-wonguwan-flash-update-no1-07-november-9-pm-local-time" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 0, "record_mapped": 33, "not_assessed": 58, "complete": false, "by_state": { "value_present": 33, "not_reported": 0, "unresolved": 0, "not_assessed": 58, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0347", "case_url": "records/AIBD-0347.html", "reason": "Detection of threats, investigation of offenses, or protection of people and wildlife.", "details": [ { "label": "Focus", "value": "Public and environmental security" }, { "label": "Purpose", "value": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output." }, { "label": "AI contribution", "value": "No cited source documents an AI or machine-learning role. INTERPOL's release says it tracked the vessel with support from OceanMind and that hourly updates were aided by contributions from Global Fishing Watch. GFW's own article credits named analysts who tracked AIS data, predicted the course and identified rendezvous points. It does not mention algorithms, machine learning or automated detection. OceanMind's homepage states only that it uses satellites, AI and human expertise in general. The AI role is thus undocumented rather than established. The account comes from INTERPOL, GFW, and the Blue Belt blog. The court outcome (Supreme Court verdict of 27 February 2020, conviction, Rp300,000,000 fine, forfeiture) comes from the Blue Belt blog. The Blue Belt post of 5 June 2021 is no longer available, and no archived copy is cited." } ], "filters": { "topic": "Public and environmental security", "benefit": "not_assessed", "jurisdiction": [ "Indonesia" ] }, "record": { "id": "AIBD-0347", "title": "Indonesia captures the blacklisted MV NIKA on 12 July 2019 after INTERPOL tracking supported by OceanMind and Global Fishing Watch; the tracking was by analysts, not an AI detection", "summary": "MV NIKA seized by Indonesia 12 July 2019 after INTERPOL tracking with OceanMind and GFW; captain convicted, owner fined, vessel forfeited; no cited source documents an AI detection.", "date_observed": "2019-07-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "category": "protective response", "unexpected": false, "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output.", "system": "Global Fishing Watch vessel-tracking platform and analysts; OceanMind analysis for INTERPOL (no AI component documented for this case)", "model_version": null, "developer": "Global Fishing Watch; OceanMind", "deployer": "INTERPOL (Global Fisheries Enforcement program); Indonesian naval and fisheries authorities (patrol boats ORCA 2 and ORCA 3)", "location": "Indonesian exclusive economic zone, Indonesia", "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "International police cooperation and national maritime enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": "The crime is illegal fishing, the AI role is undocumented, and the good is a seizure and conviction rather than less violence.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "depth": null, "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "depth": null, "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ], "ai_role": { "status": "unassessed", "statement": "No cited source documents an AI or machine-learning role. INTERPOL's release says it tracked the vessel with support from OceanMind and that hourly updates were aided by contributions from Global Fishing Watch. GFW's own article credits named analysts who tracked AIS data, predicted the course and identified rendezvous points. It does not mention algorithms, machine learning or automated detection. OceanMind's homepage states only that it uses satellites, AI and human expertise in general. The AI role is thus undocumented rather than established. The account comes from INTERPOL, GFW, and the Blue Belt blog. The court outcome (Supreme Court verdict of 27 February 2020, conviction, Rp300,000,000 fine, forfeiture) comes from the Blue Belt blog. The Blue Belt post of 5 June 2021 is no longer available, and no archived copy is cited." }, "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "date": null, "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ], "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "date": null, "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "model_version": null, "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] }, "paired_incident": null, "headline": "GFW analysts help Indonesia seize MV NIKA; no AI detection", "metric": "the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited", "comparator": null, "later": { "status": "unknown", "date": null, "note": "The reported verdict of 27 February 2020, conviction, fine, and forfeiture rest on a UK Government Blue Belt blog that is no longer available. They lack independent confirmation in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "France", "unrated_because": "the seizure is documented, the AI's part is not", "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: two press releases from the makers and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0347", "catalog_version": "1.0.0", "source_fingerprint": "c8d0aa4fd21ddc071c29e698a7b48458113030802ec4971dabf6dec4f5108888", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0347-source-0001", "AIBD-0347-source-0002", "AIBD-0347-source-0003", "AIBD-0347-source-0004", "AIBD-0347-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Global Fishing Watch; OceanMind", "deployer": "INTERPOL (Global Fisheries Enforcement program); Indonesian naval and fisheries authorities (patrol boats ORCA 2 and ORCA 3)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "display": { "answer": "INTERPOL received vessel-tracking assistance from OceanMind and Global Fishing Watch. The cited accounts describe analysts’ work but do not identify an AI component used in this operation.