{ "schema_version": "1.0", "slug": "adyt", "name": "Adyt", "subtitle": "Safeguards", "description": "Reported harm prevention and protective responses involving AI.", "note": "Specific harm prevented and protective response are separate categories. In a protective response, prevention remains unproven.", "filters": [ { "key": "category", "label": "Outcome", "options": [ { "value": "Protective response, prevention unproven", "label": "Protective response, prevention unproven", "count": 70 }, { "value": "Specific harm prevented", "label": "Specific harm prevented", "count": 34 } ] }, { "key": "comparison", "label": "Comparison", "options": [ { "value": "assumed", "label": "Assumed comparison", "count": 26 }, { "value": "measured", "label": "Measured comparison", "count": 19 }, { "value": "undocumented", "label": "No documented comparison", "count": 59 } ] } ], "count": 104, "cases": [ { "case_id": "AIBD-0023", "case_url": "records/AIBD-0023.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The paper reports that the watch recognized the fall and automatically called 112 and alerted emergency contacts. Those alerts brought responders to the man behind the hedge. Humans provided resuscitation, defibrillation, and surgery. The watch's contribution is documented in the clinical narrative, but the time to rescue or outcome without it cannot be measured from this case. Apple's documentation describes the detection and notification sequence without disclosing the classifier or model version." }, { "label": "Comparison", "value": "being found without the automatic alert, on a remote spot behind a hedge", "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven" }, { "label": "Before", "value": "Zonder die melding had ik hem niet zo snel gevonden (without that alert I would not have found him so quickly)" }, { "label": "After", "value": "CPR approximately 6–7 min after collapse; full neurological recovery" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0023", "title": "Apple Watch Fall Detection summoning help after out-of-hospital cardiac arrest", "summary": "Fall Detection on his Apple Watch Series 10 called emergency services, and he recovered fully after CPR and hospital treatment.", "date_observed": "2026-01-01", "date_basis": "event", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A case report published on 12 August 2026 describes a 69-year-old man who collapsed from ventricular fibrillation while running alone. He was on a dead-end road, hidden behind a hedge. The paper gives no date or place. The authors' hospitals are Radboud University Medical Center, Nijmegen, and Slingeland Hospital, Doetinchem. His Apple Watch (reported as Apple Watch 10) with Fall Detection enabled detected the fall. It automatically alerted his predefined emergency contacts and the emergency medical services by calling 112. Family members reached him, found him unresponsive and began cardiopulmonary resuscitation (CPR) about 6–7 minutes after the collapse. An ambulance arrived about 1.5 minutes later, defibrillated him and restored circulation. He underwent coronary artery bypass grafting and defibrillator implantation, was discharged on day 29 and made a full neurological recovery. Two interviews identify the patient as Alwi Siebers, 69, of Varsseveld, and date the collapse to 1 January 2026. Men's Health NL (28 April 2026) opens “1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland” (1 January 2026 begins for 69-year-old Alwi Siebers as an ordinary running day; the keen runner from Varsseveld is training for the Montferland half marathon). His son Tristan says “De locatie was heel precies. Hij lag achter een heg op een afgelegen plek. Zonder die melding had ik hem niet zo snel gevonden” (the location was very precise; he was lying behind a hedge in a remote spot; without that alert I would not have found him so quickly). WANT (1 May 2026) quotes Siebers, “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon” (I know from my activity log that I started my training loop at twelve past twelve). It reproduces the alert his son received, headed “do 1 jan, 12:12”. The interviews match the paper on the man's age and the Apple Watch Series 10 with fall detection. They also match on the automatic alert to 112 and to his emergency contacts, the hedge and the remote spot. They match on the son starting CPR and the daughter giving mouth-to-mouth, and a history of asthma and bronchiectasis. They match on six days in intensive care, four bypass grafts, an ICD and a full recovery. No source names both the paper and Siebers, so the identification is inferred from these details. Men's Health says that Siebers also pressed the SOS button himself and that a pulse returned within about three minutes. The paper's timeline does not support that: CPR after 6–7 minutes, four shocks, adrenaline and amiodarone before circulation returned. The street is probable but inferred. A fire-brigade pager message of 1 January 2026 at 12:18:59 records a resuscitation on the Oude Doetinchemseweg in Varsseveld. That is a dead-end road at the western edge of the village. The map in the alert screenshot shows the pin on a side road off the Doetinchemseweg. No source names the street in connection with Siebers.", "category": "harm prevented", "unexpected": false, "purpose": "Fall Detection is built to recognize a hard fall and, if the wearer stays immobile, to call emergency services and notify contacts with the location. That is what it did.", "system": "Apple Watch Fall Detection", "model_version": null, "developer": "Apple", "deployer": null, "location": "Varsseveld, Gelderland, Netherlands", "domain": "Emergency medicine / resuscitation", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 5, "reason": "The patient was alone and hidden from view in cardiac arrest. The automatic alerts brought CPR within 6–7 minutes. He left hospital neurologically intact, which the source presents as life-saving." } ], "ai_role": { "status": "assessed", "statement": "The paper reports that the watch recognized the fall and automatically called 112 and alerted emergency contacts. Those alerts brought responders to the man behind the hedge. Humans provided resuscitation, defibrillation, and surgery. The watch's contribution is documented in the clinical narrative, but the time to rescue or outcome without it cannot be measured from this case. Apple's documentation describes the detection and notification sequence without disclosing the classifier or model version." }, "evidence": [ { "kind": "case report", "citation": "Jansen CE, Jaspers Focks J, Manglani Rodriguez S, Pol L, Edgar R, van Royen N, Bonnes JL (2026). Wearable saving lives: a case report. Resuscitation Plus 31:101447.", "title": "Wearable saving lives: a case report", "url": "https://europepmc.org/article/PMC/PMC13543876", "date": "2026-08-12", "written_by": "independent", "quote": "The Apple Watch detected the fall and automatically alerted predefined emergency contacts as well as the emergency medical services by calling 112." }, { "kind": "news report", "citation": "Boerman, E. (2026). ‘Ik was voor drie minuten dood, maar mijn Apple Watch redde mijn leven’. Men's Health NL, April 28, 2026.", "title": "Hartstilstand tijdens hardlopen: Apple Watch redt leven", "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "date": "2026-04-28", "written_by": "independent", "quote": "1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland en trekt eropuit zoals hij dat al jaren doet. Toch gaat het mis. Alwi krijgt tijdens het lopen een hartstilstand." }, { "kind": "news report", "citation": "Kraak J (2026). Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert. WANT, 1 May 2026 (with the alert screenshot headed 'do 1 jan, 12:12').", "title": "Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert", "url": "https://www.want.nl/apple-watch-leven-gered-alwi-siebers/", "date": "2026-05-01", "written_by": "independent", "quote": "Het ging 1 januari van dit jaar echter mis. “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon.” Niet veel later kreeg zoon Tristan een bericht op zijn smartphone. Dat bericht was niet gestuurd door Alwi zelf, maar door zijn Apple Watch Series 10. Het was een melding dat zijn vader gevallen was." }, { "kind": "other", "citation": "112-meldingen.nl (2026). P2000 pager message, Varsseveld, 1 January 2026, 12:18:59, fire brigade Lichtenvoorde (resuscitation, privacy screen), with ambulance capcodes at 12:17 and 12:18.", "title": "Incident Oude Doetinchemseweg Varsseveld", "url": "https://112-meldingen.nl/112-varsseveld/incident-oude-doetinchemseweg-varsseveld-1402058", "date": "2026-01-01", "written_by": "unknown", "quote": "0803203 12:18:59 01-01-26 P 2 BON-02 Reanimatie (zichtscherm) Oude Doetinchemseweg Varsseveld 066371" }, { "kind": "other", "citation": "Apple. Use Fall Detection with Apple Watch, May 6, 2026; “How it works,” “What happens if your Apple Watch detects that you're immobile,” and notes 1–3.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Fall Detection is enabled on a worn Apple Watch with cellular or phone connectivity", "The wearer collapses with an impact the classifier recognizes and then stays immobile", "Emergency services and contacts can reach the shared location quickly" ], "must_hold": [ "The watch has connectivity at the scene", "The collapse produces a detectable fall signature", "Responders arrive within the window in which resuscitation can succeed" ], "echoes": [ { "date": "2024-05-13", "setting": "elderly man, found by his wife lying unresponsive on the floor; place and setting not stated in the abstract (authors: EMS medical director for the District of Herford and Herford Hospital, Germany); smartwatch brand not stated in the accessible abstract (full text paywalled); watch auto-reported the fall from ventricular fibrillation to the local emergency control center, the dispatcher phoned his wife, EMS resuscitated him, discharged without long-term complications (publication date)", "model_version": null, "outcome": "recurred", "source": "Grautoff & Watol (2024). Herzschr Elektrophys 35(2):140-143. https://doi.org/10.1007/s00399-024-01026-w" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "463", "title": "Apple Devices Mistook Skiing Activities, Dialed False Distress Emergency Calls", "url": "https://incidentdatabase.ai/cite/463", "severity": null, "group": "personal", "basis": "Same developer and feature family: Apple Watch automatic emergency calling; Fall Detection summoned 112 for a collapsed runner, while Crash Detection placed false 911 calls for skiers, harming Apple Watch users and emergency dispatchers." }, "headline": "An Apple Watch called for help after a runner’s heart stopped", "metric": "CPR at 6–7 min, full neurological recovery", "comparator": { "against": "being found without the automatic alert, on a remote spot behind a hedge", "before": "Zonder die melding had ik hem niet zo snel gevonden (without that alert I would not have found him so quickly)", "after": "CPR approximately 6–7 min after collapse; full neurological recovery", "basis": "assumed", "quote": "'De locatie was heel precies. Hij lag achter een heg op een afgelegen plek. Zonder die melding had ik hem niet zo snel gevonden.'", "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven" }, "later": { "status": "unknown", "date": null, "note": "No development dated after the 1 January 2026 event beyond the case report and interviews already cited was found.", "quote": null, "url": null }, "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": "2026-10-20", "change": "Paired with AI Incident Database incident 463 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Noted in the account that the Netherlands is inferred from the authors' affiliations rather than stated in the case text, and removed “at home” and “Herford district” from the Grautoff & Watol echo because the abstract gives no place or setting beyond the patient lying on the floor.", "source": "https://europepmc.org/article/PMC/PMC13543876 ; https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=DOI:10.1007/s00399-024-01026-w&resultType=core&format=json", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Dated to 1 January 2026 and placed at Varsseveld from two interviews with the patient (Men's Health NL, WANT); the street, Oude Doetinchemseweg, is inferred from a dispatch log and the alert's map and marked as such; sources added.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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 from the cited sources.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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, purpose, 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": "Personal benefit depth changed from 4 to 5 for the reported life-preserving outcome.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the separate 2024 ventricular-fibrillation case from this patient's evidence and consolidated the two Men's Health excerpts. Retained the clinical report as the lead, the interviews for identification and contradictory details, and the pager reproduction only for contextual dispatch timing. Added the Apple documentation cited by the clinical paper for the device's behavior. Preserved that linking the anonymous patient to Alwi Siebers is an inference.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved Apple Watch Series 10 from the AI model-version field to the documented hardware configuration. Reviewed the original clinical report, patient interviews, and Apple documentation; the deployed watchOS and classifier releases remain unreported.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced “called 112” with “called emergency services” in the subtitle.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent case report, two independent news reports, the makers' other and one other with authorship relationship unassessed. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0023", "catalog_version": "1.0.0", "source_fingerprint": "8049d87606517d6e72056f910c254541f517f3b85d1f9e8ff95f9e6782ad1ebd", "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" } ], "source_ids": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-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": "Apple" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 paper reports that the watch recognized the fall and automatically called 112 and alerted emergency contacts. Those alerts brought responders to the man behind the hedge. Humans provided resuscitation, defibrillation, and surgery. The watch's contribution is documented in the clinical narrative, but the time to rescue or outcome without it cannot be measured from this case. Apple's documentation describes the detection and notification sequence without disclosing the classifier or model version." } } }, "provenance": { "kind": "source_review", "basis": "Updated only the mapped fields affected by this source review. Other mapped content and review decisions are preserved.", "record_fields": [ "ai_role" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The clinical report states that the watch detected the fall, called emergency services, and alerted emergency contacts. Responders found the man behind a hedge and treated his cardiac arrest. The report does not establish how quickly he would have been found without the watch.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The paper reports that the watch recognized the fall and automatically called 112 and alerted emergency contacts. Those alerts brought responders to the man behind the hedge. Humans provided resuscitation, defibrillation, and surgery. The watch's contribution is documented in the clinical narrative, but the time to rescue or outcome without it cannot be measured from this case. Apple's documentation describes the detection and notification sequence without disclosing the classifier or model version." } } }, "provenance": { "kind": "source_review", "basis": "Updated only the mapped fields affected by this source review. Other mapped content and review decisions are preserved.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "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": 5, "reason": "The patient was alone and hidden from view in cardiac arrest. The automatic alerts brought CPR within 6–7 minutes. He left hospital neurologically intact, which the source presents as life-saving." } ] } }, "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 case report, two independent news reports, the makers' other and one other with authorship relationship unassessed. The cited evidence includes no finding by a regulator or court.", "interpretation": "Existing display rule retained. Originality, authorship, and independence are assessed separately in the source lineage. Repeated formats, quotations, and versions are not independent confirmations." } }, "provenance": { "kind": "source_review", "basis": "Rechecked source authorship and independence without changing benefit-depth ratings.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "The patient was alone and hidden from view in cardiac arrest. The automatic alerts brought CPR within 6–7 minutes. He left hospital neurologically intact, which the source presents as life-saving." } ] } }, "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": 5, "reason": "The patient was alone and hidden from view in cardiac arrest. The automatic alerts brought CPR within 6–7 minutes. He left hospital neurologically intact, which the source presents as life-saving." } ] } }, "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": "Same developer and feature family: Apple Watch automatic emergency calling; Fall Detection summoned 112 for a collapsed runner, while Crash Detection placed false 911 calls for skiers, harming Apple Watch users and emergency dispatchers.", "links": [ { "database": "AI Incident Database", "identifier": "463", "title": "Apple Devices Mistook Skiing Activities, Dialed False Distress Emergency Calls", "url": "https://incidentdatabase.ai/cite/463", "group": "personal", "basis": "Same developer and feature family: Apple Watch automatic emergency calling; Fall Detection summoned 112 for a collapsed runner, while Crash Detection placed false 911 calls for skiers, harming Apple Watch users and emergency dispatchers.", "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", 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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": "CPR at 6–7 min, full neurological recovery", "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": { "paired_incident": { "database": "AI Incident Database", "identifier": "463", "title": "Apple Devices Mistook Skiing Activities, Dialed False Distress Emergency Calls", "url": "https://incidentdatabase.ai/cite/463", "group": "personal", "basis": "Same developer and feature family: Apple Watch automatic emergency calling; Fall Detection summoned 112 for a collapsed runner, while Crash Detection placed false 911 calls for skiers, harming Apple Watch users and emergency dispatchers." } } }, "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": "unknown", "note": "No development dated after the 1 January 2026 event beyond the case report and interviews already cited 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-0023" } }, "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" } }, "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 case report published on 12 August 2026 describes a 69-year-old man who collapsed from ventricular fibrillation while running alone. He was on a dead-end road, hidden behind a hedge. The paper gives no date or place. The authors' hospitals are Radboud University Medical Center, Nijmegen, and Slingeland Hospital, Doetinchem. His Apple Watch (reported as Apple Watch 10) with Fall Detection enabled detected the fall. It automatically alerted his predefined emergency contacts and the emergency medical services by calling 112. Family members reached him, found him unresponsive and began cardiopulmonary resuscitation (CPR) about 6–7 minutes after the collapse. An ambulance arrived about 1.5 minutes later, defibrillated him and restored circulation. He underwent coronary artery bypass grafting and defibrillator implantation, was discharged on day 29 and made a full neurological recovery. Two interviews identify the patient as Alwi Siebers, 69, of Varsseveld, and date the collapse to 1 January 2026. Men's Health NL (28 April 2026) opens “1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland” (1 January 2026 begins for 69-year-old Alwi Siebers as an ordinary running day; the keen runner from Varsseveld is training for the Montferland half marathon). His son Tristan says “De locatie was heel precies. Hij lag achter een heg op een afgelegen plek. Zonder die melding had ik hem niet zo snel gevonden” (the location was very precise; he was lying behind a hedge in a remote spot; without that alert I would not have found him so quickly). WANT (1 May 2026) quotes Siebers, “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon” (I know from my activity log that I started my training loop at twelve past twelve). It reproduces the alert his son received, headed “do 1 jan, 12:12”. The interviews match the paper on the man's age and the Apple Watch Series 10 with fall detection. They also match on the automatic alert to 112 and to his emergency contacts, the hedge and the remote spot. They match on the son starting CPR and the daughter giving mouth-to-mouth, and a history of asthma and bronchiectasis. They match on six days in intensive care, four bypass grafts, an ICD and a full recovery. No source names both the paper and Siebers, so the identification is inferred from these details. Men's Health says that Siebers also pressed the SOS button himself and that a pulse returned within about three minutes. The paper's timeline does not support that: CPR after 6–7 minutes, four shocks, adrenaline and amiodarone before circulation returned. The street is probable but inferred. A fire-brigade pager message of 1 January 2026 at 12:18:59 records a resuscitation on the Oude Doetinchemseweg in Varsseveld. That is a dead-end road at the western edge of the village. The map in the alert screenshot shows the pin on a side road off the Doetinchemseweg. No source names the street in connection with Siebers.", "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": 5, "reason": "The patient was alone and hidden from view in cardiac arrest. The automatic alerts brought CPR within 6–7 minutes. He left hospital neurologically intact, which the source presents as life-saving." } ] } }, "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": "Fall Detection is built to recognize a hard fall and, if the wearer stays immobile, to call emergency services and notify contacts with the location. That is what it did." } }, "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": "Varsseveld, Gelderland, Netherlands", "developer_country": "United States" } }, "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": "Fall Detection is built to recognize a hard fall and, if the wearer stays immobile, to call emergency services and notify contacts with the location. That is what it did." } }, "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": "2026-01-01", "date_basis": "event", "date_precision": "day", "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 463 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Noted in the account that the Netherlands is inferred from the authors' affiliations rather than stated in the case text, and removed “at home” and “Herford district” from the Grautoff & Watol echo because the abstract gives no place or setting beyond the patient lying on the floor.", "source": "https://europepmc.org/article/PMC/PMC13543876 ; https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=DOI:10.1007/s00399-024-01026-w&resultType=core&format=json", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Dated to 1 January 2026 and placed at Varsseveld from two interviews with the patient (Men's Health NL, WANT); the street, Oude Doetinchemseweg, is inferred from a dispatch log and the alert's map and marked as such; sources added.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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 from the cited sources.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-23", "change": "Personal benefit depth changed from 4 to 5 for the reported life-preserving outcome.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the separate 2024 ventricular-fibrillation case from this patient's evidence and consolidated the two Men's Health excerpts. Retained the clinical report as the lead, the interviews for identification and contradictory details, and the pager reproduction only for contextual dispatch timing. Added the Apple documentation cited by the clinical paper for the device's behavior. Preserved that linking the anonymous patient to Alwi Siebers is an inference.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved Apple Watch Series 10 from the AI model-version field to the documented hardware configuration. Reviewed the original clinical report, patient interviews, and Apple documentation; the deployed watchOS and classifier releases remain unreported.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced “called 112” with “called emergency services” in the subtitle.", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Clarified the emergency call in the subtitle without changing the underlying evidence.", "record_fields": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Fall Detection is enabled on a worn Apple Watch with cellular or phone connectivity", "The wearer collapses with an impact the classifier recognizes and then stays immobile", "Emergency services and contacts can reach the shared location quickly" ], "must_hold": [ "The watch has connectivity at the scene", "The collapse produces a detectable fall signature", "Responders arrive within the window in which resuscitation can succeed" ], "echoes": [ { "date": "2024-05-13", "setting": "elderly man, found by his wife lying unresponsive on the floor; place and setting not stated in the abstract (authors: EMS medical director for the District of Herford and Herford Hospital, Germany); smartwatch brand not stated in the accessible abstract (full text paywalled); watch auto-reported the fall from ventricular fibrillation to the local emergency control center, the dispatcher phoned his wife, EMS resuscitated him, discharged without long-term complications (publication date)", "outcome": "recurred", "source": "Grautoff & Watol (2024). Herzschr Elektrophys 35(2):140-143. https://doi.org/10.1007/s00399-024-01026-w" } ] } } }, "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": [ "Fall Detection is enabled on a worn Apple Watch with cellular or phone connectivity", "The wearer collapses with an impact the classifier recognizes and then stays immobile", "Emergency services and contacts can reach the shared location quickly" ], "must_hold": [ "The watch has connectivity at the scene", "The collapse produces a detectable fall signature", "Responders arrive within the window in which resuscitation can succeed" ], "echoes": [ { "date": "2024-05-13", "setting": "elderly man, found by his wife lying unresponsive on the floor; place and setting not stated in the abstract (authors: EMS medical director for the District of Herford and Herford Hospital, Germany); smartwatch brand not stated in the accessible abstract (full text paywalled); watch auto-reported the fall from ventricular fibrillation to the local emergency control center, the dispatcher phoned his wife, EMS resuscitated him, discharged without long-term complications (publication date)", "outcome": "recurred", "source": "Grautoff & Watol (2024). Herzschr Elektrophys 35(2):140-143. https://doi.org/10.1007/s00399-024-01026-w" } ] } } }, "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 463 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/463", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Noted in the account that the Netherlands is inferred from the authors' affiliations rather than stated in the case text, and removed “at home” and “Herford district” from the Grautoff & Watol echo because the abstract gives no place or setting beyond the patient lying on the floor.", "source": "https://europepmc.org/article/PMC/PMC13543876 ; https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=DOI:10.1007/s00399-024-01026-w&resultType=core&format=json", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Dated to 1 January 2026 and placed at Varsseveld from two interviews with the patient (Men's Health NL, WANT); the street, Oude Doetinchemseweg, is inferred from a dispatch log and the alert's map and marked as such; sources added.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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 from the cited sources.", "source": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "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, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-23", "change": "Personal benefit depth changed from 4 to 5 for the reported life-preserving outcome.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the separate 2024 ventricular-fibrillation case from this patient's evidence and consolidated the two Men's Health excerpts. Retained the clinical report as the lead, the interviews for identification and contradictory details, and the pager reproduction only for contextual dispatch timing. Added the Apple documentation cited by the clinical paper for the device's behavior. Preserved that linking the anonymous patient to Alwi Siebers is an inference.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Moved Apple Watch Series 10 from the AI model-version field to the documented hardware configuration. Reviewed the original clinical report, patient interviews, and Apple documentation; the deployed watchOS and classifier releases remain unreported.", "source": "https://europepmc.org/article/PMC/PMC13543876", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Replaced “called 112” with “called emergency services” in the subtitle.", "editor": "AIBD" } ] } }, "provenance": { "kind": "curatorial", "basis": "Clarified the emergency call in the subtitle without changing the underlying evidence.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Emergency medicine / resuscitation", "segment": "Consumer wearables" } }, "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": "2026-10-24", "versions_and_access": [ { "source_url": "https://europepmc.org/article/PMC/PMC13543876", "access_url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13543876/fullTextXML", "status": "Full clinical text and references read." }, { "source_url": "https://support.apple.com/en-us/108896", "version": "May 6, 2026 documentation; later than the reported January event." } ], "limitations": [ "The clinical paper and interviews conflict on manual SOS and the time to return of circulation.", "No source explicitly links the named interviewee to the anonymous clinical case. The street remains inferred.", "The exact Apple classifier, learning method and deployed version are not disclosed in the cited device documentation.", "The original emergency dispatch record and raw watch logs were not available." ] } }, "provenance": { "kind": "source_review", "basis": "Read the available originals and relevant source chains, with unresolved access and evidentiary limits recorded explicitly.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The clinical report and patient interviews describe the rescue. They differ on manual SOS activation and the time to return of circulation. Emergency dispatch records and raw watch logs are unavailable.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "case report", "citation": "Jansen CE, Jaspers Focks J, Manglani Rodriguez S, Pol L, Edgar R, van Royen N, Bonnes JL (2026). Wearable saving lives: a case report. Resuscitation Plus 31:101447.", "title": "Wearable saving lives: a case report", "url": "https://europepmc.org/article/PMC/PMC13543876", "date": "2026-08-12", "written_by": "independent", "quote": "The Apple Watch detected the fall and automatically alerted predefined emergency contacts as well as the emergency medical services by calling 112." }, { "kind": "news report", "citation": "Boerman, E. (2026). ‘Ik was voor drie minuten dood, maar mijn Apple Watch redde mijn leven’. Men's Health NL, April 28, 2026.", "title": "Hartstilstand tijdens hardlopen: Apple Watch redt leven", "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "date": "2026-04-28", "written_by": "independent", "quote": "1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland en trekt eropuit zoals hij dat al jaren doet. Toch gaat het mis. Alwi krijgt tijdens het lopen een hartstilstand." }, { "kind": "news report", "citation": "Kraak J (2026). Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert. WANT, 1 May 2026 (with the alert screenshot headed 'do 1 jan, 12:12').", "title": "Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert", "url": "https://www.want.nl/apple-watch-leven-gered-alwi-siebers/", "date": "2026-05-01", "written_by": "independent", "quote": "Het ging 1 januari van dit jaar echter mis. “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon.” Niet veel later kreeg zoon Tristan een bericht op zijn smartphone. Dat bericht was niet gestuurd door Alwi zelf, maar door zijn Apple Watch Series 10. Het was een melding dat zijn vader gevallen was." }, { "kind": "other", "citation": "112-meldingen.nl (2026). P2000 pager message, Varsseveld, 1 January 2026, 12:18:59, fire brigade Lichtenvoorde (resuscitation, privacy screen), with ambulance capcodes at 12:17 and 12:18.", "title": "Incident Oude Doetinchemseweg Varsseveld", "url": "https://112-meldingen.nl/112-varsseveld/incident-oude-doetinchemseweg-varsseveld-1402058", "date": "2026-01-01", "written_by": "unknown", "quote": "0803203 12:18:59 01-01-26 P 2 BON-02 Reanimatie (zichtscherm) Oude Doetinchemseweg Varsseveld 066371" }, { "kind": "other", "citation": "Apple. Use Fall Detection with Apple Watch, May 6, 2026; “How it works,” “What happens if your Apple Watch detects that you're immobile,” and notes 1–3.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated only the mapped fields affected by this source review. Other mapped content and review decisions are preserved.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "role": "Retained evidence sources in reviewed order" }, "provenance": { "kind": "curatorial", "basis": "Preserved existing source identifiers and appended the material technical source where applicable.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "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": "case report", "citation": "Jansen CE, Jaspers Focks J, Manglani Rodriguez S, Pol L, Edgar R, van Royen N, Bonnes JL (2026). Wearable saving lives: a case report. Resuscitation Plus 31:101447.", "title": "Wearable saving lives: a case report", "url": "https://europepmc.org/article/PMC/PMC13543876", "date": "2026-08-12", "written_by": "independent", "quote": "The Apple Watch detected the fall and automatically alerted predefined emergency contacts as well as the emergency medical services by calling 112." }, { "kind": "news report", "citation": "Boerman, E. (2026). ‘Ik was voor drie minuten dood, maar mijn Apple Watch redde mijn leven’. Men's Health NL, April 28, 2026.", "title": "Hartstilstand tijdens hardlopen: Apple Watch redt leven", "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "date": "2026-04-28", "written_by": "independent", "quote": "1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland en trekt eropuit zoals hij dat al jaren doet. Toch gaat het mis. Alwi krijgt tijdens het lopen een hartstilstand." }, { "kind": "news report", "citation": "Kraak J (2026). Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert. WANT, 1 May 2026 (with the alert screenshot headed 'do 1 jan, 12:12').", "title": "Apple Watch Series 10 redt leven van Alwi (69) en stuurt 112-melding: zelfs Tim Cook reageert", "url": "https://www.want.nl/apple-watch-leven-gered-alwi-siebers/", "date": "2026-05-01", "written_by": "independent", "quote": "Het ging 1 januari van dit jaar echter mis. “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon.” Niet veel later kreeg zoon Tristan een bericht op zijn smartphone. Dat bericht was niet gestuurd door Alwi zelf, maar door zijn Apple Watch Series 10. Het was een melding dat zijn vader gevallen was." }, { "kind": "other", "citation": "112-meldingen.nl (2026). P2000 pager message, Varsseveld, 1 January 2026, 12:18:59, fire brigade Lichtenvoorde (resuscitation, privacy screen), with ambulance capcodes at 12:17 and 12:18.", "title": "Incident Oude Doetinchemseweg Varsseveld", "url": "https://112-meldingen.nl/112-varsseveld/incident-oude-doetinchemseweg-varsseveld-1402058", "date": "2026-01-01", "written_by": "unknown", "quote": "0803203 12:18:59 01-01-26 P 2 BON-02 Reanimatie (zichtscherm) Oude Doetinchemseweg Varsseveld 066371" }, { "kind": "other", "citation": "Apple. Use Fall Detection with Apple Watch, May 6, 2026; “How it works,” “What happens if your Apple Watch detects that you're immobile,” and notes 1–3.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated only the mapped fields affected by this source review. Other mapped content and review decisions are preserved.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-24", "lead_source_ref": "AIBD-0023-source-0001", "retained_source_order": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "origins": [ { "url": "https://europepmc.org/article/PMC/PMC13543876", "contribution": "Original clinical narrative establishes CPR timing, resuscitation, surgery and neurological recovery.", "locator": "Case report; case presentation and discussion; reference 5.", "relationship": "Treating clinical authors report the episode, separately from the device manufacturer.", "source_refs": [ "AIBD-0023-source-0001" ], "display_name": "Resuscitation Plus" }, { "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "contribution": "Interview supplies the name, January 1 event date, family testimony and conflicting SOS/recovery details.", "locator": "Opening narrative and quotations from Alwi and Tristan Siebers.", "relationship": "Original interview. The source does not identify the anonymized journal patient.", "source_refs": [ "AIBD-0023-source-0002" ] }, { "url": "https://www.want.nl/apple-watch-leven-gered-alwi-siebers/", "contribution": "Separate interview and reproduced alert screenshot provide date, watch model and location context.", "locator": "Patient and son interviews; screenshot headed “do 1 jan, 12:12”.", "relationship": "Original reporting from the same witnesses, not another clinical evaluation.", "source_refs": [ "AIBD-0023-source-0003" ] }, { "url": "https://112-meldingen.nl/112-varsseveld/incident-oude-doetinchemseweg-varsseveld-1402058", "contribution": "Reproduced P2000 dispatch gives contextual street and time.", "locator": "Message 0803203, January 1, 2026, 12:18:59.", "relationship": "Intermediary reproduction of emergency traffic. It does not identify the patient.", "source_refs": [ "AIBD-0023-source-0004" ] }, { "url": "https://support.apple.com/en-us/108896", "contribution": "Manufacturer describes automatic calling, immobility timing, connection requirements and false alerts.", "locator": "How it works; immobility section; notes 1–3.", "relationship": "Primary technical description from Apple, not validation of this rescue.", "source_refs": [ "AIBD-0023-source-0005" ] } ], "claim_chains": [ { "claim": "Resuscitation and recovery", "path": [ "https://europepmc.org/article/PMC/PMC13543876" ], "finding": "Clinical case is the original source. Its patient identity and event date are not disclosed." }, { "claim": "Identity, date, and street", "path": [ "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "https://www.want.nl/apple-watch-leven-gered-alwi-siebers/", "https://112-meldingen.nl/112-varsseveld/incident-oude-doetinchemseweg-varsseveld-1402058" ], "finding": "Interview details support an inferred match. The pager entry establishes a dispatch, not the identity of the patient." }, { "claim": "Detection and automatic calls", "path": [ "https://europepmc.org/article/PMC/PMC13543876", "Reference 5", "https://support.apple.com/en-us/108896" ], "finding": "Apple describes functional behavior and connection limits. The case report's separate DETECT-2 reference concerns a different experimental system and cannot establish the deployed Apple model." } ], "limitations": [ "The clinical paper and interviews conflict on manual SOS and the time to return of circulation.", "No source explicitly links the named interviewee to the anonymous clinical case. The street remains inferred.", "The exact Apple classifier, learning method and deployed version are not disclosed in the cited device documentation.", "The original emergency dispatch record and raw watch logs were not available." ], "excluded_or_consolidated_sources": [ { "url": "https://doi.org/10.1007/s00399-024-01026-w", "reason": "Different 2024 patient; cannot corroborate the 2026 episode." }, { "url": "https://www.menshealth.nl/nieuws/hartstilstand-tijdens-hardlopen-apple-watch-redt-leven", "reason": "Second excerpt of the same Men's Health article." } ], "review_scope": "Traced the claims in this case through accessible original publications and relevant embedded references, tables, appendices, corrections, and supplements. The stated access limits remain unresolved.", "system_identity_review": { "reviewed_on": "2026-10-24", "hardware_source_ref": "AIBD-0023-source-0001", "technical_source_ref": "AIBD-0023-source-0005", "locators": [ "Clinical report: Case description, opening paragraph", "Clinical report: reference 5", "Apple: Here's how it works; What happens if your Apple Watch detects that you're immobile" ], "finding": "The sources establish Apple Watch Series 10 and Fall Detection. They do not establish the installed watchOS version, a separate classifier name, or its release.", "excluded_identification": "The DETECT-2 algorithm in reference 10 of the clinical report is a different experimental system and cannot identify the classifier installed on the patient's Apple Watch." } } }, "provenance": { "kind": "source_review", "basis": "Read the available originals and relevant source chains, with unresolved access and evidentiary limits recorded explicitly.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Treating clinicians describe the rescue and recovery. Separate patient and family interviews supply the name, date, and watch hardware. Apple explains how Fall Detection works, without verifying this rescue.", "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": "Fall Detection on his Apple Watch Series 10 called emergency services, and he recovered fully after CPR and hospital treatment.", "account": "A case report published on 12 August 2026 describes a 69-year-old man who collapsed from ventricular fibrillation while running alone. He was on a dead-end road, hidden behind a hedge. The paper gives no date or place. The authors' hospitals are Radboud University Medical Center, Nijmegen, and Slingeland Hospital, Doetinchem. His Apple Watch (reported as Apple Watch 10) with Fall Detection enabled detected the fall. It automatically alerted his predefined emergency contacts and the emergency medical services by calling 112. Family members reached him, found him unresponsive and began cardiopulmonary resuscitation (CPR) about 6–7 minutes after the collapse. An ambulance arrived about 1.5 minutes later, defibrillated him and restored circulation. He underwent coronary artery bypass grafting and defibrillator implantation, was discharged on day 29 and made a full neurological recovery. Two interviews identify the patient as Alwi Siebers, 69, of Varsseveld, and date the collapse to 1 January 2026. Men's Health NL (28 April 2026) opens “1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland” (1 January 2026 begins for 69-year-old Alwi Siebers as an ordinary running day; the keen runner from Varsseveld is training for the Montferland half marathon). His son Tristan says “De locatie was heel precies. Hij lag achter een heg op een afgelegen plek. Zonder die melding had ik hem niet zo snel gevonden” (the location was very precise; he was lying behind a hedge in a remote spot; without that alert I would not have found him so quickly). WANT (1 May 2026) quotes Siebers, “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon” (I know from my activity log that I started my training loop at twelve past twelve). It reproduces the alert his son received, headed “do 1 jan, 12:12”. The interviews match the paper on the man's age and the Apple Watch Series 10 with fall detection. They also match on the automatic alert to 112 and to his emergency contacts, the hedge and the remote spot. They match on the son starting CPR and the daughter giving mouth-to-mouth, and a history of asthma and bronchiectasis. They match on six days in intensive care, four bypass grafts, an ICD and a full recovery. No source names both the paper and Siebers, so the identification is inferred from these details. Men's Health says that Siebers also pressed the SOS button himself and that a pulse returned within about three minutes. The paper's timeline does not support that: CPR after 6–7 minutes, four shocks, adrenaline and amiodarone before circulation returned. The street is probable but inferred. A fire-brigade pager message of 1 January 2026 at 12:18:59 records a resuscitation on the Oude Doetinchemseweg in Varsseveld. That is a dead-end road at the western edge of the village. The map in the alert screenshot shows the pin on a side road off the Doetinchemseweg. No source names the street in connection with Siebers." } }, "provenance": { "kind": "curatorial", "basis": "Clarified the emergency call in the subtitle without changing the underlying evidence.", "record_fields": [ "summary", "account" ], "source_refs": [], "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": "value_present", "value": { "kind": "structured", "data": { "hardware": { "manufacturer": "Apple", "model": "Apple Watch Series 10", "source_wording": "Apple Watch 10", "source_ref": "AIBD-0023-source-0001", "locator": "Case description, opening paragraph" }, "feature": "Fall Detection", "feature_enabled": true, "operating_system": { "name": "watchOS", "status": "not reported for this case" }, "classifier_version": { "status": "not reported for this case" }, "qualification": "The named patient is an inferred match between the anonymous clinical report and the interviews. No software version is inferred from the hardware or event date." } }, "provenance": { "kind": "source_review", "basis": "Separated the reported watch hardware and enabled feature from the undisclosed operating-system and classifier releases.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Apple Watch Series 10 with Fall Detection.", "editor_note": "The Series 10 identification comes from interviews matched to the anonymous clinical report by the circumstances of the collapse and rescue (references 1–3). Neither source explicitly confirms that match." } }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Read the case evidence and the relevant original technical documentation; no case-specific model identity or release was disclosed.", "record_fields": [], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "reason": "Apple names the Fall Detection feature but does not disclose a separate name for the classifier used in this case.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Apple’s classifier for Fall Detection. Apple does not publish a separate model name.", "editor_note": "" } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Read the case evidence and the relevant original technical documentation; no case-specific model identity or release was disclosed.", "record_fields": [ "model_version" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "reason": "The clinical report and patient interviews identify the watch hardware and Fall Detection, but they do not state the installed watchOS version or the classifier release. Apple Watch Series 10 is hardware, not an AI model version.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The installed software and classifier versions are not disclosed.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Apple Watch Fall Detection" } }, "provenance": { "kind": "source_review", "basis": "Identified the reported product or pipeline from its original case and technical sources; kept hardware, underlying model, and software release distinct.