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "No cited source documents an AI or machine-learning role. INTERPOL's release says it tracked the vessel with support from OceanMind and that hourly updates were aided by contributions from Global Fishing Watch. GFW's own article credits named analysts who tracked AIS data, predicted the course and identified rendezvous points. It does not mention algorithms, machine learning or automated detection. OceanMind's homepage states only that it uses satellites, AI and human expertise in general. The AI role is thus undocumented rather than established. The account comes from INTERPOL, GFW, and the Blue Belt blog. The court outcome (Supreme Court verdict of 27 February 2020, conviction, Rp300,000,000 fine, forfeiture) comes from the Blue Belt blog. The Blue Belt post of 5 June 2021 is no longer available, and no archived copy is cited." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: two press releases from the makers and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "OceanMind’s general description of its use of AI does not establish that it used AI here. The Blue Belt account of the court outcome is no longer available, and the entry has no archived copy.", "editor_note": "" } }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The reported verdict of 27 February 2020, conviction, fine, and forfeiture rest on a UK Government Blue Belt blog that is no longer available. They lack independent confirmation in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Analysts tracked the vessel’s signals, predicted its course, and identified possible rendezvous points." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0347" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Violence", "taxonomy_note": "The crime is illegal fishing, the AI role is undocumented, and the good is a seizure and conviction rather than less violence." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual beneficiary is identified in any cited source." }, { "group": "organizational", "reason": "Indonesian authorities and INTERPOL obtained a capture, a conviction and a forfeiture. No cited source attributes any part of the result to an AI output." }, { "group": "societal", "reason": "One blacklisted vessel removed from Southern Ocean fishing. The contribution of any AI system is undocumented and the deterrent effect is not quantified." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Indonesian exclusive economic zone, Indonesia", "developer_country": "United States", "deployer_country": "France" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Vessel tracking and analysis to support fisheries enforcement against illegal, unreported and unregulated fishing. The capture was the intended use, but the sources attribute it to analysts and inter-agency coordination rather than to an AI output." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2019-07-12", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the seizure is documented, the AI's part is not.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Violence under Trustible's full taxonomy of 23 types; it was Improved Environmental Sustainability among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Global_Fishing_Watch; https://www.interpol.int/en/Who-we-are/What-is-INTERPOL", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to INTERPOL.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "International police cooperation and national maritime enforcement" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0347-source-0001", "AIBD-0347-source-0002", "AIBD-0347-source-0003", "AIBD-0347-source-0004", "AIBD-0347-source-0005" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "INTERPOL (2019). INTERPOL supports apprehension of vessel suspected of illegal fishing. News release, 22 July 2019.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "INTERPOL tracked the movement of the MV NIKA with support from OceanMind, sending daily reports to the relevant countries including Indonesia, New Zealand, Panama, the UK and the US." }, { "kind": "press release", "citation": "INTERPOL (2019). Same release, paragraph on the approach to Indonesian waters.", "title": "INTERPOL supports apprehension of vessel suspected of illegal fishing (Global Fishing Watch paragraph)", "url": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing", "date": "2019-07-22", "written_by": "deployer", "quote": "As it approached Indonesian waters, additional updates were sent to the national authorities hourly, aided by contributions from Global Fishing Watch." }, { "kind": "press release", "citation": "Global Fishing Watch (2019). The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it. globalfishingwatch.org, 23 July 2019.", "title": "The capture of the MV NIKA: a case of illicit fishing and a showcase for how to beat it", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "GFW analysts helped predict the MV NIKA's course and identify rendezvous points where the patrol vessels could intercept" }, { "kind": "press release", "citation": "Global Fishing Watch (2019). Same article, paragraph on the live coordination.", "title": "The capture of the MV NIKA (coordination paragraph)", "url": "https://globalfishingwatch.org/article/capture-mv-nika-case/", "date": "2019-07-23", "written_by": "makers", "quote": "a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour, swapped information on its every move" }, { "kind": "press release", "citation": "OceanMind (n.d.). Homepage, general description of methods (no mention of NIKA).", "title": "OceanMind homepage", "url": "https://oceanmind.global/", "written_by": "makers", "quote": "For more than a decade, we have used satellites, AI, and human expertise to provide crucial insights that enable others to protect the ocean for people and planet." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MV NIKA seized by Indonesia 12 July 2019 after INTERPOL tracking with OceanMind and GFW; captain convicted, owner fined, vessel forfeited; no cited source documents an AI detection.", "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Global Fishing Watch vessel-tracking platform and analysts; OceanMind analysis for INTERPOL (no AI component documented for this case)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Global Fishing Watch; OceanMind" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Indonesia captures the blacklisted MV NIKA on 12 July 2019 after INTERPOL tracking supported by OceanMind and Global Fishing Watch; the tracking was by analysts, not an AI detection", "headline": "GFW analysts help Indonesia seize MV NIKA; no AI detection" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "c8d0aa4fd21ddc071c29e698a7b48458113030802ec4971dabf6dec4f5108888" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "INTERPOL reported on 22 July 2019 that the MV NIKA had been detained by Indonesian authorities. The vessel was suspected of illegal fishing in the Southern Ocean. Its owner also owned the blacklisted STS 50 intercepted in Indonesia in 2018. INTERPOL tracked the vessel's movement with support from the UK not-for-profit OceanMind, sending daily reports to Indonesia, New Zealand, Panama, the UK and the US. Hourly updates aided by Global Fishing Watch (GFW) followed as it approached Indonesian waters. GFW's account (23 July 2019) says GFW analyst Imam Prakoso helped track the vessel from February 2019 after it illegally switched off its tracking system. It says a live multi-way communication between Indonesian naval and fishery officials, INTERPOL and GFW analyst Charles Kilgour exchanged information. GFW analysts helped predict its course and identify rendezvous points. Indonesian patrol boats ORCA 2 and ORCA 3 intercepted it on 12 July 2019 at 08:06 local time. The court outcome comes from a UK Government Blue Belt blog of 5 June 2021. That blog was no longer available as of 20 October 2026. The Indonesian Supreme Court gave its verdict on 27 February 2020, the captain was convicted, the owner fined Rp300,000,000 and the vessel forfeited. None of the sources describes a machine-learning or AI detection. OceanMind's homepage says only, in general terms, that it uses satellites, AI and human expertise.", "replication": { "produced_by": [ "A blacklisted vessel whose identity and ownership were already known to INTERPOL and CCAMLR", "Continuous tracking by OceanMind and GFW analysts with daily, then hourly, reports to coastal states", "Indonesian patrol vessels positioned at predicted rendezvous points", "Indonesian prosecution leading to a Supreme Court verdict" ], "must_hold": [ "A coastal state willing and able to intercept and prosecute", "Analyst capacity to follow a vessel that has switched off its tracking system", "Information sharing between INTERPOL, NGOs and national agencies in near real time", "Documentation of what, if anything, an automated system contributed, which is absent here" ], "echoes": [ { "setting": "STS 50, a blacklisted vessel of the same owner, intercepted in Indonesia in 2018 (INTERPOL release); AI role equally undocumented", "outcome": "unknown", "source": "https://www.interpol.int/en/News-and-Events/News/2019/INTERPOL-supports-apprehension-of-vessel-suspected-of-illegal-fishing" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0186", "case_url": "records/AIBD-0186.html", "reason": "Detection or prevention of fraud and illicit financial activity.", "details": [ { "label": "Focus", "value": "Financial security" }, { "label": "Purpose", "value": "Identify or prevent inappropriate Medicare payments by generating leads that program-integrity contractors investigate and act on." }, { "label": "AI contribution", "value": "The report counts savings from actions taken due to the FPS and from investigations the FPS expedited, augmented or corroborated. So the system's own contribution cannot be separated from investigators' work. The technique is not described. CMS deployed the system and paid unnamed developing and modeling contractors, so developer and deployer are not the same body." } ], "filters": { "topic": "Financial security", "benefit": "not_assessed", "jurisdiction": [ "Unstated" ] }, "record": { "id": "AIBD-0186", "title": "CMS's Fraud Prevention System identifies or prevents $654.8 million in 2015, an $11.5-to-$1 return on investment", "summary": "CMS's return-on-investment report for the Fraud Prevention System's fourth implementation year counts $654.8 million in inappropriate Medicare payments identified or prevented in 2015 against $57.1...", "date_observed": "2015-12-31", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Centers for Medicare and Medicaid Services' report “Fraud Prevention System Return on Investment, Fourth Implementation Year” is undated. The Wayback Machine archived it on 24 August 2016. It states the FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015, about 44% more than the previous year. That counts actions taken due to the FPS and investigations it expedited, augmented or corroborated. The