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0002", "AIBD-0023-source-0003", "AIBD-0023-source-0004", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete" }, "systems[].provider": { "state": "value_present", "value": { "kind": "text", "text": "Apple" }, "provenance": { "kind": "source_review", "basis": "The case identifies an Apple Watch, and Apple documents its Fall Detection feature.", "record_fields": [ "developer" ], "source_refs": [ "AIBD-0023-source-0001", "AIBD-0023-source-0005" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Apple Watch Fall Detection summoning help after out-of-hospital cardiac arrest", "headline": "An Apple Watch called for help after a runner’s heart stopped" } }, "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": [ "8049d87606517d6e72056f910c254541f517f3b85d1f9e8ff95f9e6782ad1ebd" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed the fingerprint after clarifying the subtitle.", "record_fields": [ "summary", "revisions" ], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A case report published on 12 August 2026 describes a 69-year-old man who collapsed from ventricular fibrillation while running alone. He was on a dead-end road, hidden behind a hedge. The paper gives no date or place. The authors' hospitals are Radboud University Medical Center, Nijmegen, and Slingeland Hospital, Doetinchem. His Apple Watch (reported as Apple Watch 10) with Fall Detection enabled detected the fall. It automatically alerted his predefined emergency contacts and the emergency medical services by calling 112. Family members reached him, found him unresponsive and began cardiopulmonary resuscitation (CPR) about 6–7 minutes after the collapse. An ambulance arrived about 1.5 minutes later, defibrillated him and restored circulation. He underwent coronary artery bypass grafting and defibrillator implantation, was discharged on day 29 and made a full neurological recovery. Two interviews identify the patient as Alwi Siebers, 69, of Varsseveld, and date the collapse to 1 January 2026. Men's Health NL (28 April 2026) opens “1 januari 2026 begint voor de 69-jarige Alwi Siebers als een gewone hardloopdag. De fanatieke loper uit Varsseveld is in training voor de halve marathon van Montferland” (1 January 2026 begins for 69-year-old Alwi Siebers as an ordinary running day; the keen runner from Varsseveld is training for the Montferland half marathon). His son Tristan says “De locatie was heel precies. Hij lag achter een heg op een afgelegen plek. Zonder die melding had ik hem niet zo snel gevonden” (the location was very precise; he was lying behind a hedge in a remote spot; without that alert I would not have found him so quickly). WANT (1 May 2026) quotes Siebers, “Ik weet via mijn activiteitenlog dat ik om twaalf over twaalf aan mijn trainingsrondje begon” (I know from my activity log that I started my training loop at twelve past twelve). It reproduces the alert his son received, headed “do 1 jan, 12:12”. The interviews match the paper on the man's age and the Apple Watch Series 10 with fall detection. They also match on the automatic alert to 112 and to his emergency contacts, the hedge and the remote spot. They match on the son starting CPR and the daughter giving mouth-to-mouth, and a history of asthma and bronchiectasis. They match on six days in intensive care, four bypass grafts, an ICD and a full recovery. No source names both the paper and Siebers, so the identification is inferred from these details. Men's Health says that Siebers also pressed the SOS button himself and that a pulse returned within about three minutes. The paper's timeline does not support that: CPR after 6–7 minutes, four shocks, adrenaline and amiodarone before circulation returned. The street is probable but inferred. A fire-brigade pager message of 1 January 2026 at 12:18:59 records a resuscitation on the Oude Doetinchemseweg in Varsseveld. That is a dead-end road at the western edge of the village. The map in the alert screenshot shows the pin on a side road off the Doetinchemseweg. No source names the street in connection with Siebers.", "replication": { "produced_by": [ "Fall Detection is enabled on a worn Apple Watch with cellular or phone connectivity", "The wearer collapses with an impact the classifier recognizes and then stays immobile", "Emergency services and contacts can reach the shared location quickly" ], "must_hold": [ "The watch has connectivity at the scene", "The collapse produces a detectable fall signature", "Responders arrive within the window in which resuscitation can succeed" ], "echoes": [ { "date": "2024-05-13", "setting": "elderly man, found by his wife lying unresponsive on the floor; place and setting not stated in the abstract (authors: EMS medical director for the District of Herford and Herford Hospital, Germany); smartwatch brand not stated in the accessible abstract (full text paywalled); watch auto-reported the fall from ventricular fibrillation to the local emergency control center, the dispatcher phoned his wife, EMS resuscitated him, discharged without long-term complications (publication date)", "outcome": "recurred", "source": "Grautoff & Watol (2024). Herzschr Elektrophys 35(2):140-143. https://doi.org/10.1007/s00399-024-01026-w" } ] } } }, "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": 34, "reviewed": 15, "record_mapped": 21, "not_assessed": 55, "complete": false, "by_state": { "value_present": 34, "not_reported": 2, "unresolved": 0, "not_assessed": 55, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0313", "case_url": "records/AIBD-0313.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "the fire's estimated spread without AI-powered imaging, as estimated by ALERTCalifornia founder Neal Driscoll", "url": "https://unlocked.microsoft.com/alertcalifornia-ai-wildfire-detection/" }, { "label": "Before", "value": "nearly 4,000 acres" }, { "label": "After", "value": "52 acres" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0313", "catalog_version": "1.0.0", "source_fingerprint": "b83738a9b8addc0e441ba937a2c21d50301a9915bf167e11bd5207f50a469d3e", "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-0313-source-0001", "AIBD-0313-source-0002", "AIBD-0313-source-0003", "AIBD-0313-source-0004", "AIBD-0313-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": "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" } }, "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": "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. 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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. 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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. 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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). 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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-0296", "case_url": "records/AIBD-0296.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Per IEEE Spectrum, the Embrace detected the seizure and its app alerted McGrath's mother, who made the emergency call. The device did not call for help itself. The article calls the algorithm proprietary. The FDA summary describes it as an on-board algorithm validated against video-EEG. Empatica's own 2021 study calls it a predefined machine-learning algorithm. The McGrath event is known only from this single news item. The AES case-study abstract is not among the sources." }, { "label": "Comparison", "value": "seizures recorded during more than 6,500 hours of monitoring of 135 patients (device test reported by IEEE Spectrum)", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life" }, { "label": "After", "value": "the device correctly identified nearly every single seizure, with a false alarm rate below that of the seizure frequency for most individuals" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0296", "title": "Empatica Embrace seizure alert reaches Morgan McGrath's mother, who calls paramedics after a seizure left the 26-year-old teacher not breathing (Fulton, Missouri)", "summary": "IEEE Spectrum (6 February 2018): an Embrace wristband alerted the mother of Morgan McGrath, 26, of Fulton, Mo., during a seizure about 15 months earlier; she called paramedics and McGrath survived.", "date_observed": "2018-02-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Elie Dolgin reported in IEEE Spectrum on 6 February 2018 on Morgan McGrath, a 26-year-old teacher from Fulton, Missouri. She “suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing”. The article dates the seizure to “fifteen months ago”, about November 2016. Her wrist-worn Embrace device alerted her mother, who called the paramedics, and “thanks to a timely response, McGrath is alive and well today”. The article says it records skin conductance and three-axis accelerometer data and “uses a proprietary algorithm to signal when someone is having a convulsive seizure”. The app sends a text or call to pre-specified caregivers. In testing on 135 patients over more than 6,500 hours it “correctly identified nearly every single seizure”. The Food and Drug Administration (FDA) cleared Embrace (Empatica S.r.l.) under 510(k) K172935 on 26 January 2018, after the McGrath event. The 510(k) summary describes an on-board algorithm validated against video electroencephalography (EEG). On detecting a likely generalized tonic-clonic seizure, it messages the Alert app to call and text designated caregivers. A later prospective multicenter study (Onorati et al. 2021) by Empatica staff with hospital co-authors reports sensitivity for a predefined machine-learning algorithm. It was 0.92 in children and 0.94 in adults. An independent Montreal epilepsy-monitoring-unit study (Jahani et al. 2026) found the Embrace2 detected all 16 convulsive seizures while worn and working. Alerts reached caregivers in 15 of 16 cases, with 29 false alarms. The McGrath rescue rests on this single news item. The Empatica case-study abstract (Regalia, Onorati, Picard, American Epilepsy Society) is not among the sources. No second account of the McGrath event was found.", "category": "protective response", "unexpected": false, "purpose": "Built to detect convulsive seizures from electrodermal activity and motion and to summon designated caregivers. The alert to McGrath's mother was that purpose.", "system": "Empatica Embrace wrist-worn seizure detection (electrodermal activity and accelerometer, on-board algorithm) with the Alert smartphone app", "model_version": "Embrace (the article does not state the generation); echoes concern Embrace2", "developer": "Empatica", "deployer": null, "location": "Fulton, Missouri, United States", "domain": "Healthcare (epilepsy; seizure monitoring)", "segment": "Prescription wearable seizure monitor used at home", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "A young woman not breathing during a seizure had paramedics summoned by her mother on the device's alert and is reported alive and well. A potentially life-saving response documented only in one news report." } ], "ai_role": { "status": "assessed", "statement": "Per IEEE Spectrum, the Embrace detected the seizure and its app alerted McGrath's mother, who made the emergency call. The device did not call for help itself. The article calls the algorithm proprietary. The FDA summary describes it as an on-board algorithm validated against video-EEG. Empatica's own 2021 study calls it a predefined machine-learning algorithm. The McGrath event is known only from this single news item. The AES case-study abstract is not among the sources." }, "evidence": [ { "kind": "news report", "citation": "Dolgin E (2018). This Seizure-Detecting Smartwatch Could Save Your Life. IEEE Spectrum, 6 February 2018.", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "McGrath's wristworn Embrace device sent her mother an alert. Her mother called the paramedics." }, { "kind": "news report", "citation": "Dolgin E (2018), IEEE Spectrum, 6 February 2018 (on the seizure).", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "Fifteen months ago, the 26-year-old teacher from Fulton, Mo., suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing." }, { "kind": "regulatory document", "citation": "U.S. Food and Drug Administration (2018). 510(k) Summary K172935, Embrace, Empatica S.r.l.; decision 26 January 2018 (text extracted from the PDF).", "title": "510(k) Summary K172935: Embrace", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf17/K172935.pdf", "date": "2018-01-26", "written_by": "makers", "quote": "When a likely GTC is detected, the Embrace sends a message to the Alert smartphone application, which initiates calls and texts to summon the attention of designated caregivers." }, { "kind": "peer-reviewed study", "citation": "Onorati F, Regalia G, Caborni C, LaFrance WC Jr, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R (2021). Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Frontiers in Neurology 12:724904 (PMC8418082). Lead authors at Empatica, Inc.", "title": "Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/", "date": "2021-08-18", "written_by": "makers", "quote": "A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm." }, { "kind": "peer-reviewed study", "citation": "Jahani A, Galindo Lazo DA, Sarzo Wabi I, Gharbi O, Robert M, Millan JP, D'Onofrio G, Robin A, Nguyen DK, Bou Assi E (2026). Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit. Epilepsia 67(4):1802-1816 (PMC13075604).", "title": "Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "date": "2026-01-07", "written_by": "independent", "quote": "Alerts successfully reached caregivers in 15 of 16 cases, with one failure due to a Wi‐Fi disconnection." } ], "replication": { "produced_by": [ "A convulsive (generalized tonic-clonic) seizure, the only type the device is cleared to detect", "The wristband worn and paired with a phone running the Alert app, with a caregiver designated", "A caregiver able to reach the patient or call emergency services on receiving the alert" ], "must_hold": [ "Connectivity between watch, phone and caregiver (one of 16 alerts in Montreal failed on a Wi-Fi disconnection)", "Caregivers tolerate false alarms (29 in 373 monitoring days in Montreal, mostly from routine movements)", "Focal seizures without convulsions are not detected (none of 510 in Montreal)" ], "echoes": [ { "date": "2026-01-07", "setting": "Center hospitalier de l'Université de Montréal epilepsy monitoring unit, 72 patients, April 2024 to June 2025; alerts routed to nursing staff", "model_version": "Embrace2", "outcome": "recurred", "source": "Jahani et al. (2026) Epilepsia 67(4):1802-1816, https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/ (quote: 'The Embrace2 wrist‐worn device detected all 16 FBTCSs/GTCSs that occurred while the device was functioning and worn, but none of 510 FSs.')" }, { "date": "2021-08-18", "setting": "Multicentre epilepsy-monitoring-unit study, 152 patients (85 children), 66 convulsive seizures; detection performance only, no home alerts", "model_version": null, "outcome": "recurred", "source": "Onorati et al. (2021) Front Neurol 12:724904, https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/ (sensitivity 0.92 pediatric, 0.94 adult; false alarm rate 1.26 and 0.57 per 24 h)" } ] }, "paired_incident": null, "headline": "Empatica Embrace seizure alert reaches Morgan McGrath's mother", "metric": null, "comparator": { "against": "seizures recorded during more than 6,500 hours of monitoring of 135 patients (device test reported by IEEE Spectrum)", "before": null, "after": "the device correctly identified nearly every single seizure, with a false alarm rate below that of the seizure frequency for most individuals", "basis": "measured", "quote": "When tested on 135 patients who were monitored for a collective total of more than 6,500 hours, the device correctly identified nearly every single seizure, with a false alarm rate below that of the seizure frequency for most individuals.", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life" }, "later": { "status": "persisted", "date": "2026-01-07", "note": "An independent Montreal epilepsy-monitoring-unit study later found the Embrace2 detected all 16 convulsive seizures that occurred while worn and working, with alerts reaching caregivers in 15 of 16 cases and 29 false alarms over the study.", "quote": "Alerts successfully reached caregivers in 15 of 16 cases, with one failure due to a Wi‐Fi disconnection.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/" }, "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 the benefit is measured against, what happened since from the cited sources.", "source": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life; https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "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/Empatica", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study, one independent news report, the makers' regulatory document and the makers' peer-reviewed study. 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The FDA summary describes it as an on-board algorithm validated against video-EEG. Empatica's own 2021 study calls it a predefined machine-learning algorithm. The McGrath event is known only from this single news item. 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She “suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing”. The article dates the seizure to “fifteen months ago”, about November 2016. Her wrist-worn Embrace device alerted her mother, who called the paramedics, and “thanks to a timely response, McGrath is alive and well today”. The article says it records skin conductance and three-axis accelerometer data and “uses a proprietary algorithm to signal when someone is having a convulsive seizure”. The app sends a text or call to pre-specified caregivers. In testing on 135 patients over more than 6,500 hours it “correctly identified nearly every single seizure”. The Food and Drug Administration (FDA) cleared Embrace (Empatica S.r.l.) under 510(k) K172935 on 26 January 2018, after the McGrath event. The 510(k) summary describes an on-board algorithm validated against video electroencephalography (EEG). On detecting a likely generalized tonic-clonic seizure, it messages the Alert app to call and text designated caregivers. A later prospective multicenter study (Onorati et al. 2021) by Empatica staff with hospital co-authors reports sensitivity for a predefined machine-learning algorithm. It was 0.92 in children and 0.94 in adults. An independent Montreal epilepsy-monitoring-unit study (Jahani et al. 2026) found the Embrace2 detected all 16 convulsive seizures while worn and working. Alerts reached caregivers in 15 of 16 cases, with 29 false alarms. The McGrath rescue rests on this single news item. The Empatica case-study abstract (Regalia, Onorati, Picard, American Epilepsy Society) is not among the sources. No second account of the McGrath event was found.", "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": 3, "reason": "A young woman not breathing during a seizure had paramedics summoned by her mother on the device's alert and is reported alive and well. A potentially life-saving response documented only in one news report." } ] } }, "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 convulsive seizures from electrodermal activity and motion and to summon designated caregivers. The alert to McGrath's mother 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": "Fulton, Missouri, United States", "developer_country": "United States" } }, "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": "Built to detect convulsive seizures from electrodermal activity and motion and to summon designated caregivers. The alert to McGrath's mother 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": "2018-02-06", "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 the benefit is measured against, what happened since from the cited sources.", "source": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life; https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "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/Empatica", "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 convulsive (generalized tonic-clonic) seizure, the only type the device is cleared to detect", "The wristband worn and paired with a phone running the Alert app, with a caregiver designated", "A caregiver able to reach the patient or call emergency services on receiving the alert" ], "must_hold": [ "Connectivity between watch, phone and caregiver (one of 16 alerts in Montreal failed on a Wi-Fi disconnection)", "Caregivers tolerate false alarms (29 in 373 monitoring days in Montreal, mostly from routine movements)", "Focal seizures without convulsions are not detected (none of 510 in Montreal)" ], "echoes": [ { "date": "2026-01-07", "setting": "Center hospitalier de l'Université de Montréal epilepsy monitoring unit, 72 patients, April 2024 to June 2025; alerts routed to nursing staff", "model_version": "Embrace2", "outcome": "recurred", "source": "Jahani et al. (2026) Epilepsia 67(4):1802-1816, https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/ (quote: 'The Embrace2 wrist‐worn device detected all 16 FBTCSs/GTCSs that occurred while the device was functioning and worn, but none of 510 FSs.')" }, { "date": "2021-08-18", "setting": "Multicentre epilepsy-monitoring-unit study, 152 patients (85 children), 66 convulsive seizures; detection performance only, no home alerts", "outcome": "recurred", "source": "Onorati et al. (2021) Front Neurol 12:724904, https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/ (sensitivity 0.92 pediatric, 0.94 adult; false alarm rate 1.26 and 0.57 per 24 h)" } ] } } }, "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 convulsive (generalized tonic-clonic) seizure, the only type the device is cleared to detect", "The wristband worn and paired with a phone running the Alert app, with a caregiver designated", "A caregiver able to reach the patient or call emergency services on receiving the alert" ], "must_hold": [ "Connectivity between watch, phone and caregiver (one of 16 alerts in Montreal failed on a Wi-Fi disconnection)", "Caregivers tolerate false alarms (29 in 373 monitoring days in Montreal, mostly from routine movements)", "Focal seizures without convulsions are not detected (none of 510 in Montreal)" ], "echoes": [ { "date": "2026-01-07", "setting": "Center hospitalier de l'Université de Montréal epilepsy monitoring unit, 72 patients, April 2024 to June 2025; alerts routed to nursing staff", "model_version": "Embrace2", "outcome": "recurred", "source": "Jahani et al. (2026) Epilepsia 67(4):1802-1816, https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/ (quote: 'The Embrace2 wrist‐worn device detected all 16 FBTCSs/GTCSs that occurred while the device was functioning and worn, but none of 510 FSs.')" }, { "date": "2021-08-18", "setting": "Multicentre epilepsy-monitoring-unit study, 152 patients (85 children), 66 convulsive seizures; detection performance only, no home alerts", "outcome": "recurred", "source": "Onorati et al. (2021) Front Neurol 12:724904, https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/ (sensitivity 0.92 pediatric, 0.94 adult; false alarm rate 1.26 and 0.57 per 24 h)" } ] } } }, "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 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, what happened since from the cited sources.", "source": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life; https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "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/Empatica", "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": "Healthcare (epilepsy; seizure monitoring)", "segment": "Prescription wearable seizure monitor used at home" } }, "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": "Dolgin E (2018). This Seizure-Detecting Smartwatch Could Save Your Life. IEEE Spectrum, 6 February 2018.", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "McGrath's wristworn Embrace device sent her mother an alert. Her mother called the paramedics." }, { "kind": "news report", "citation": "Dolgin E (2018), IEEE Spectrum, 6 February 2018 (on the seizure).", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "Fifteen months ago, the 26-year-old teacher from Fulton, Mo., suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing." }, { "kind": "regulatory document", "citation": "U.S. Food and Drug Administration (2018). 510(k) Summary K172935, Embrace, Empatica S.r.l.; decision 26 January 2018 (text extracted from the PDF).", "title": "510(k) Summary K172935: Embrace", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf17/K172935.pdf", "date": "2018-01-26", "written_by": "makers", "quote": "When a likely GTC is detected, the Embrace sends a message to the Alert smartphone application, which initiates calls and texts to summon the attention of designated caregivers." }, { "kind": "peer-reviewed study", "citation": "Onorati F, Regalia G, Caborni C, LaFrance WC Jr, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R (2021). Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Frontiers in Neurology 12:724904 (PMC8418082). Lead authors at Empatica, Inc.", "title": "Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/", "date": "2021-08-18", "written_by": "makers", "quote": "A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm." }, { "kind": "peer-reviewed study", "citation": "Jahani A, Galindo Lazo DA, Sarzo Wabi I, Gharbi O, Robert M, Millan JP, D'Onofrio G, Robin A, Nguyen DK, Bou Assi E (2026). Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit. Epilepsia 67(4):1802-1816 (PMC13075604).", "title": "Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "date": "2026-01-07", "written_by": "independent", "quote": "Alerts successfully reached caregivers in 15 of 16 cases, with one failure due to a Wi‐Fi disconnection." } ] } }, "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-0296-source-0001", "AIBD-0296-source-0002", "AIBD-0296-source-0003", "AIBD-0296-source-0004", "AIBD-0296-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": "Dolgin E (2018). This Seizure-Detecting Smartwatch Could Save Your Life. IEEE Spectrum, 6 February 2018.", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "McGrath's wristworn Embrace device sent her mother an alert. Her mother called the paramedics." }, { "kind": "news report", "citation": "Dolgin E (2018), IEEE Spectrum, 6 February 2018 (on the seizure).", "title": "This Seizure-Detecting Smartwatch Could Save Your Life", "url": "https://spectrum.ieee.org/this-seizuredetecting-smartwatch-could-save-your-life", "date": "2018-02-06", "written_by": "independent", "quote": "Fifteen months ago, the 26-year-old teacher from Fulton, Mo., suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing." }, { "kind": "regulatory document", "citation": "U.S. Food and Drug Administration (2018). 510(k) Summary K172935, Embrace, Empatica S.r.l.; decision 26 January 2018 (text extracted from the PDF).", "title": "510(k) Summary K172935: Embrace", "url": "https://www.accessdata.fda.gov/cdrh_docs/pdf17/K172935.pdf", "date": "2018-01-26", "written_by": "makers", "quote": "When a likely GTC is detected, the Embrace sends a message to the Alert smartphone application, which initiates calls and texts to summon the attention of designated caregivers." }, { "kind": "peer-reviewed study", "citation": "Onorati F, Regalia G, Caborni C, LaFrance WC Jr, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R (2021). Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Frontiers in Neurology 12:724904 (PMC8418082). Lead authors at Empatica, Inc.", "title": "Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/", "date": "2021-08-18", "written_by": "makers", "quote": "A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm." }, { "kind": "peer-reviewed study", "citation": "Jahani A, Galindo Lazo DA, Sarzo Wabi I, Gharbi O, Robert M, Millan JP, D'Onofrio G, Robin A, Nguyen DK, Bou Assi E (2026). Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit. Epilepsia 67(4):1802-1816 (PMC13075604).", "title": "Prospective evaluation of a seizure detection wearable device for timely interventions in an epilepsy monitoring unit", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/", "date": "2026-01-07", "written_by": "independent", "quote": "Alerts successfully reached caregivers in 15 of 16 cases, with one failure due to a Wi‐Fi disconnection." } ] } }, "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": "IEEE Spectrum (6 February 2018): an Embrace wristband alerted the mother of Morgan McGrath, 26, of Fulton, Mo., during a seizure about 15 months earlier; she called paramedics and McGrath survived.", "account": "Elie Dolgin reported in IEEE Spectrum on 6 February 2018 on Morgan McGrath, a 26-year-old teacher from Fulton, Missouri. She “suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing”. The article dates the seizure to “fifteen months ago”, about November 2016. Her wrist-worn Embrace device alerted her mother, who called the paramedics, and “thanks to a timely response, McGrath is alive and well today”. The article says it records skin conductance and three-axis accelerometer data and “uses a proprietary algorithm to signal when someone is having a convulsive seizure”. The app sends a text or call to pre-specified caregivers. In testing on 135 patients over more than 6,500 hours it “correctly identified nearly every single seizure”. The Food and Drug Administration (FDA) cleared Embrace (Empatica S.r.l.) under 510(k) K172935 on 26 January 2018, after the McGrath event. The 510(k) summary describes an on-board algorithm validated against video electroencephalography (EEG). On detecting a likely generalized tonic-clonic seizure, it messages the Alert app to call and text designated caregivers. A later prospective multicenter study (Onorati et al. 2021) by Empatica staff with hospital co-authors reports sensitivity for a predefined machine-learning algorithm. It was 0.92 in children and 0.94 in adults. An independent Montreal epilepsy-monitoring-unit study (Jahani et al. 2026) found the Embrace2 detected all 16 convulsive seizures while worn and working. Alerts reached caregivers in 15 of 16 cases, with 29 false alarms. The McGrath rescue rests on this single news item. The Empatica case-study abstract (Regalia, Onorati, Picard, American Epilepsy Society) is not among the sources. No second account of the McGrath event 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 device classified sensor signals for possible seizures and sent alerts through its phone app." }, "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 initial report concerned the Embrace device. Later reports concerned Embrace2." }, "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": "Embrace (the article does not state the generation); echoes concern Embrace2" } }, "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": "Empatica Embrace wrist-worn seizure detection (electrodermal activity and accelerometer, on-board algorithm) with the Alert smartphone 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": "Empatica" } }, "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": "Empatica Embrace seizure alert reaches Morgan McGrath's mother, who calls paramedics after a seizure left the 26-year-old teacher not breathing (Fulton, Missouri)", "headline": "Empatica Embrace seizure alert reaches Morgan McGrath's mother" } }, "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": [ "f99e1615d50bffa666908e3450541b8efeac87e0e2db34b16b81b314b6d8fe87" ], "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": "Elie Dolgin reported in IEEE Spectrum on 6 February 2018 on Morgan McGrath, a 26-year-old teacher from Fulton, Missouri. She “suffered an epileptic seizure that left her unresponsive, frothing at the mouth, with blue lips, and not breathing”. The article dates the seizure to “fifteen months ago”, about November 2016. Her wrist-worn Embrace device alerted her mother, who called the paramedics, and “thanks to a timely response, McGrath is alive and well today”. The article says it records skin conductance and three-axis accelerometer data and “uses a proprietary algorithm to signal when someone is having a convulsive seizure”. The app sends a text or call to pre-specified caregivers. In testing on 135 patients over more than 6,500 hours it “correctly identified nearly every single seizure”. The Food and Drug Administration (FDA) cleared Embrace (Empatica S.r.l.) under 510(k) K172935 on 26 January 2018, after the McGrath event. The 510(k) summary describes an on-board algorithm validated against video electroencephalography (EEG). On detecting a likely generalized tonic-clonic seizure, it messages the Alert app to call and text designated caregivers. A later prospective multicenter study (Onorati et al. 2021) by Empatica staff with hospital co-authors reports sensitivity for a predefined machine-learning algorithm. It was 0.92 in children and 0.94 in adults. An independent Montreal epilepsy-monitoring-unit study (Jahani et al. 2026) found the Embrace2 detected all 16 convulsive seizures while worn and working. Alerts reached caregivers in 15 of 16 cases, with 29 false alarms. The McGrath rescue rests on this single news item. The Empatica case-study abstract (Regalia, Onorati, Picard, American Epilepsy Society) is not among the sources. No second account of the McGrath event was found.", "replication": { "produced_by": [ "A convulsive (generalized tonic-clonic) seizure, the only type the device is cleared to detect", "The wristband worn and paired with a phone running the Alert app, with a caregiver designated", "A caregiver able to reach the patient or call emergency services on receiving the alert" ], "must_hold": [ "Connectivity between watch, phone and caregiver (one of 16 alerts in Montreal failed on a Wi-Fi disconnection)", "Caregivers tolerate false alarms (29 in 373 monitoring days in Montreal, mostly from routine movements)", "Focal seizures without convulsions are not detected (none of 510 in Montreal)" ], "echoes": [ { "date": "2026-01-07", "setting": "Center hospitalier de l'Université de Montréal epilepsy monitoring unit, 72 patients, April 2024 to June 2025; alerts routed to nursing staff", "model_version": "Embrace2", "outcome": "recurred", "source": "Jahani et al. (2026) Epilepsia 67(4):1802-1816, https://pmc.ncbi.nlm.nih.gov/articles/PMC13075604/ (quote: 'The Embrace2 wrist‐worn device detected all 16 FBTCSs/GTCSs that occurred while the device was functioning and worn, but none of 510 FSs.')" }, { "date": "2021-08-18", "setting": "Multicentre epilepsy-monitoring-unit study, 152 patients (85 children), 66 convulsive seizures; detection performance only, no home alerts", "outcome": "recurred", "source": "Onorati et al. (2021) Front Neurol 12:724904, https://pmc.ncbi.nlm.nih.gov/articles/PMC8418082/ (sensitivity 0.92 pediatric, 0.94 adult; false alarm rate 1.26 and 0.57 per 24 h)" } ] } } }, "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-0009", "case_url": "records/AIBD-0009.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "train-elephant collisions before the system (national average of elephant deaths on tracks)", "url": "https://www.tribuneindia.com/news/india/ai-based-surveillance-mechanism-helps-eliminate-elephant-deaths-due-to-train-collisions-in-11-northeast-corridors-541918" }, { "label": "Before", "value": "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" }, { "label": "After", "value": "since the launch of the system, these 11 corridors have not reported any train-elephant collision" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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.", "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": "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.", "links": [ { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "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.", "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" }, "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": "saved 414 elephants in 2023 and 383 up to 16 October 2024", "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": { "paired_incident": { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "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." } } }, "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": "2025-12-05", "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/" } } }, "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-0009" } }, "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": "The collision avoided is between a train 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. 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.", "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", "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" }, "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 elephants near the track and alert loco pilots, station masters and control rooms. This benefit 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": "Lumding, 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": "Built to detect elephants near the track and alert loco pilots, station masters and control rooms. 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": "2024-10-16", "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 (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.", "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": [ "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": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "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": [ "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 (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.", "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 and wildlife protection", "segment": "Rail transport" } }, "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": "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", "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" } ] } }, "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-0009-source-0001", "AIBD-0009-source-0002", "AIBD-0009-source-0003", "AIBD-0009-source-0004", "AIBD-0009-source-0005", "AIBD-0009-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": "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", "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" } ] } }, "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": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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-0328", "case_url": "records/AIBD-0328.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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. 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[].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": 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" }, "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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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-0580", "case_url": "records/AIBD-0580.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "Lynxight detected distress and alerted the lifeguard, who had not seen the man sink. Human rescuers brought him to safety. Whether he would have survived without the alert is unknown." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0580", "title": "Lynxight alerts lifeguards to an unconscious swimmer at Inglewood Pool; he recovers fully after rescue", "summary": "Lynxight sent his location and images from the pool’s cameras to a lifeguard’s smartwatch.", "date_observed": "2025-06-10", "date_basis": "event", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "On June 10, 2025, a man suffered a medical episode and sank in the outdoor pool at Stirling Leisure - Inglewood, Perth. Lynxight sent lifeguard Cameron Menzies a smartwatch alert with the swimmer's location and images. Another swimmer, Ofer Lefelman, independently noticed the emergency and lifted the man to the surface. Menzies and fellow lifeguards brought him out of the water. The City of Stirling reports that he was conscious when paramedics took him away and later recovered fully. ABC subsequently interviewed the city's operations coordinator, who credited the alert with accelerating the lifeguard's response.", "category": "harm prevented", "unexpected": false, "purpose": "Detect swimmers in distress and alert lifeguards.", "system": "Lynxight AI pool monitoring with CCTV analysis and smartwatch alerts", "model_version": null, "developer": "Lynxight", "deployer": "City of Stirling", "location": "Stirling Leisure - Inglewood, Perth, Western Australia, Australia", "domain": "Aquatic safety and rescue", "segment": "Public swimming pools", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 5, "reason": "An unconscious swimmer survived and recovered fully after a rescue supported by an AI alert. Another swimmer also intervened independently." } ], "ai_role": { "status": "assessed", "statement": "Lynxight detected distress and alerted the lifeguard, who had not seen the man sink. Human rescuers brought him to safety. Whether he would have survived without the alert is unknown." }, "evidence": [ { "kind": "press release", "citation": "City of Stirling, July 1, 2025.", "title": "Power of anti-drowning technology on display at Stirling Leisure - Inglewood in WA’s first AI-assisted pool rescue", "url": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "date": "2025-07-01", "written_by": "deployer", "quote": "The swimmer was conscious and talking when he was taken away by paramedics and has since made a full recovery." }, { "kind": "news report", "citation": "Petroska, R. (2025). AI pool monitoring system helps save life of swimmer suffering medical episode in the water. 7NEWS, July 1, 2025.", "title": "AI pool monitoring system helps save life of swimmer suffering medical episode in the water", "url": "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "date": "2025-07-01", "written_by": "independent", "quote": "Lifeguards were sent an urgent alert at the same time fellow swimmer Ofer Lefelman noticed something was wrong." }, { "kind": "news report", "citation": "Lauren Smith and Isabel Moussalli, ABC News, December 31, 2025.", "title": "AI technology gives swimming pool lifeguards an extra set of eyes this summer", "url": "https://www.abc.net.au/news/2025-12-31/ai-system-to-help-lifeguards-in-pools-swim-safety/106172140", "date": "2025-12-31", "written_by": "independent", "quote": "our lifeguard was there pretty much a few seconds after that" }, { "kind": "report", "citation": "Lynxight. “מדיניות פרטיות לאימון המערכת ולשיפורה” [Privacy policy for training and improving the system]. Effective August 23, 2026. Company details and contact information.", "title": "מדיניות פרטיות לאימון המערכת ולשיפורה", "url": "https://www.lynxight.com/legal/privacy-policy-pools-he", "date": "2026-08-23", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Camera analysis identified distress and sent a location alert.", "A nearby swimmer and trained lifeguards performed the rescue." ], "must_hold": [ "The system must detect distress and deliver an alert that lifeguards can act on promptly." ], "echoes": [] }, "paired_incident": null, "headline": "Lifeguards and a fellow swimmer rescued an unconscious man", "metric": null, "comparator": null, "later": null, "value": null, "developer_country": "Israel", "deployer_country": "Australia", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-23", "change": "Added the case.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the PerthNow account to the original July 1, 2025 7NEWS report. Retained the City of Stirling's original account, the original interviews with the swimmer and lifeguard, and ABC's separate later interview. Preserved that a bystander noticed the emergency and pulled the swimmer up, independently of the lifeguard alert.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added Israel as the developer jurisdiction from Lynxight’s own company-address statement. Retained the policy only for corporate geography and preserved the event date, beneficiary account, system attribution, and ratings.", "source": "https://www.lynxight.com/legal/privacy-policy-pools-he", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the makers' 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-0580", "catalog_version": "1.0.0", "source_fingerprint": "c59b561f1965f0513531983bd68b819e8e40dce2523d8ea5c9a9594bc9dd78f7", "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" } ], "source_ids": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-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": "Lynxight", "deployer": "City of Stirling" } }, "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": "Lynxight detected distress and alerted the lifeguard, who had not seen the man sink. Human rescuers brought him to safety. Whether he would have survived without the alert 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": "harm prevented", "ai_role": { "status": "assessed", "statement": "Lynxight detected distress and alerted the lifeguard, who had not seen the man sink. Human rescuers brought him to safety. 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Another swimmer also intervened independently." } ] } }, "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": "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-0580" } }, "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" } }, "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 June 10, 2025, a man suffered a medical episode and sank in the outdoor pool at Stirling Leisure - Inglewood, Perth. Lynxight sent lifeguard Cameron Menzies a smartwatch alert with the swimmer's location and images. Another swimmer, Ofer Lefelman, independently noticed the emergency and lifted the man to the surface. Menzies and fellow lifeguards brought him out of the water. The City of Stirling reports that he was conscious when paramedics took him away and later recovered fully. ABC subsequently interviewed the city's operations coordinator, who credited the alert with accelerating the lifeguard's response.", "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": 5, "reason": "An unconscious swimmer survived and recovered fully after a rescue supported by an AI alert. Another swimmer also intervened independently." } ] } }, "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": "reported" } }, "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": "Detect swimmers in distress and alert lifeguards." } }, "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": "structured", "data": { "location": "Stirling Leisure - Inglewood, Perth, Western Australia, Australia", "developer_country": "Israel", "deployer_country": "Australia", "developer_jurisdictions": [ "Israel" ], "case_jurisdictions": [ "Australia" ] } }, "provenance": { "kind": "source_review", "basis": "Lynxight’s Hebrew policy, effective August 23, 2026, identifies Lynxight Ltd. at 1 HaTzmicha Street, Yokneam Illit, Israel. This establishes the company’s stated jurisdiction as of the policy date, not an independently verified historical address on the June 2025 rescue date. The existing Australian deployer and benefit location are preserved from the reviewed City of Stirling account. The policy adds no evidence about the rescue outcome.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The rescue occurred at Stirling Leisure - Inglewood in Perth, Australia. Lynxight is based in Israel, and the pool operator is Australian.", "editor_note": "" } }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Detect swimmers in distress and alert lifeguards." } }, "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": "2025-06-10", "date_basis": "event", "date_precision": "day", "revisions": [ { "date": "2026-10-23", "change": "Added the case.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the PerthNow account to the original July 1, 2025 7NEWS report. Retained the City of Stirling's original account, the original interviews with the swimmer and lifeguard, and ABC's separate later interview. Preserved that a bystander noticed the emergency and pulled the swimmer up, independently of the lifeguard alert.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added Israel as the developer jurisdiction from Lynxight’s own company-address statement. Retained the policy only for corporate geography and preserved the event date, beneficiary account, system attribution, and ratings.", "source": "https://www.lynxight.com/legal/privacy-policy-pools-he", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Added the geography review to the revision history and preserved the previously assessed event chronology and its evidence.