corrected ROI is $11.5 to $1, after a rounding error in the originally reported $11.6. Table 1 trends identified savings from $115.4 million in the first year (July 2011 to June 2012) through $250.1 million and $454.0 million. The cumulative total is $1,474.3 million. Savings are split across pre-payment edit denials, payment suspensions, projected cost avoidance from revoking billing privileges and overpayments referred for recovery. The value of law-enforcement referrals was $49.2 million in 2015. The report says the methodology was certified by the HHS Office of Inspector General. It puts 45 providers on active payment suspension at year end. Costs were an estimated $57.1 million, including $19.5 million for a developing contractor and $4.8 million for a modeling contractor. The report does not describe the system's technique.", "category": "general benefit", "unexpected": false, "purpose": "Identify or prevent inappropriate Medicare payments by generating leads that program-integrity contractors investigate and act on.", "system": "Fraud Prevention System (FPS)", "model_version": null, "developer": "Contractors engaged by CMS (the report lists 'Developing Contractor Costs' and 'Modeling Contractor Costs' without naming firms)", "deployer": "Centers for Medicare and Medicaid Services (CMS)", "location": null, "domain": "Health insurance fraud and improper payments", "segment": "Medicare program integrity", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "The report gives aggregate identified savings for CMS with no single attributable case." }, { "group": "societal", "depth": null, "reason": "Savings to the Medicare program are stated only as yearly aggregates mixing actual denials with projected avoidance." } ], "ai_role": { "status": "unassessed", "statement": "The report counts savings from actions taken due to the FPS and from investigations the FPS expedited, augmented or corroborated. So the system's own contribution cannot be separated from investigators' work. The technique is not described. CMS deployed the system and paid unnamed developing and modeling contractors, so developer and deployer are not the same body." }, "evidence": [ { "kind": "report", "citation": "Centers for Medicare and Medicaid Services, 'Fraud Prevention System Return on Investment, Fourth Implementation Year', undated (covers 1 January to 31 December 2015; archived 24 August 2016)", "title": "Fraud Prevention System Return on Investment, Fourth Implementation Year", "url": "https://web.archive.org/web/20160824134124/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "date": null, "written_by": "deployer", "quote": "The FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015" } ], "replication": { "produced_by": [ "Claims data run through FPS edits and models that generate leads for Zone Program Integrity Contractors", "Administrative levers listed in Table 1 (pre-payment edits, payment suspensions, revocation of billing privileges, overpayment referrals, law-enforcement referrals)" ], "must_hold": [ "Statutory basis and funding (the Small Business Jobs Act of 2010 defines the implementation years)", "Contractor capacity to investigate leads (investigation costs of $28.9 million in 2015)", "A savings methodology certified by HHS-OIG" ], "echoes": [ { "date": "2012-06-30", "setting": "First implementation year, July 2011 to June 2012", "model_version": null, "outcome": "recurred", "source": "Table 1: $115.4 million identified savings" }, { "date": "2013-09-30", "setting": "Second implementation year, October 2012 to September 2013", "model_version": null, "outcome": "recurred", "source": "Table 1 and note 4: $210.7 million certified plus $39.4 million attributed, $250.1 million" }, { "date": "2014-12-31", "setting": "Third implementation year, calendar 2014", "model_version": null, "outcome": "recurred", "source": "Table 1: $454.0 million" } ] }, "paired_incident": null, "headline": "CMS's FPS identifies or prevents $654.8 million in 2015", "metric": "$654.8 million in inappropriate Medicare payments identified or prevented in 2015", "comparator": { "against": "the FPS's identified savings in the previous year, 2014", "before": "identified savings from the previous year ($454.0 million in Table 1)", "after": "$654.8 million", "basis": "measured", "quote": "The FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015 through actions taken due to the FPS or through investigations expedited, augmented, or corroborated by the FPS (Table 1). These identified savings were about 44% higher than the identified savings from the previous year, with a nearly $11.51 to $1 ROI.", "url": "https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf" }, "later": null, "value": { "amount": "$654.8 million", "what": "inappropriate Medicare payments identified or prevented in calendar year 2015", "who": "Medicare program (CMS)", "estimate": false, "quote": "The FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015 through actions taken due to the FPS or through investigations expedited, augmented, or corroborated by the FPS (Table 1).", "url": "https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf" }, "developer_country": null, "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.cms.gov/about-cms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf; https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's report. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0186", "catalog_version": "1.0.0", "source_fingerprint": "dbb137d510c046e493190446357ab264f74b76d6a694ce594514b34e0f64547e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0186-source-0001" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "text", "text": "An existing source-stated monetary observation is present in the original value object." }, "provenance": { "kind": "legacy_record", "basis": "Existing value object only; its financial interpretation has not been reverified.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0186-legacy-value" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Contractors engaged by CMS (the report lists 'Developing Contractor Costs' and 'Modeling Contractor Costs' without naming firms)", "deployer": "Centers for Medicare and Medicaid Services (CMS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "unassessed", "statement": "The report counts savings from actions taken due to the FPS and from investigations the FPS expedited, augmented or corroborated. So the system's own contribution cannot be separated from investigators' work. The technique is not described. CMS deployed the system and paid unnamed developing and modeling contractors, so developer and deployer are not the same body." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the deployer's report. 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The Wayback Machine archived it on 24 August 2016. It states the FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015, about 44% more than the previous year. That counts actions taken due to the FPS and investigations it expedited, augmented or corroborated. The corrected ROI is $11.5 to $1, after a rounding error in the originally reported $11.6. Table 1 trends identified savings from $115.4 million in the first year (July 2011 to June 2012) through $250.1 million and $454.0 million. The cumulative total is $1,474.3 million. Savings are split across pre-payment edit denials, payment suspensions, projected cost avoidance from revoking billing privileges and overpayments referred for recovery. The value of law-enforcement referrals was $49.2 million in 2015. The report says the methodology was certified by the HHS Office of Inspector General. It puts 45 providers on active payment suspension at year end. Costs were an estimated $57.1 million, including $19.5 million for a developing contractor and $4.8 million for a modeling contractor. The report does not describe the system's technique.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "reason": "The report gives aggregate identified savings for CMS with no single attributable case." }, { "group": "societal", "reason": "Savings to the Medicare program are stated only as yearly aggregates mixing actual denials with projected avoidance." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Identify or prevent inappropriate Medicare payments by generating leads that program-integrity contractors investigate and act on." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2015-12-31", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.cms.gov/about-cms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf; https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Claims data run through FPS edits and models that generate leads for Zone Program Integrity Contractors", "Administrative levers listed in Table 1 (pre-payment edits, payment suspensions, revocation of billing privileges, overpayment referrals, law-enforcement referrals)" ], "must_hold": [ "Statutory basis and funding (the Small Business Jobs Act of 2010 defines the implementation years)", "Contractor capacity to investigate leads (investigation costs of $28.9 million in 2015)", "A savings methodology certified by HHS-OIG" ], "echoes": [ { "date": "2012-06-30", "setting": "First implementation year, July 2011 to June 2012", "outcome": "recurred", "source": "Table 1: $115.4 million identified savings" }, { "date": "2013-09-30", "setting": "Second implementation year, October 2012 to September 2013", "outcome": "recurred", "source": "Table 1 and note 4: $210.7 million certified plus $39.4 million attributed, $250.1 million" }, { "date": "2014-12-31", "setting": "Third implementation year, calendar 2014", "outcome": "recurred", "source": "Table 1: $454.0 million" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Claims data run through FPS edits and models that generate leads for Zone Program Integrity Contractors", "Administrative levers listed in Table 1 (pre-payment edits, payment suspensions, revocation of billing privileges, overpayment referrals, law-enforcement referrals)" ], "must_hold": [ "Statutory basis and funding (the Small Business Jobs Act of 2010 defines the implementation years)", "Contractor capacity to investigate leads (investigation costs of $28.9 million in 2015)", "A savings methodology certified by HHS-OIG" ], "echoes": [ { "date": "2012-06-30", "setting": "First implementation year, July 2011 to June 2012", "outcome": "recurred", "source": "Table 1: $115.4 million identified savings" }, { "date": "2013-09-30", "setting": "Second implementation year, October 2012 to September 2013", "outcome": "recurred", "source": "Table 1 and note 4: $210.7 million certified plus $39.4 million attributed, $250.1 million" }, { "date": "2014-12-31", "setting": "Third implementation year, calendar 2014", "outcome": "recurred", "source": "Table 1: $454.0 million" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.cms.gov/about-cms", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf; https://web.archive.org/web/20160824134124id_/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Health insurance fraud and improper payments", "segment": "Medicare program integrity" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Centers for Medicare and Medicaid Services, 'Fraud