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "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": [ "Camera analysis identified distress and sent a location alert.", "A nearby swimmer and trained lifeguards performed the rescue." ], "must_hold": [ "The system must detect distress and deliver an alert that lifeguards can act on promptly." ], "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": "2026-10-23", "change": "Added the case.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the PerthNow account to the original July 1, 2025 7NEWS report. Retained the City of Stirling's original account, the original interviews with the swimmer and lifeguard, and ABC's separate later interview. Preserved that a bystander noticed the emergency and pulled the swimmer up, independently of the lifeguard alert.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added Israel as the developer jurisdiction from Lynxight’s own company-address statement. Retained the policy only for corporate geography and preserved the event date, beneficiary account, system attribution, and ratings.", "source": "https://www.lynxight.com/legal/privacy-policy-pools-he", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Added the geography review to the revision history and preserved the previously assessed event chronology and its evidence.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Aquatic safety and rescue", "segment": "Public swimming pools" } }, "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": [ { "source_url": "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "published_on": "2025-07-01", "status": "Original interviews and published date read." }, { "source_url": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "status": "Operator article read through its indexed original page; repeated direct fetches timed out." }, { "source_url": "https://www.lynxight.com/legal/privacy-policy-pools-he", "source_ref": "AIBD-0580-source-0004", "accessed_on": "2026-10-25", "language": "Hebrew", "status": "Original developer webpage inspected for corporate geography.", "locator": "Company details and contact information: Lynxight Ltd., 1 HaTzmicha Street, Yokneam Illit, Israel. Effective date at the beginning of the page.", "effective_date": "2026-08-23" } ], "limitations": [ "The swimmer's clinical records and raw detection/alert timing are unavailable.", "The sources do not establish what would have happened without the alert. The bystander was already responding." ] } }, "provenance": { "kind": "source_review", "basis": "Inspected the additional corporate-geography source and retained prior access and identity limitations.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The pool operator and subsequent reporting describe the rescue. Clinical records and raw alert timings are unavailable. A bystander had already begun responding, so the alert’s effect on the outcome cannot be isolated.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "City of Stirling, July 1, 2025.", "title": "Power of anti-drowning technology on display at Stirling Leisure - Inglewood in WA’s first AI-assisted pool rescue", "url": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "date": "2025-07-01", "written_by": "deployer", "quote": "The swimmer was conscious and talking when he was taken away by paramedics and has since made a full recovery." }, { "kind": "news report", "citation": "Petroska, R. (2025). AI pool monitoring system helps save life of swimmer suffering medical episode in the water. 7NEWS, July 1, 2025.", "title": "AI pool monitoring system helps save life of swimmer suffering medical episode in the water", "url": "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "date": "2025-07-01", "written_by": "independent", "quote": "Lifeguards were sent an urgent alert at the same time fellow swimmer Ofer Lefelman noticed something was wrong." }, { "kind": "news report", "citation": "Lauren Smith and Isabel Moussalli, ABC News, December 31, 2025.", "title": "AI technology gives swimming pool lifeguards an extra set of eyes this summer", "url": "https://www.abc.net.au/news/2025-12-31/ai-system-to-help-lifeguards-in-pools-swim-safety/106172140", "date": "2025-12-31", "written_by": "independent", "quote": "our lifeguard was there pretty much a few seconds after that" }, { "kind": "report", "citation": "Lynxight. “מדיניות פרטיות לאימון המערכת ולשיפורה” [Privacy policy for training and improving the system]. Effective August 23, 2026. Company details and contact information.", "title": "מדיניות פרטיות לאימון המערכת ולשיפורה", "url": "https://www.lynxight.com/legal/privacy-policy-pools-he", "date": "2026-08-23", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Appended an original corporate-geography source while preserving the existing evidence, ordering, and clinical or system-identity qualifications.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "role": "Retained evidence sources in reviewed order" }, "provenance": { "kind": "source_review", "basis": "Preserved all existing source identifiers and appended the primary corporate-geography source.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "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": "City of Stirling, July 1, 2025.", "title": "Power of anti-drowning technology on display at Stirling Leisure - Inglewood in WA’s first AI-assisted pool rescue", "url": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "date": "2025-07-01", "written_by": "deployer", "quote": "The swimmer was conscious and talking when he was taken away by paramedics and has since made a full recovery." }, { "kind": "news report", "citation": "Petroska, R. (2025). AI pool monitoring system helps save life of swimmer suffering medical episode in the water. 7NEWS, July 1, 2025.", "title": "AI pool monitoring system helps save life of swimmer suffering medical episode in the water", "url": "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "date": "2025-07-01", "written_by": "independent", "quote": "Lifeguards were sent an urgent alert at the same time fellow swimmer Ofer Lefelman noticed something was wrong." }, { "kind": "news report", "citation": "Lauren Smith and Isabel Moussalli, ABC News, December 31, 2025.", "title": "AI technology gives swimming pool lifeguards an extra set of eyes this summer", "url": "https://www.abc.net.au/news/2025-12-31/ai-system-to-help-lifeguards-in-pools-swim-safety/106172140", "date": "2025-12-31", "written_by": "independent", "quote": "our lifeguard was there pretty much a few seconds after that" }, { "kind": "report", "citation": "Lynxight. “מדיניות פרטיות לאימון המערכת ולשיפורה” [Privacy policy for training and improving the system]. Effective August 23, 2026. Company details and contact information.", "title": "מדיניות פרטיות לאימון המערכת ולשיפורה", "url": "https://www.lynxight.com/legal/privacy-policy-pools-he", "date": "2026-08-23", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Appended an original corporate-geography source while preserving the existing evidence, ordering, and clinical or system-identity qualifications.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0580-source-0001", "retained_source_order": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "origins": [ { "url": "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "contribution": "Original operator account dates the incident to June 10 and reports recovery.", "locator": "Incident description, Cameron Menzies's account, and recovery statement.", "relationship": "Deployer report. No raw alert log or medical record is supplied.", "source_refs": [ "AIBD-0580-source-0001" ], "display_name": "City of Stirling" }, { "url": "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "contribution": "Direct interviews add the bystander's actions and the lifeguard's cautious description of the alert.", "locator": "Ofer Lefelman and Cameron interviews in the report.", "relationship": "Original reporting; same rescue, not a clinical counterfactual.", "source_refs": [ "AIBD-0580-source-0002" ] }, { "url": "https://www.abc.net.au/news/2025-12-31/ai-system-to-help-lifeguards-in-pools-swim-safety/106172140", "contribution": "Separate interview with the city's leisure manager describes the sequence of bystander and lifeguard arrival.", "locator": "Duncan Hutton interview about the Inglewood rescue.", "relationship": "Original later reporting based on operator testimony; no independent clinical verification.", "source_refs": [ "AIBD-0580-source-0003" ] }, { "url": "https://www.lynxight.com/legal/privacy-policy-pools-he", "contribution": "Establishes the developer’s company address in Israel. Retained for corporate geography only.", "locator": "Company details and contact information: Lynxight Ltd., 1 HaTzmicha Street, Yokneam Illit, Israel. Effective date at the beginning of the page.", "relationship": "Original developer policy in Hebrew. It supplies no new evidence about the swimmer’s rescue or recovery.", "source_refs": [ "AIBD-0580-source-0004" ], "display_name": "Lynxight" } ], "claim_chains": [ { "claim": "Alert, rescue and recovery", "path": [ "https://www.stirling.wa.gov.au/city-and-council/news/power-of-anti-drowning-technology-on-display-at-stirling-leisure-%E2%80%93-inglewood-in-wa%E2%80%99s-first-ai-assist", "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045", "https://www.abc.net.au/news/2025-12-31/ai-system-to-help-lifeguards-in-pools-swim-safety/106172140" ], "finding": "The operator is the original source for the June 10 date and full recovery. The two original reports add interviews and the crucial human-rescuer qualification." }, { "claim": "Secondary-source origin", "path": [ "https://www.perthnow.com.au/wa/perth/north/stirling/ai-pool-monitoring-system-in-stirling-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19213706", "https://7news.com.au/news/ai-pool-monitoring-system-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19203045" ], "finding": "PerthNow repeats 7NEWS interviews. The original 7NEWS report replaces it rather than being counted alongside it." }, { "claim": "Developer jurisdiction and geographic scope", "path": [ "https://www.lynxight.com/legal/privacy-policy-pools-he" ], "finding": "Lynxight’s Hebrew policy, effective August 23, 2026, identifies Lynxight Ltd. at 1 HaTzmicha Street, Yokneam Illit, Israel. This establishes the company’s stated jurisdiction as of the policy date, not an independently verified historical address on the June 2025 rescue date. The existing Australian deployer and benefit location are preserved from the reviewed City of Stirling account. The policy adds no evidence about the rescue outcome." } ], "limitations": [ "The swimmer's clinical records and raw detection/alert timing are unavailable.", "The sources do not establish what would have happened without the alert. The bystander was already responding." ], "excluded_or_consolidated_sources": [ { "url": "https://www.perthnow.com.au/wa/perth/north/stirling/ai-pool-monitoring-system-in-stirling-helps-save-life-of-swimmer-suffering-medical-episode-in-the-water-c-19213706", "reason": "Republication of the 7NEWS report, with no additional evidence for this case." } ], "review_scope": "Traced the claims in this case through accessible original publications and relevant embedded references, tables, appendices, corrections, and supplements. The stated access limits remain unresolved. Additional review on October 25, 2026, establishes developer jurisdiction and clarifies geographic scope; prior clinical and attribution limitations remain unchanged." } }, "provenance": { "kind": "source_review", "basis": "Added corporate geography and its original source without repeating or replacing the prior clinical and system-identity assessments.", "record_fields": [ "evidence", "developer_country", "location" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003", "AIBD-0580-source-0004" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The City of Stirling reports the rescue. Separate 7NEWS and ABC interviews add accounts from a bystander, lifeguard, and leisure manager. Lynxight’s policy establishes its corporate address, not the rescue outcome.", "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": "Lynxight sent his location and images from the pool’s cameras to a lifeguard’s smartwatch.", "account": "On June 10, 2025, a man suffered a medical episode and sank in the outdoor pool at Stirling Leisure - Inglewood, Perth. Lynxight sent lifeguard Cameron Menzies a smartwatch alert with the swimmer's location and images. Another swimmer, Ofer Lefelman, independently noticed the emergency and lifted the man to the surface. Menzies and fellow lifeguards brought him out of the water. The City of Stirling reports that he was conscious when paramedics took him away and later recovered fully. ABC subsequently interviewed the city's operations coordinator, who credited the alert with accelerating the lifeguard's 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": "value_present", "value": { "kind": "structured", "data": { "inputs": "Images from the pool’s existing CCTV cameras.", "outputs": "A smartwatch alert with the swimmer’s location and camera images.", "human_action": "Lifeguards and a fellow swimmer rescued the swimmer.", "unreported_details": [ "Underlying model and release", "Installed software version", "Specific camera model" ] } }, "provenance": { "kind": "source_review", "basis": "Recorded the deployment components that the original case accounts describe.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Lynxight analyzed images from the pool’s existing cameras and sent a smartwatch alert with the swimmer’s location and camera images.", "editor_note": "" } }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "The original pool-operator account and subsequent reporting identify Lynxight, but do not name the underlying model, release, camera model, or installed software version.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003" ], "reviewed_on": "2026-10-24" }, "reason": "The original pool-operator account and subsequent reporting identify Lynxight, but do not name the underlying model, release, camera model, or installed software version.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Lynxight’s detector for unusual swimmer behavior. The operator’s account gives no separate model name.", "editor_note": "" } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "The original pool-operator account and subsequent reporting identify Lynxight, but do not name the underlying model, release, camera model, or installed software version.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003" ], "reviewed_on": "2026-10-24" }, "reason": "The original pool-operator account and subsequent reporting identify Lynxight, but do not name the underlying model, release, camera model, or installed software version.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The installed model and software releases are not disclosed.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Lynxight AI pool monitoring with CCTV analysis and smartwatch 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": "Lynxight", "underlying_model_provider": "No separate model provider is identified." } }, "provenance": { "kind": "source_review", "basis": "The original operator or research-institution report identifies the application developer.", "record_fields": [ "developer", "system" ], "source_refs": [ "AIBD-0580-source-0001", "AIBD-0580-source-0002", "AIBD-0580-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Lynxight alerts lifeguards to an unconscious swimmer at Inglewood Pool; he recovers fully after rescue", "headline": "Lifeguards and a fellow swimmer rescued an unconscious man" } }, "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": [ "c59b561f1965f0513531983bd68b819e8e40dce2523d8ea5c9a9594bc9dd78f7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed the fingerprint after the system-identity and model-version review.", "record_fields": [ "revisions" ], "source_refs": [], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On June 10, 2025, a man suffered a medical episode and sank in the outdoor pool at Stirling Leisure - Inglewood, Perth. Lynxight sent lifeguard Cameron Menzies a smartwatch alert with the swimmer's location and images. Another swimmer, Ofer Lefelman, independently noticed the emergency and lifted the man to the surface. Menzies and fellow lifeguards brought him out of the water. The City of Stirling reports that he was conscious when paramedics took him away and later recovered fully. ABC subsequently interviewed the city's operations coordinator, who credited the alert with accelerating the lifeguard's response.", "replication": { "produced_by": [ "Camera analysis identified distress and sent a location alert.", "A nearby swimmer and trained lifeguards performed the rescue." ], "must_hold": [ "The system must detect distress and deliver an alert that lifeguards can act on promptly." ], "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": 13, "record_mapped": 21, "not_assessed": 57, "complete": false, "by_state": { "value_present": 32, "not_reported": 2, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0582", "case_url": "records/AIBD-0582.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "AI flagged the bleeding and notified the stroke team, according to her treating surgeon. Clinicians treated the aneurysm. Viz.ai is an inferred identification from his separate interview. The sources do not isolate the software’s effect on survival." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0582", "title": "AI flags Julie Knichel's brain hemorrhage before aneurysm treatment and her return to nursing", "summary": "Julie Knichel’s doctors received an alert after software, apparently Viz.ai, detected her brain bleed.", "date_observed": "2024-02-02", "date_basis": "event", "date_precision": "day", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "Julie Knichel, a 57-year-old nurse, suffered a ruptured brain aneurysm. At Baptist Health's Marcus Neuroscience Institute, AI reviewed her scan and alerted clinicians to the bleeding. They sealed the aneurysm with coils delivered through a catheter. After about three weeks in hospital, she recovered and returned to full-time nursing. Her symptoms began on 2 February 2024. Baptist Health gives the day and month, and Jan Engoren’s subsequent interviews with Knichel and Snelling establish the year. The surgeon names Viz.ai in a later interview and describes an unnamed ICU nurse whose circumstances match Knichel’s. The product attribution is therefore inferred. Neither account identifies the module or release.", "category": "harm prevented", "unexpected": false, "purpose": "Detect brain hemorrhage and alert clinicians.", "system": "Viz.ai stroke detection and care coordination (attribution inferred)", "model_version": null, "developer": "Viz.ai (inferred)", "deployer": "Baptist Health, Marcus Neuroscience Institute", "location": "Boca Raton, Florida, United States", "domain": "Stroke care", "segment": "Emergency patients with brain hemorrhage", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 5, "reason": "Knichel survived a life-threatening hemorrhage and resumed her work and activities." } ], "ai_role": { "status": "assessed", "statement": "AI flagged the bleeding and notified the stroke team, according to her treating surgeon. Clinicians treated the aneurysm. Viz.ai is an inferred identification from his separate interview. The sources do not isolate the software’s effect on survival." }, "evidence": [ { "kind": "report", "citation": "Adrienne Sylver, Baptist Health, November 25, 2024.", "title": "Stroke Nurse Shares Her Experience After Brain Aneurysm", "url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "date": "2024-11-25", "written_by": "deployer", "quote": "Ms. Knichel is happy to have returned to work full time and to her previous activities" }, { "kind": "news report", "citation": "Brian Snelling and Felipe De Los Rios. “Incorporating AI Into Stroke Triage,” NeurologyLive, October 22, 2025. Original video, 2:29–2:36 and 5:03–5:36; edited transcript, Snelling’s responses.", "title": "Incorporating AI Into Stroke Triage", "url": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "date": "2025-10-22", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "Jan Engoren. “Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke,” The Coastal Star, April 29, 2025. Paragraphs describing February 2024 treatment, subarachnoid hemorrhage, and recovery.", "title": "Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke", "url": "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover", "date": "2025-04-29", "written_by": "independent", "quote": null }, { "kind": "news report", "citation": "Kara Duffy. “From caregiver to cared for: How an ICU nurse’s training saved her own life.” CBS12/WPEC, October 29, 2024.", "title": "From caregiver to cared for: How an ICU nurse’s training saved her own life", "url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "date": "2024-10-29", "written_by": "independent", "quote": null }, { "kind": "report", "citation": "Viz.ai. “Viz.ai Trust Center Privacy Notice.” Section 14, “Viz.ai Head Office.” Undated.", "title": "Viz.ai Trust Center Privacy Notice", "url": "https://www.viz.ai/trust-center-privacy-notice", "date": null, "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "AI-assisted imaging and specialist treatment." ], "must_hold": [ "Prompt clinical review and access to aneurysm treatment." ], "echoes": [] }, "paired_incident": null, "headline": "Doctors found a nurse’s brain bleed with help from AI", "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": "2026-10-23", "change": "Added the case.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Read Baptist Health's original patient and clinician account, including the surrounding video reference and stroke background. Kept one institutional source and preserved the absence of a named system, measured time saving or patient-specific comparison.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Placed the beneficiary’s personal name in the subtitle and kept the display headline focused on the reported benefit.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Marked Viz.ai as inferred in the system and developer fields and qualified the AI-role statement. Added earlier original CBS12 interviews, preserved the clinical testimony, and recorded the absence of a confirmed module or release.", "source": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the homepage subtitle for concise database prose, with courtesy titles for named beneficiaries and numbered clauses for the Coates drug-selection account. Preserved the reported evidence and material uncertainty.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the editorial parenthetical and unconfirmed product name from the subtitle. Preserved the inferred Viz.ai identification and its supporting evidence in the case account, system assessment, and source notes.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Restored Viz.ai in the subtitle with an explicit qualification and numbered the two reported clauses. Preserved the inferred system identification and the existing source assessments.", "source": null, "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the subtitle to the alert and detection sequence while retaining the qualified Viz.ai identification.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added the United States as the jurisdiction of the inferred developer, Viz.ai, using its own headquarters statement. Preserved the inferred product attribution and existing clinical chronology, account, and ratings. The corporate notice is not evidence that Viz.ai was used for this patient.", "source": "https://www.viz.ai/trust-center-privacy-notice", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the makers' report, the deployer's report 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-0582", "catalog_version": "1.0.0", "source_fingerprint": "2e3e3df9046689dd81d56e536fd692f7e4e834745f330a46e124736af5bf6e5f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-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": "Viz.ai (inferred)", "deployer": "Baptist Health, Marcus Neuroscience Institute" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "deployer", "developer" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Baptist Health’s Marcus Neuroscience Institute treated Knichel. Viz.ai probably supplied the detection software.", "editor_note": "" } }, "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": "AI flagged the bleeding and notified the stroke team, according to her treating surgeon. Clinicians treated the aneurysm. Viz.ai is an inferred identification from his separate interview. The sources do not isolate the software’s effect on survival." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "ai_role" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "The surgeon reported that software detected the bleeding and alerted the stroke team. Clinicians treated the aneurysm. The sources do not separately measure the software’s effect on survival.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "AI flagged the bleeding and notified the stroke team, according to her treating surgeon. Clinicians treated the aneurysm. Viz.ai is an inferred identification from his separate interview. The sources do not isolate the software’s effect on survival." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "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": 5, "reason": "Knichel survived a life-threatening hemorrhage and resumed her work and activities." } ] } }, "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": "Baptist Health, CBS12/WPEC, The Coastal Star, and NeurologyLive provide patient or treating-clinician accounts. No independent study measures the AI contribution, and no finding by a regulator or court is cited.", "interpretation": "Independent interviews corroborate the patient outcome and event year. The product attribution follows the treating clinician’s account and is not independently verified.", "system_identity_review": "Additional sources clarify technical identity or chronology. They do not establish the AI contribution independently of the original investigators or witnesses." } }, "provenance": { "kind": "source_review", "basis": "Recomputed the existing display rule after adding the reviewed sources. Source independence remains claim-specific.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 5, "reason": "Knichel survived a life-threatening hemorrhage and resumed her work and activities." } ] } }, "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 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"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": "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-0582" } }, "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" } }, "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": "Julie Knichel, a 57-year-old nurse, suffered a ruptured brain aneurysm. At Baptist Health's Marcus Neuroscience Institute, AI reviewed her scan and alerted clinicians to the bleeding. They sealed the aneurysm with coils delivered through a catheter. After about three weeks in hospital, she recovered and returned to full-time nursing. Her symptoms began on 2 February 2024. Baptist Health gives the day and month, and Jan Engoren’s subsequent interviews with Knichel and Snelling establish the year. The surgeon names Viz.ai in a later interview and describes an unnamed ICU nurse whose circumstances match Knichel’s. The product attribution is therefore inferred. Neither account identifies the module or release.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "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": "personal", "depth": 5, "reason": "Knichel survived a life-threatening hemorrhage and resumed her work and activities." } ] } }, "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": "reported" } }, "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": "Detect brain hemorrhage and alert clinicians." } }, "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": "structured", "data": { "location": "Boca Raton, Florida, United States", "developer_country": "United States", "deployer_country": "United States", "developer_jurisdictions": [ "United States" ], "case_jurisdictions": [ "United States" ], "developer_jurisdiction_inferred": true, "developer_note": "Viz.ai is the inferred developer. Its U.S. headquarters is documented, but its involvement in this patient’s care remains inferred." } }, "provenance": { "kind": "source_review", "basis": "Section 14 of Viz.ai’s own Trust Center Privacy Notice explicitly identifies U.S. headquarters and a San Francisco address. The system and developer attribution to Knichel remain inferred, so the developer jurisdiction is conditional on that attribution. The notice supplies no patient-specific evidence and has no stated publication date. The existing U.S. hospital and benefit location are preserved from the reviewed hospital account.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Knichel received care in Boca Raton, Florida, United States. Both Baptist Health and the inferred provider, Viz.ai, are based in the United States.", "editor_note": "" } }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Detect brain hemorrhage and alert clinicians." } }, "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": "2024-02-02", "date_basis": "event", "date_precision": "day", "revisions": [ { "date": "2026-10-23", "change": "Added the case.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Read Baptist Health's original patient and clinician account, including the surrounding video reference and stroke background. Kept one institutional source and preserved the absence of a named system, measured time saving or patient-specific comparison.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Placed the beneficiary’s personal name in the subtitle and kept the display headline focused on the reported benefit.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Marked Viz.ai as inferred in the system and developer fields and qualified the AI-role statement. Added earlier original CBS12 interviews, preserved the clinical testimony, and recorded the absence of a confirmed module or release.", "source": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the homepage subtitle for concise database prose, with courtesy titles for named beneficiaries and numbered clauses for the Coates drug-selection account. Preserved the reported evidence and material uncertainty.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the editorial parenthetical and unconfirmed product name from the subtitle. Preserved the inferred Viz.ai identification and its supporting evidence in the case account, system assessment, and source notes.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Restored Viz.ai in the subtitle with an explicit qualification and numbered the two reported clauses. Preserved the inferred system identification and the existing source assessments.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the subtitle to the alert and detection sequence while retaining the qualified Viz.ai identification.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added the United States as the jurisdiction of the inferred developer, Viz.ai, using its own headquarters statement. Preserved the inferred product attribution and existing clinical chronology, account, and ratings. The corporate notice is not evidence that Viz.ai was used for this patient.", "source": "https://www.viz.ai/trust-center-privacy-notice", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Added the geography review to the revision history and preserved the previously assessed event chronology and its evidence.", "record_fields": [ "date_observed", "date_basis", "date_precision", "revisions" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "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": [ "AI-assisted imaging and specialist treatment." ], "must_hold": [ "Prompt clinical review and access to aneurysm treatment." ], "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": "2026-10-23", "change": "Added the case.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Read Baptist Health's original patient and clinician account, including the surrounding video reference and stroke background. Kept one institutional source and preserved the absence of a named system, measured time saving or patient-specific comparison.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Placed the beneficiary’s personal name in the subtitle and kept the display headline focused on the reported benefit.", "source": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Marked Viz.ai as inferred in the system and developer fields and qualified the AI-role statement. Added earlier original CBS12 interviews, preserved the clinical testimony, and recorded the absence of a confirmed module or release.", "source": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the homepage subtitle for concise database prose, with courtesy titles for named beneficiaries and numbered clauses for the Coates drug-selection account. Preserved the reported evidence and material uncertainty.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Removed the editorial parenthetical and unconfirmed product name from the subtitle. Preserved the inferred Viz.ai identification and its supporting evidence in the case account, system assessment, and source notes.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Restored Viz.ai in the subtitle with an explicit qualification and numbered the two reported clauses. Preserved the inferred system identification and the existing source assessments.", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the subtitle to the alert and detection sequence while retaining the qualified Viz.ai identification.", "editor": "AIBD" }, { "date": "2026-10-25", "change": "Added the United States as the jurisdiction of the inferred developer, Viz.ai, using its own headquarters statement. Preserved the inferred product attribution and existing clinical chronology, account, and ratings. The corporate notice is not evidence that Viz.ai was used for this patient.", "source": "https://www.viz.ai/trust-center-privacy-notice", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Added the geography review to the revision history and preserved the previously assessed event chronology and its evidence.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Stroke care", "segment": "Emergency patients with brain hemorrhage" } }, "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": [ { "source_url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "published_on": "2024-11-25", "status": "Complete article and outgoing links inspected." }, { "source_url": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "status": "Original source inspected.", "locator": "Original video 2:29–2:36, 5:03–5:36. The edited transcript omits the pointer to the earlier press release.", "reviewed_on": "2026-10-24" }, { "source_url": "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover", "status": "Original source inspected.", "locator": "Paragraph beginning “Knichel came under Snelling’s care in February 2024”; treatment and recovery paragraphs.", "reviewed_on": "2026-10-24" }, { "source_url": "https://players.brightcove.net/10203567001/wf0hzDVjW_default/index.html?videoId=6383528276112", "source_ref": "AIBD-0582-source-0002", "status": "Original interview captions inspected; same work as the edited transcript.", "locator": "2:29–2:36 and 5:03–5:36." }, { "source_url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "source_ref": "AIBD-0582-source-0001", "status": "Embedded hospital-video transcript and frames inspected. No product brand appears in the AI passage.", "locator": "1:42–2:19." }, { "source_url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "source_ref": "AIBD-0582-source-0004", "publication_date": "2024-10-29", "accessed_on": "2026-10-24", "status": "Original publisher text inspected.", "locator": "Opening patient account, Snelling’s first response, and the video caption." }, { "source_ref": "AIBD-0582-source-0001", "source_url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "access_url": "https://physicianresources.baptisthealth.net/videos/ai-saves-the-day", "captions_url": "https://s7d9.scene7.com/is/content/bhsf/Transcript-English-AI%20Saves%20the%20Day", "accessed_on": "2026-10-24", "locator": "1:42–2:19", "status": "Original hospital video and publisher-supplied captions inspected. Snelling describes the AI alert in Julie’s case without naming the product. This is the same video as the embedded hospital article, not another source." }, { "source_url": "https://www.viz.ai/trust-center-privacy-notice", "source_ref": "AIBD-0582-source-0005", "accessed_on": "2026-10-25", "language": "English", "status": "Original developer webpage inspected for corporate geography.", "locator": "Section 14, “Viz.ai Head Office”: U.S. headquarters and San Francisco address." } ], "limitations": [ "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred.", "No reviewed source identifies the exact Viz.ai module or model version, or supplies a patient-specific measured comparison of AI against manual review." ] } }, "provenance": { "kind": "source_review", "basis": "Inspected the additional corporate-geography source and retained prior access and identity limitations.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital’s patient story and the surgeon’s interviews describe detection and treatment. They provide no scan-specific processing record or comparison of the software with manual review.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "Adrienne Sylver, Baptist Health, November 25, 2024.", "title": "Stroke Nurse Shares Her Experience After Brain Aneurysm", "url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "date": "2024-11-25", "written_by": "deployer", "quote": "Ms. Knichel is happy to have returned to work full time and to her previous activities" }, { "kind": "news report", "citation": "Brian Snelling and Felipe De Los Rios. “Incorporating AI Into Stroke Triage,” NeurologyLive, October 22, 2025. Original video, 2:29–2:36 and 5:03–5:36; edited transcript, Snelling’s responses.", "title": "Incorporating AI Into Stroke Triage", "url": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "date": "2025-10-22", "written_by": "deployer" }, { "kind": "news report", "citation": "Jan Engoren. “Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke,” The Coastal Star, April 29, 2025. Paragraphs describing February 2024 treatment, subarachnoid hemorrhage, and recovery.", "title": "Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke", "url": "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover", "date": "2025-04-29", "written_by": "independent" }, { "kind": "news report", "citation": "Kara Duffy. “From caregiver to cared for: How an ICU nurse’s training saved her own life.” CBS12/WPEC, October 29, 2024.", "title": "From caregiver to cared for: How an ICU nurse’s training saved her own life", "url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "date": "2024-10-29", "written_by": "independent" }, { "kind": "report", "citation": "Viz.ai. “Viz.ai Trust Center Privacy Notice.” Section 14, “Viz.ai Head Office.” Undated.", "title": "Viz.ai Trust Center Privacy Notice", "url": "https://www.viz.ai/trust-center-privacy-notice", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Appended an original corporate-geography source while preserving the existing evidence, ordering, and clinical or system-identity qualifications.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "role": "Retained evidence sources in reviewed order" }, "provenance": { "kind": "source_review", "basis": "Preserved all existing source identifiers and appended the primary corporate-geography source.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "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": "report", "citation": "Adrienne Sylver, Baptist Health, November 25, 2024.", "title": "Stroke Nurse Shares Her Experience After Brain Aneurysm", "url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "date": "2024-11-25", "written_by": "deployer", "quote": "Ms. Knichel is happy to have returned to work full time and to her previous activities" }, { "kind": "news report", "citation": "Brian Snelling and Felipe De Los Rios. “Incorporating AI Into Stroke Triage,” NeurologyLive, October 22, 2025. Original video, 2:29–2:36 and 5:03–5:36; edited transcript, Snelling’s responses.", "title": "Incorporating AI Into Stroke Triage", "url": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "date": "2025-10-22", "written_by": "deployer" }, { "kind": "news report", "citation": "Jan Engoren. “Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke,” The Coastal Star, April 29, 2025. Paragraphs describing February 2024 treatment, subarachnoid hemorrhage, and recovery.", "title": "Health & Harmony: Fast action, expert surgeon empower nurse’s recovery from stroke", "url": "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover", "date": "2025-04-29", "written_by": "independent" }, { "kind": "news report", "citation": "Kara Duffy. “From caregiver to cared for: How an ICU nurse’s training saved her own life.” CBS12/WPEC, October 29, 2024.", "title": "From caregiver to cared for: How an ICU nurse’s training saved her own life", "url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "date": "2024-10-29", "written_by": "independent" }, { "kind": "report", "citation": "Viz.ai. “Viz.ai Trust Center Privacy Notice.” Section 14, “Viz.ai Head Office.” Undated.", "title": "Viz.ai Trust Center Privacy Notice", "url": "https://www.viz.ai/trust-center-privacy-notice", "written_by": "makers" } ] } }, "provenance": { "kind": "source_review", "basis": "Appended an original corporate-geography source while preserving the existing evidence, ordering, and clinical or system-identity qualifications.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial" }, "sources[].upstream_sources": { "state": "value_present", "value": { "kind": "structured", "data": { "reviewed_on": "2026-10-25", "lead_source_ref": "AIBD-0582-source-0001", "retained_source_order": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "origins": [ { "url": "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "contribution": "Original patient and treating-team interviews describe the brain bleed, scan review, coiling, three-week admission and return to work.", "locator": "Unique stroke services; Colleagues who care.", "relationship": "Treating institution's account. The accompanying video is part of the same publication, not corroboration.", "source_refs": [ "AIBD-0582-source-0001" ], "display_name": "Baptist Health" }, { "url": "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "contribution": "Snelling names Viz.ai as a hospital system and later recounts an unnamed nurse’s case that matches Knichel’s circumstances.", "locator": "Original video 2:29–2:36, 5:03–5:36. The edited transcript omits the pointer to the earlier press release.", "relationship": "Original clinician testimony. The product attribution and matching of the unnamed patient remain inferences; the video is less explicit than the edited transcript.", "source_refs": [ "AIBD-0582-source-0002" ], "display_name": "NeurologyLive", "access_url": "https://players.brightcove.net/10203567001/wf0hzDVjW_default/index.html?videoId=6383528276112" }, { "url": "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover", "contribution": "Patient and clinician interviews establish February 2024, subarachnoid hemorrhage, treatment, and return to work.", "locator": "Paragraph beginning “Knichel came under Snelling’s care in February 2024”; treatment and recovery paragraphs.", "relationship": "Original reporting corroborates the patient outcome and chronology, not the AI product or its separate effect.", "source_refs": [ "AIBD-0582-source-0003" ], "display_name": "The Coastal Star" }, { "url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "source_refs": [ "AIBD-0582-source-0004" ], "locator": "Opening patient account, Snelling’s first response, and the video caption.", "display_name": "CBS12/WPEC", "contribution": "Earlier original interviews name Knichel, date the episode to February 2024, and quote Snelling’s account of AI detecting the bleed before clinicians.", "relationship": "Separate reporting from the same patient and treating surgeon. It establishes an earlier public account but supplies no brand identification, clinical record, or independent measurement of the AI contribution." }, { "url": "https://www.viz.ai/trust-center-privacy-notice", "contribution": "Establishes Viz.ai’s U.S. headquarters. The attribution of Viz.ai to Knichel remains inferred.", "locator": "Section 14, “Viz.ai Head Office”: U.S. headquarters and San Francisco address.", "relationship": "Original developer statement about corporate geography. It does not identify the software used for this patient or independently corroborate her clinical outcome.", "source_refs": [ "AIBD-0582-source-0005" ], "display_name": "Viz.ai" } ], "claim_chains": [ { "claim": "AI-assisted care and recovery", "path": [ "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life" ], "finding": "The hospital account and an earlier CBS12 interview directly report AI assistance through the treating surgeon. Neither supplies a scan record, model evaluation, or software brand." }, { "claim": "System identity", "path": [ "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover" ], "finding": "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred." }, { "claim": "System attribution", "path": [ "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "https://www.neurologylive.com/view/incorporating-ai-into-stroke-triage", "https://players.brightcove.net/10203567001/wf0hzDVjW_default/index.html?videoId=6383528276112" ], "finding": "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred." }, { "claim": "Event chronology", "path": [ "https://baptisthealth.net/baptist-health-news/stroke-nurse-shares-her-experience-after-brain-aneurysm", "https://thecoastalstar.com/profiles/blogs/fast-action-expert-surgeon-empowers-nurse-to-recover" ], "finding": "The hospital gives February 2. The subsequent patient and clinician interviews establish February 2024, supporting 2 February 2024." }, { "claim": "Developer jurisdiction and geographic scope", "path": [ "https://www.viz.ai/trust-center-privacy-notice" ], "finding": "Section 14 of Viz.ai’s own Trust Center Privacy Notice explicitly identifies U.S. headquarters and a San Francisco address. The system and developer attribution to Knichel remain inferred, so the developer jurisdiction is conditional on that attribution. The notice supplies no patient-specific evidence and has no stated publication date. The existing U.S. hospital and benefit location are preserved from the reviewed hospital account." } ], "limitations": [ "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred.", "No reviewed source identifies the exact Viz.ai module or model version, or supplies a patient-specific measured comparison of AI against manual review." ], "excluded_or_consolidated_sources": [], "review_scope": "Reviewed the earlier CBS12 report, original hospital video and captions, NeurologyLive video, and additional patient interviews. AI involvement is directly reported by the treating surgeon; the product, module, and release remain unconfirmed for this patient. Additional review on October 25, 2026, establishes developer jurisdiction and clarifies geographic scope; prior clinical and attribution limitations remain unchanged.", "homepage_source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002" ], "earlier_reporting": [ { "url": "https://cbs12.com/news/local/from-caregiver-to-cared-for-how-an-icu-nurses-training-saved-her-own-life", "publication_date": "2024-10-29", "finding": "Original named-patient and surgeon interviews precede the retained hospital article." } ], "additional_interviews_checked": [ { "url": "https://www.theeducatedpatient.com/view/hear-my-journey-from-neuro-icu-nurse-to-brain-aneurysm-survivor", "title": "Hear My Journey: From Neuro ICU Nurse to Brain Aneurysm Survivor", "author": "Lana Pine", "publication_date": "2025-08-06", "format_inspected": "Complete original interview audio, approximately 9 minutes, reviewed through a local automated transcription; the webpage is an edited account.", "finding": "The interview describes the hemorrhage, treatment, hospitalization, and return to work but does not discuss AI. It does not resolve the system identity.", "relationship": "Additional testimony from the same patient and clinician; retained as a checked source note, not a separate confirmation." }, { "url": "https://www.wpbf.com/article/on-the-record-knowing-the-signs-of-a-stroke/71263276", "title": "On the Record: Knowing the signs of a stroke", "author": "Malcolm Shields", "publication_date": "2026-05-10", "format_inspected": "Complete original interview audio, approximately 20 minutes, reviewed through a local automated transcription. No publisher transcript was available.", "locators": [ "1:04–1:10: Knichel dates the episode to February 2, 2024", "5:15–6:02: Snelling describes the AI alert", "8:33–8:59: return to work" ], "finding": "The patient and treating surgeon confirm the reported course and AI alert. No product, module, or release is named.", "relationship": "Follow-up testimony from the same witnesses; retained as a checked source note, not an independent evaluation." } ] } }, "provenance": { "kind": "source_review", "basis": "Added corporate geography and its original source without repeating or replacing the prior clinical and system-identity assessments.", "record_fields": [ "evidence", "developer_country", "location" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004", "AIBD-0582-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Baptist Health, CBS12, and The Coastal Star interview Knichel and her surgeon. NeurologyLive separately interviews the surgeon about Viz.ai. These accounts support the treatment history but do not independently measure the software’s contribution.", "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": "Julie Knichel’s doctors received an alert after software, apparently Viz.ai, detected her brain bleed.", "account": "Julie Knichel, a 57-year-old nurse, suffered a ruptured brain aneurysm. At Baptist Health's Marcus Neuroscience Institute, AI reviewed her scan and alerted clinicians to the bleeding. They sealed the aneurysm with coils delivered through a catheter. After about three weeks in hospital, she recovered and returned to full-time nursing. Her symptoms began on 2 February 2024. Baptist Health gives the day and month, and Jan Engoren’s subsequent interviews with Knichel and Snelling establish the year. The surgeon names Viz.ai in a later interview and describes an unnamed ICU nurse whose circumstances match Knichel’s. The product attribution is therefore inferred. Neither account identifies the module or release." } }, "provenance": { "kind": "curatorial", "basis": "Updated the displayed wording and revision history without changing the reviewed facts, sources, or assessments.