Prevention System Return on Investment, Fourth Implementation Year', undated (covers 1 January to 31 December 2015; archived 24 August 2016)", "title": "Fraud Prevention System Return on Investment, Fourth Implementation Year", "url": "https://web.archive.org/web/20160824134124/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "written_by": "deployer", "quote": "The FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0186-source-0001" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Centers for Medicare and Medicaid Services, 'Fraud Prevention System Return on Investment, Fourth Implementation Year', undated (covers 1 January to 31 December 2015; archived 24 August 2016)", "title": "Fraud Prevention System Return on Investment, Fourth Implementation Year", "url": "https://web.archive.org/web/20160824134124/https://www.cms.gov/about-cms/components/cpi/downloads/fraud-prevention-system-return-on-investment-fourth-implementation-year-2015.pdf", "written_by": "deployer", "quote": "The FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "CMS's return-on-investment report for the Fraud Prevention System's fourth implementation year counts $654.8 million in inappropriate Medicare payments identified or prevented in 2015 against $57.1...", "account": "The Centers for Medicare and Medicaid Services' report “Fraud Prevention System Return on Investment, Fourth Implementation Year” is undated. The Wayback Machine archived it on 24 August 2016. It states the FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015, about 44% more than the previous year. That counts actions taken due to the FPS and investigations it expedited, augmented or corroborated. The corrected ROI is $11.5 to $1, after a rounding error in the originally reported $11.6. Table 1 trends identified savings from $115.4 million in the first year (July 2011 to June 2012) through $250.1 million and $454.0 million. The cumulative total is $1,474.3 million. Savings are split across pre-payment edit denials, payment suspensions, projected cost avoidance from revoking billing privileges and overpayments referred for recovery. The value of law-enforcement referrals was $49.2 million in 2015. The report says the methodology was certified by the HHS Office of Inspector General. It puts 45 providers on active payment suspension at year end. Costs were an estimated $57.1 million, including $19.5 million for a developing contractor and $4.8 million for a modeling contractor. The report does not describe the system's technique." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Fraud Prevention System (FPS)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Contractors engaged by CMS (the report lists 'Developing Contractor Costs' and 'Modeling Contractor Costs' without naming firms)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "CMS's Fraud Prevention System identifies or prevents $654.8 million in 2015, an $11.5-to-$1 return on investment", "headline": "CMS's FPS identifies or prevents $654.8 million in 2015" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "dbb137d510c046e493190446357ab264f74b76d6a694ce594514b34e0f64547e" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Centers for Medicare and Medicaid Services' report “Fraud Prevention System Return on Investment, Fourth Implementation Year” is undated. The Wayback Machine archived it on 24 August 2016. It states the FPS helped identify or prevent $654.8 million in inappropriate payments during calendar year 2015, about 44% more than the previous year. That counts actions taken due to the FPS and investigations it expedited, augmented or corroborated. The corrected ROI is $11.5 to $1, after a rounding error in the originally reported $11.6. Table 1 trends identified savings from $115.4 million in the first year (July 2011 to June 2012) through $250.1 million and $454.0 million. The cumulative total is $1,474.3 million. Savings are split across pre-payment edit denials, payment suspensions, projected cost avoidance from revoking billing privileges and overpayments referred for recovery. The value of law-enforcement referrals was $49.2 million in 2015. The report says the methodology was certified by the HHS Office of Inspector General. It puts 45 providers on active payment suspension at year end. Costs were an estimated $57.1 million, including $19.5 million for a developing contractor and $4.8 million for a modeling contractor. The report does not describe the system's technique.", "replication": { "produced_by": [ "Claims data run through FPS edits and models that generate leads for Zone Program Integrity Contractors", "Administrative levers listed in Table 1 (pre-payment edits, payment suspensions, revocation of billing privileges, overpayment referrals, law-enforcement referrals)" ], "must_hold": [ "Statutory basis and funding (the Small Business Jobs Act of 2010 defines the implementation years)", "Contractor capacity to investigate leads (investigation costs of $28.9 million in 2015)", "A savings methodology certified by HHS-OIG" ], "echoes": [ { "date": "2012-06-30", "setting": "First implementation year, July 2011 to June 2012", "outcome": "recurred", "source": "Table 1: $115.4 million identified savings" }, { "date": "2013-09-30", "setting": "Second implementation year, October 2012 to September 2013", "outcome": "recurred", "source": "Table 1 and note 4: $210.7 million certified plus $39.4 million attributed, $250.1 million" }, { "date": "2014-12-31", "setting": "Third implementation year, calendar 2014", "outcome": "recurred", "source": "Table 1: $454.0 million" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } } ] }