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "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": "value_present", "value": { "kind": "structured", "data": { "clinical_setting": "Marcus Neuroscience Institute, Baptist Health, Boca Raton.", "reported_workflow": "AI reviewed brain imaging and alerted clinicians before their own review, according to the treating surgeon.", "unreported_details": [ "Confirmed product and module for this patient", "Model and software release", "Scan-specific processing record" ] } }, "provenance": { "kind": "source_review", "basis": "Patient and treating-clinician accounts establish the clinical workflow but not the exact technical configuration.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Software analyzed brain images and alerted the treating team before its review, according to the surgeon.", "editor_note": "" } }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "The named-patient accounts do not identify the software brand, module, underlying model, or release. The Viz.ai attribution relies on a separate interview and remains inferred.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "reason": "The named-patient accounts do not identify the software brand, module, underlying model, or release. The Viz.ai attribution relies on a separate interview and remains inferred.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The detector for brain bleeding is not separately named.", "editor_note": "" } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "The named-patient accounts do not identify the software brand, module, underlying model, or release. The Viz.ai attribution relies on a separate interview and remains inferred.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "reason": "The named-patient accounts do not identify the software brand, module, underlying model, or release. The Viz.ai attribution relies on a separate interview and remains inferred.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The sources do not identify the installed release.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Viz.ai stroke detection and care coordination (attribution inferred)" } }, "provenance": { "kind": "derivation", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Apparently Viz.ai.", "editor_note": "In a separate interview, the surgeon names Viz.ai and describes an unnamed ICU nurse whose circumstances match Knichel’s hospital story. He does not explicitly connect her name to the product (references 1–3)." } }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "provider": "Viz.ai", "attribution": "inferred", "basis": "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred." } }, "provenance": { "kind": "derivation", "basis": "Snelling names Viz.ai as software used by the hospital, then discusses workflows and an unnamed ICU nurse whose circumstances match Knichel’s. The original video does not explicitly connect her name to the product. The attribution remains inferred.", "record_fields": [ "developer", "system" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Viz.ai, inferred from the surgeon’s separate interview.", "editor_note": "" } }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "AI flags Julie Knichel's brain hemorrhage before aneurysm treatment and her return to nursing", "headline": "Doctors found a nurse’s brain bleed with help from AI" } }, "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": [ "2e3e3df9046689dd81d56e536fd692f7e4e834745f330a46e124736af5bf6e5f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Recomputed the fingerprint after the homepage copy revision.", "record_fields": [ "summary", "revisions" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Julie Knichel, a 57-year-old nurse, suffered a ruptured brain aneurysm. At Baptist Health's Marcus Neuroscience Institute, AI reviewed her scan and alerted clinicians to the bleeding. They sealed the aneurysm with coils delivered through a catheter. After about three weeks in hospital, she recovered and returned to full-time nursing. Her symptoms began on 2 February 2024. Baptist Health gives the day and month, and Jan Engoren’s subsequent interviews with Knichel and Snelling establish the year. The surgeon names Viz.ai in a later interview and describes an unnamed ICU nurse whose circumstances match Knichel’s. The product attribution is therefore inferred. Neither account identifies the module or release.", "replication": { "produced_by": [ "AI-assisted imaging and specialist treatment." ], "must_hold": [ "Prompt clinical review and access to aneurysm treatment." ], "echoes": [] } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0582-source-0001", "AIBD-0582-source-0002", "AIBD-0582-source-0003" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Software detected bleeding in brain images and alerted clinicians. The treating team investigated and treated the aneurysm.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 20, "record_mapped": 14, "not_assessed": 57, "complete": false, "by_state": { "value_present": 32, "not_reported": 2, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0010", "case_url": "records/AIBD-0010.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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": "Specific harm prevented.", "details": [ { "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." }, { "label": "Comparison", "value": "reports escalated without Meta's pattern-recognition acceleration", "url": "https://about.fb.com/news/2017/11/getting-our-community-help-in-real-time/" }, { "label": "Before", "value": "escalation speed of other reports" }, { "label": "After", "value": "escalated to local authorities twice as quickly as other reports" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "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. 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": [ "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-0317", "case_url": "records/AIBD-0317.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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). 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." } ], "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": [] }, "paired_incident": null, "headline": "Vyntra's AI risk models may have stopped a USD 500,000 ATM fraud", "metric": "fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million)", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No dated follow-up to any of the cases was found; the June 2025 merger of NetGuardians and Intix into Vyntra concerns the vendor, not the cases.", "quote": null, "url": null }, "value": { "amount": "USD 500,000 (KSh 58 million)", "what": "fraudulent ATM withdrawal detected and stopped, with arrests", "who": "the second bank, an unnamed client bank (the page labels the case 'Kenya')", "estimate": false, "quote": "The fraud was detected in real time and the fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank’s ATMs. Vyntra's AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals.", "url": "https://www.vyntra.com/fraud-cases/" }, "developer_country": "Switzerland", "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": "Depth rated 3 for personal and 4 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 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.", "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-0317", "catalog_version": "1.0.0", "source_fingerprint": "0242cb63702bd620f893f6be7eb51974757fa5552e2abf436018f8573cdcbf56", "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-0317-source-0001", "AIBD-0317-source-0002", "AIBD-0317-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-0317-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": "Vyntra (NetGuardians SA, merged with Intix in June 2025)", "deployer": "Unnamed client banks" } }, "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 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." } } }, "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 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" }, "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. 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It says payments up to USD 850,000 were blocked before funds left client banks. Serious losses averted if the account holds. 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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" }, "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": "USD 500,000 (KSh 58 million)", "what": "fraudulent ATM withdrawal detected and stopped, with arrests", "who": "the second bank, an unnamed client bank (the page labels the case 'Kenya')", "estimate": false, "quote": "The fraud was detected in real time and the fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank’s ATMs. Vyntra's AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals.", "url": "https://www.vyntra.com/fraud-cases/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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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": "fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million)", "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": "unknown", "note": "No dated follow-up to any of the cases was found; 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": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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": 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"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-0011", "case_url": "records/AIBD-0011.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The CNN detector returned the man's coordinates after police and tracking dogs had failed. Rescuers then retrieved him. Three of the five case-report authors work for SARUAV Ltd., so the independent confirmation is the GOPR Bieszczady account quoted in the Polish press." }, { "label": "Comparison", "value": "the ground search by police officers and rescuers with tracking dogs, which had failed", "url": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html" }, { "label": "Before", "value": "Psy zgubiły trop (the dogs lost the scent); police and rescuers could not find him" }, { "label": "After", "value": "Po prawie 24 godzinach od zginięcia program wskazał potencjalnego poszukiwanego (after almost 24 hours the program indicated the probable missing person)" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "record": { "id": "AIBD-0011", "title": "SARUAV convolutional-network detector locates an unconscious 65-year-old in tall grass during a Bieszczady Mountain Rescue search", "summary": "29 June 2021: SARUAV CNN flagged a 65-year-old man unconscious in tall grass after 24+ h; GOPR Bieszczady retrieved him alive", "date_observed": "2021-06-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Monday 28 June 2021 at about 4 pm a 65-year-old man with Alzheimer's disease left his home in Cergowa, south-east Poland. He had a stroke the previous day. Police and tracking dogs did not find him after a storm. On Tuesday 29 June the Bieszczady Mountain Rescue Service (GOPR Bieszczady) flew four drone flights. It ran SARUAV, software from University of Wrocław researchers that uses convolutional neural networks (CNNs) to find people in nadir aerial images, on 782 images. After 4 h 31 min of analysis the system detected him and returned his coordinates. He was lying unconscious in grass over two meters tall after more than 24 hours outdoors. GOPR rescuers carried him out and handed him to the ambulance service. No cited source gives the day of the find outright. 29 June follows from the 28 June 4 pm departure and the GOPR communique quoted by Świat Dronów. It opens “Od wczoraj – 28 czerwca” (“since yesterday, 28 June”) and says the program flagged him after almost 24 hours.", "category": "harm prevented", "unexpected": false, "purpose": "Built to detect people automatically in drone imagery for search and rescue; this benefit is that purpose.", "system": "SARUAV (Search and Rescue with Unmanned Aerial Vehicle) human detection software", "model_version": "SARUAV, run on images from DJI Matrice 300 drones", "developer": "SARUAV Ltd. / University of Wrocław (team of Tomasz Niedzielski)", "deployer": "Bieszczady Mountain Rescue Service (GOPR Bieszczady)", "location": "Cergowa, Poland", "domain": "Search and rescue", "segment": "Mountain rescue", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 4, "reason": "A 65-year-old man with Alzheimer's and a stroke a day earlier lay unconscious in two-meter grass after over 24 hours outdoors. He was found and handed to the ambulance service alive." }, { "group": "organizational", "depth": 2, "reason": "GOPR Bieszczady closed a search that police, dogs and ground teams had not resolved, in what it called the first such find in Poland." } ], "ai_role": { "status": "assessed", "statement": "The CNN detector returned the man's coordinates after police and tracking dogs had failed. Rescuers then retrieved him. Three of the five case-report authors work for SARUAV Ltd., so the independent confirmation is the GOPR Bieszczady account quoted in the Polish press." }, "evidence": [ { "kind": "case report", "citation": "Niedzielski, T., Jurecka, M., Miziński, B., Pawul, W., Motyl, T. (2021). First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland). Remote Sensing 13(23):4903. doi:10.3390/rs13234903.", "title": "First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)", "url": "https://doi.org/10.3390/rs13234903", "date": "2021-12-03", "written_by": "makers", "quote": "After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates" }, { "kind": "news report", "citation": "Olejarka, B. (2021). Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce. Super Express (Rzeszów).", "title": "Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce", "url": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html", "date": "2021-07-09", "written_by": "independent", "quote": "Mamy odnalezienie! Pierwsze w Polsce skuteczne odnalezienie poszukiwanego za pomocą drona i programu SARUAV!" }, { "kind": "news report", "citation": "oiot.pl (2021). Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "title": "Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "url": "https://oiot.pl/dron-saruav-uratowal-zycie-czlowiekowi-w-polsce/", "date": "2021-06-30", "written_by": "independent", "quote": "Pomoc dotarła na czas, a zaginiony został bezpiecznie przekazany w ręce pogotowia ratunkowego." }, { "kind": "news report", "citation": "Świat Dronów (2021). Polski system SARUAV uratował życie zaginionemu mężczyźnie.", "title": "Polski system SARUAV uratował życie zaginionemu mężczyźnie", "url": "https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "date": "2021-06-30", "written_by": "independent", "quote": "Po prawie 24 godzinach od zginięcia program wskazał potencjalnego poszukiwanego" }, { "kind": "press release", "citation": "University of Wrocław (n.d.). Another missing person found using the SARUAV system. uwr.edu.pl.", "title": "Another missing person found using the SARUAV system", "url": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-system/", "date": null, "written_by": "makers", "quote": "the world's first automatic detection of a missing person on aerial imagery taken from a drone" } ], "replication": { "produced_by": [ "Nadir drone imagery at close range over the search area", "CNN detector run during the search, with rescuers able to verify hits and reach the coordinates" ], "must_hold": [ "The person is within the imaged area and visible from above", "Processing completes while the person can still be helped" ], "echoes": [ { "date": "2024-08-01", "setting": "Sand in Taufers area, South Tyrol, Italy, “beginning of August 2024” (first day of month used): Bergrettungsdienst im Alpenverein Südtirol drone team used SARUAV to find a missing person, deceased, in terrain helicopters had searched; reported by the developer's university (University of Wrocław), not an independent source", "model_version": null, "outcome": "recurred", "source": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-system/" } ] }, "paired_incident": null, "headline": "SARUAV finds an unconscious 65-year-old alive in tall grass", "metric": "After 4 h 31 min of analysis the system detected him and returned his coordinates", "comparator": { "against": "the ground search by police officers and rescuers with tracking dogs, which had failed", "before": "Psy zgubiły trop (the dogs lost the scent); police and rescuers could not find him", "after": "Po prawie 24 godzinach od zginięcia program wskazał potencjalnego poszukiwanego (after almost 24 hours the program indicated the probable missing person)", "basis": "measured", "quote": "Burza, która przeszła nad terenem gdzie zgubił się chory 65-letni mężczyzna, utrudniła poszukiwania. Psy zgubiły trop. Pomogła nowoczesna technologia i to w ostatniej chwili, bo mężczyzna leżał w ponad dwumetrowych zaroślach bez ruchu.", "url": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html" }, "later": null, "value": null, "developer_country": "Poland", "deployer_country": "Poland", "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": "Added to the account that the 29 June date is derived from the 28 June departure and the GOPR communique rather than stated outright in any source, and dated the Świat Dronów report 30 June 2021 from its page metadata; the August 2024 echo keeps its disclosed first-of-month placeholder because dates must be full ISO days.", "source": "https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "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, the model version from the cited sources.", "source": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html; https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "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.saruav.pl/; https://pl.wikipedia.org/wiki/Grupa_Bieszczadzka_GOPR", "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, the makers' case report and the makers' press release. 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He was found and handed to the ambulance service alive." }, { "group": "organizational", "depth": 2, "reason": "GOPR Bieszczady closed a search that police, dogs and ground teams had not resolved, in what it called the first such find in Poland." } ] } }, "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 automatically in drone imagery for search and rescue; this benefit is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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https://pl.wikipedia.org/wiki/Grupa_Bieszczadzka_GOPR", "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": [ "Nadir drone imagery at close range over the search area", "CNN detector run during the search, with rescuers able to verify hits and reach the coordinates" ], "must_hold": [ "The person is within the imaged area and visible from above", "Processing completes while the person can still be helped" ], "echoes": [ { "date": "2024-08-01", "setting": "Sand in Taufers area, South Tyrol, Italy, “beginning of August 2024” (first day of month used): Bergrettungsdienst im Alpenverein Südtirol drone team used SARUAV to find a missing person, deceased, in terrain helicopters had searched; 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reported by the developer's university (University of Wrocław), not an independent source", "outcome": "recurred", "source": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-system/" } ] } } }, "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": "Added to the account that the 29 June date is derived from the 28 June departure and the GOPR communique rather than stated outright in any source, and dated the Świat Dronów report 30 June 2021 from its page metadata; the August 2024 echo keeps its disclosed first-of-month placeholder because dates must be full ISO days.", "source": "https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; 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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": "Search and rescue", "segment": "Mountain rescue" } }, "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": "Niedzielski, T., Jurecka, M., Miziński, B., Pawul, W., Motyl, T. (2021). First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland). Remote Sensing 13(23):4903. doi:10.3390/rs13234903.", "title": "First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)", "url": "https://doi.org/10.3390/rs13234903", "date": "2021-12-03", "written_by": "makers", "quote": "After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates" }, { "kind": "news report", "citation": "Olejarka, B. (2021). Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce. Super Express (Rzeszów).", "title": "Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce", "url": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html", "date": "2021-07-09", "written_by": "independent", "quote": "Mamy odnalezienie! Pierwsze w Polsce skuteczne odnalezienie poszukiwanego za pomocą drona i programu SARUAV!" }, { "kind": "news report", "citation": "oiot.pl (2021). Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "title": "Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "url": "https://oiot.pl/dron-saruav-uratowal-zycie-czlowiekowi-w-polsce/", "date": "2021-06-30", "written_by": "independent", "quote": "Pomoc dotarła na czas, a zaginiony został bezpiecznie przekazany w ręce pogotowia ratunkowego." }, { "kind": "news report", "citation": "Świat Dronów (2021). Polski system SARUAV uratował życie zaginionemu mężczyźnie.", "title": "Polski system SARUAV uratował życie zaginionemu mężczyźnie", "url": "https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "date": "2021-06-30", "written_by": "independent", "quote": "Po prawie 24 godzinach od zginięcia program wskazał potencjalnego poszukiwanego" }, { "kind": "press release", "citation": "University of Wrocław (n.d.). Another missing person found using the SARUAV system. uwr.edu.pl.", "title": "Another missing person found using the SARUAV system", "url": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-system/", "written_by": "makers", "quote": "the world's first automatic detection of a missing person on aerial imagery taken from a drone" } ] } }, "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-0011-source-0001", "AIBD-0011-source-0002", "AIBD-0011-source-0003", "AIBD-0011-source-0004", "AIBD-0011-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": "case report", "citation": "Niedzielski, T., Jurecka, M., Miziński, B., Pawul, W., Motyl, T. (2021). First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland). Remote Sensing 13(23):4903. doi:10.3390/rs13234903.", "title": "First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)", "url": "https://doi.org/10.3390/rs13234903", "date": "2021-12-03", "written_by": "makers", "quote": "After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates" }, { "kind": "news report", "citation": "Olejarka, B. (2021). Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce. Super Express (Rzeszów).", "title": "Bieszczady. Dron odnalazł zagubionego człowieka! Pierwszy raz w Polsce", "url": "https://www.se.pl/rzeszow/bieszczady-pierwszy-raz-w-polsce-dron-odnalazl-zagubionego-czlowieka-galeria-aa-Lokr-ga7B-yzAt.html", "date": "2021-07-09", "written_by": "independent", "quote": "Mamy odnalezienie! Pierwsze w Polsce skuteczne odnalezienie poszukiwanego za pomocą drona i programu SARUAV!" }, { "kind": "news report", "citation": "oiot.pl (2021). Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "title": "Historia pisze się na naszych oczach: dron z polskim oprogramowaniem SARUAV uratował życie człowiekowi!", "url": "https://oiot.pl/dron-saruav-uratowal-zycie-czlowiekowi-w-polsce/", "date": "2021-06-30", "written_by": "independent", "quote": "Pomoc dotarła na czas, a zaginiony został bezpiecznie przekazany w ręce pogotowia ratunkowego." }, { "kind": "news report", "citation": "Świat Dronów (2021). Polski system SARUAV uratował życie zaginionemu mężczyźnie.", "title": "Polski system SARUAV uratował życie zaginionemu mężczyźnie", "url": "https://www.swiatdronow.pl/polski-system-saruav-uratowal-zycie-zaginionemu-mezczyznie", "date": "2021-06-30", "written_by": "independent", "quote": "Po prawie 24 godzinach od zginięcia program wskazał potencjalnego poszukiwanego" }, { "kind": "press release", "citation": "University of Wrocław (n.d.). Another missing person found using the SARUAV system. uwr.edu.pl.", "title": "Another missing person found using the SARUAV system", "url": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-system/", "written_by": "makers", "quote": "the world's first automatic detection of a missing person on aerial imagery taken from a drone" } ] } }, "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 June 2021: SARUAV CNN flagged a 65-year-old man unconscious in tall grass after 24+ h; GOPR Bieszczady retrieved him alive", "account": "On Monday 28 June 2021 at about 4 pm a 65-year-old man with Alzheimer's disease left his home in Cergowa, south-east Poland. He had a stroke the previous day. Police and tracking dogs did not find him after a storm. On Tuesday 29 June the Bieszczady Mountain Rescue Service (GOPR Bieszczady) flew four drone flights. It ran SARUAV, software from University of Wrocław researchers that uses convolutional neural networks (CNNs) to find people in nadir aerial images, on 782 images. After 4 h 31 min of analysis the system detected him and returned his coordinates. He was lying unconscious in grass over two meters tall after more than 24 hours outdoors. GOPR rescuers carried him out and handed him to the ambulance service. No cited source gives the day of the find outright. 29 June follows from the 28 June 4 pm departure and the GOPR communique quoted by Świat Dronów. It opens “Od wczoraj – 28 czerwca” (“since yesterday, 28 June”) and says the program flagged him after almost 24 hours." } }, "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 detector located a person in aerial photographs and returned his coordinates." }, "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": "SARUAV analyzed photographs from DJI Matrice 300 drones." }, "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": "SARUAV analyzed aerial photographs to locate people." }, "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": "SARUAV, run on images from DJI Matrice 300 drones" } }, "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": "SARUAV (Search and Rescue with Unmanned Aerial Vehicle) human detection 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": "SARUAV Ltd. / University of Wrocław (team of Tomasz Niedzielski)" } }, "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": "SARUAV convolutional-network detector locates an unconscious 65-year-old in tall grass during a Bieszczady Mountain Rescue search", "headline": "SARUAV finds an unconscious 65-year-old alive in tall grass" } }, "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": [ "d4256ad892f41eb9433d9d2982c154a0f8fd762436e63458ebdcd743a48027f3" ], "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 Monday 28 June 2021 at about 4 pm a 65-year-old man with Alzheimer's disease left his home in Cergowa, south-east Poland. He had a stroke the previous day. Police and tracking dogs did not find him after a storm. On Tuesday 29 June the Bieszczady Mountain Rescue Service (GOPR Bieszczady) flew four drone flights. It ran SARUAV, software from University of Wrocław researchers that uses convolutional neural networks (CNNs) to find people in nadir aerial images, on 782 images. After 4 h 31 min of analysis the system detected him and returned his coordinates. He was lying unconscious in grass over two meters tall after more than 24 hours outdoors. GOPR rescuers carried him out and handed him to the ambulance service. No cited source gives the day of the find outright. 29 June follows from the 28 June 4 pm departure and the GOPR communique quoted by Świat Dronów. It opens “Od wczoraj – 28 czerwca” (“since yesterday, 28 June”) and says the program flagged him after almost 24 hours.", "replication": { "produced_by": [ "Nadir drone imagery at close range over the search area", "CNN detector run during the search, with rescuers able to verify hits and reach the coordinates" ], "must_hold": [ "The person is within the imaged area and visible from above", "Processing completes while the person can still be helped" ], "echoes": [ { "date": "2024-08-01", "setting": "Sand in Taufers area, South Tyrol, Italy, “beginning of August 2024” (first day of month used): Bergrettungsdienst im Alpenverein Südtirol drone team used SARUAV to find a missing person, deceased, in terrain helicopters had searched; reported by the developer's university (University of Wrocław), not an independent source", "outcome": "recurred", "source": "https://uwr.edu.pl/en/another-missing-person-found-using-the-saruav-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": 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-0012", "case_url": "records/AIBD-0012.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The AI flagged the hikers in the drone feed, and the thermal camera confirmed the find. The inspector overseeing the search credits it with cutting the search short. The hikers also signaled with a phone light, and the software vendor is not named. FRNSW's own media release was not available and is not cited. No cited source dates it." }, { "label": "Comparison", "value": "a conventional search without the AI drone", "url": "https://www.abc.net.au/news/2026-06-27/lost-hikers-found-by-ai-drone-in-kosciuszko-national-park/106846712" }, { "label": "Before", "value": "several days (the inspector's estimate of the search time avoided)" }, { "label": "After", "value": "less than five hours after they were reported missing" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0012", "title": "Fire and Rescue NSW drone with AI person detection finds two lost hikers off the Dead Horse Gap track within five hours", "summary": "23 June 2026: FRNSW drone AI flagged two lost hikers 500 m off track in Kosciuszko NP; found in under five hours, mild exposure", "date_observed": "2026-06-23", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Tuesday 23 June 2026 two men in their early 20s failed to reach a rendezvous on the Dead Horse Gap walking track. The track is in Kosciuszko National Park, about 35 km south-west of Jindabyne. They were reported overdue at about 7 pm. NSW Police formed an alpine search team with the State Emergency Service and asked Fire and Rescue NSW for drone support. FRNSW launched a remotely piloted aircraft with thermal imaging and AI detection software. The software flagged the pair in bushland about 500 m off the track, and the thermal camera confirmed the find. The drone's spotlight directed ground crews to them. They were located less than five hours after the report, had mild effects of exposure and declined treatment. 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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": [ "Thermal-capable drone with onboard AI person detection launched at night from the search command post", "Subjects within drone range whose detection can be confirmed by thermal camera" ], "must_hold": [ "Weather permits flight", "Detections are verified by the pilot before ground teams are directed" ], "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": "2026-10-20", "change": "Restored the dropped middle clause of Inspector Eberle's sentence so that the About Regional quote is verbatim, moved the location from Jindabyne to the Dead Horse Gap track in Kosciuszko National Park where the sources place the hikers, and removed the 25 June 2026 date for FRNSW's media release because no cited source gives it.", "source": "https://aboutregional.com.au/how-ai-saved-two-hikers-lost-in-the-unforgiving-alpine-wilderness/509715/", "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 from the cited sources.", "source": "https://www.abc.net.au/news/2026-06-27/lost-hikers-found-by-ai-drone-in-kosciuszko-national-park/106846712", "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/Fire_and_Rescue_NSW", "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": "Search and rescue", "segment": "Fire and rescue services" } }, "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": "McGuirk-Scolaro, M. (2026). Hikers lost in Kosciuszko National Park rescued by drones powered by AI. ABC News (Australia).", "title": "Hikers lost in Kosciuszko National Park rescued by drones powered by AI", "url": "https://www.abc.net.au/news/2026-06-27/lost-hikers-found-by-ai-drone-in-kosciuszko-national-park/106846712", "date": "2026-06-27", "written_by": "independent", "quote": "It's definitely helped make what could have been a long-term incident into a very short-term incident." }, { "kind": "news report", "citation": "Sams, C. (2026). How AI saved two hikers lost in the unforgiving alpine wilderness. About Regional.", "title": "How AI saved two hikers lost in the unforgiving alpine wilderness", "url": "https://aboutregional.com.au/how-ai-saved-two-hikers-lost-in-the-unforgiving-alpine-wilderness/509715/", "date": "2026-06-29", "written_by": "independent", "quote": "The AI scans the area and scans the information that it can see on its camera and says, 'OK, this item that I'm looking at has a 35 per cent chance of being human'." }, { "kind": "news report", "citation": "Matler, A. (2026). AI-assisted drone helps rescue lost Kosciuszko hikers in NSW first. iTWire.", "title": "AI-assisted drone helps rescue lost Kosciuszko hikers in NSW first", "url": "https://itwire.com/business-it-news/business-technology/ai-assisted-drone-helps-rescue-lost-kosciuszko-hikers-in-nsw-first", "date": "2026-06-30", "written_by": "independent", "quote": "a first-of-its-kind mission" } ] } }, "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-0012-source-0001", "AIBD-0012-source-0002", "AIBD-0012-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": "McGuirk-Scolaro, M. (2026). Hikers lost in Kosciuszko National Park rescued by drones powered by AI. ABC News (Australia).", "title": "Hikers lost in Kosciuszko National Park rescued by drones powered by AI", "url": "https://www.abc.net.au/news/2026-06-27/lost-hikers-found-by-ai-drone-in-kosciuszko-national-park/106846712", "date": "2026-06-27", "written_by": "independent", "quote": "It's definitely helped make what could have been a long-term incident into a very short-term incident." }, { "kind": "news report", "citation": "Sams, C. (2026). How AI saved two hikers lost in the unforgiving alpine wilderness. About Regional.", "title": "How AI saved two hikers lost in the unforgiving alpine wilderness", "url": "https://aboutregional.com.au/how-ai-saved-two-hikers-lost-in-the-unforgiving-alpine-wilderness/509715/", "date": "2026-06-29", "written_by": "independent", "quote": "The AI scans the area and scans the information that it can see on its camera and says, 'OK, this item that I'm looking at has a 35 per cent chance of being human'." }, { "kind": "news report", "citation": "Matler, A. (2026). AI-assisted drone helps rescue lost Kosciuszko hikers in NSW first. iTWire.", "title": "AI-assisted drone helps rescue lost Kosciuszko hikers in NSW first", "url": "https://itwire.com/business-it-news/business-technology/ai-assisted-drone-helps-rescue-lost-kosciuszko-hikers-in-nsw-first", "date": "2026-06-30", "written_by": "independent", "quote": "a first-of-its-kind mission" } ] } }, "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": "23 June 2026: FRNSW drone AI flagged two lost hikers 500 m off track in Kosciuszko NP; found in under five hours, mild exposure", "account": "On Tuesday 23 June 2026 two men in their early 20s failed to reach a rendezvous on the Dead Horse Gap walking track. The track is in Kosciuszko National Park, about 35 km south-west of Jindabyne. They were reported overdue at about 7 pm. NSW Police formed an alpine search team with the State Emergency Service and asked Fire and Rescue NSW for drone support. FRNSW launched a remotely piloted aircraft with thermal imaging and AI detection software. The software flagged the pair in bushland about 500 m off the track, and the thermal camera confirmed the find. The drone's spotlight directed ground crews to them. They were located less than five hours after the report, had mild effects of exposure and declined treatment. FRNSW Inspector Phillip Eberle said the technology turned a potential long-term incident into a short-term 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": "Fire and Rescue NSW remotely piloted aircraft system with thermal imaging and AI person-detection software (software vendor not named in the 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": "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": "Fire and Rescue NSW drone with AI person detection finds two lost hikers off the Dead Horse Gap track within five hours", "headline": "FRNSW drone AI finds two lost hikers in under five hours" } }, "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": [ "8c39dd93c59267c78bef620f5881fce0946039ab4712bd0a41925d9c0536c17e" ], "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 Tuesday 23 June 2026 two men in their early 20s failed to reach a rendezvous on the Dead Horse Gap walking track. The track is in Kosciuszko National Park, about 35 km south-west of Jindabyne. They were reported overdue at about 7 pm. NSW Police formed an alpine search team with the State Emergency Service and asked Fire and Rescue NSW for drone support. FRNSW launched a remotely piloted aircraft with thermal imaging and AI detection software. The software flagged the pair in bushland about 500 m off the track, and the thermal camera confirmed the find. The drone's spotlight directed ground crews to them. They were located less than five hours after the report, had mild effects of exposure and declined treatment. FRNSW Inspector Phillip Eberle said the technology turned a potential long-term incident into a short-term one.", "replication": { "produced_by": [ "Thermal-capable drone with onboard AI person detection launched at night from the search command post", "Subjects within drone range whose detection can be confirmed by thermal camera" ], "must_hold": [ "Weather permits flight", "Detections are verified by the pilot before ground teams are directed" ], "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-0239", "case_url": "records/AIBD-0239.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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. 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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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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. 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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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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. 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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. 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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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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-0234", "catalog_version": "1.0.0", "source_fingerprint": "f901188e7feedf658f44ab9f117672015b105d67cca14325d6cd760c68c7535e", "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-0234-source-0001", "AIBD-0234-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": "Asilla Inc.", "deployer": "About 40 railway stations and commercial buildings in Japan (operators not named)" } }, "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": "Kyodo attributes both interventions to Asilla’s account. 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Its product documentation describes how alerts work, but the company supplies no dates, locations, or alert logs for these incidents.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "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. The facilities, dates, alert logs, and subsequent outcomes are not supplied." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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dates and facilities undisclosed", "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": "unknown", "note": "The retained sources do not establish subsequent outcomes for the two people." } } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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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. It did not identify the facilities or dates.", "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 people in apparent crisis were reached by guards after alerts, per the developer; no independent account of either case." }, { "group": "organizational", "reason": "No operator-reported outcome or false-alert data 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": "Detect behavior indicating suicide risk (and illness, immobility or violence) in security-camera footage and alert staff." } }, "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": "Japan" } }, "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": "Detect behavior indicating suicide risk (and illness, immobility or violence) in security-camera footage and alert staff." } }, "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": "2026-06-01", "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": "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": "June 1, 2026 is the Kyodo report’s publication date. 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. Kept Kyodo’s original reporting through its identified syndication, added the developer’s actual product documentation, and removed OECD as corroboration.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Public safety / suicide prevention", "segment": "Transport and real estate" } }, "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.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" } ] } }, "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-0234-source-0001", "AIBD-0234-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The complete Kyodo report is available through The Straits Times. Chiba TV identifies its original distribution link, which is inaccessible. 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. 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[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0234-source-0001", "AIBD-0234-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-0234-source-0001", "AIBD-0234-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": "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. 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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. This referral is that designed use. ChatGPT itself is a general-purpose assistant, which Zhou used to rehearse his plans.", "system": "ChatGPT (OpenAI), with OpenAI's automated detection and human review pipeline for users planning to harm others", "model_version": null, "developer": "OpenAI", "deployer": "OpenAI", "location": "South Palm Beach, United States", "domain": "Public safety - stalking and threat investigation", "segment": "Consumer chatbot trust-and-safety referral to law enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-08-15-e71a" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The reported referral led to investigation, arrest, and court restrictions that protected the threatened person. The accessible report describes probation, GPS monitoring, and contact and weapons restrictions. The underlying affidavit and plea order were not directly accessible in this review, and prevention of a future attack remains a counterfactual claim." } ], "ai_role": { "status": "assessed", "statement": "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." }, "evidence": [ { "kind": "news report", "citation": "Colton A (2026). Man Flagged by ChatGPT Pleads Guilty in Stalking Case. 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. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0260", "catalog_version": "1.0.0", "source_fingerprint": "1305768f24ac19a795ee53d018884bb83754966afae529eb2e6116f1de72b313", "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-0260-source-0001", "AIBD-0260-source-0002", "AIBD-0260-source-0003", "AIBD-0260-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": "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": "The reporting identifies an OpenAI disclosure as the route that brought Zhou’s messages to police attention. 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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.", "benefit_type": "Reduced Violence" } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped fields during the source review. 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Man Flagged by ChatGPT Pleads Guilty in Stalking Case. 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" }, { "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" }, { "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 old quotation contained non-source wording and has been removed rather than reconstructed." ] }, { "claim": "OpenAI’s policy routes detected threats through human reviewers before possible law-enforcement referral.", "source_ref": "AIBD-0260-source-0004", "source_chain": [ { "url": "https://openai.com/index/helping-people-when-they-need-it-most/", "locator": "“Escalate risk of physical harm to others for human review”", "role": "OpenAI’s original policy explains the general human-review step. It does not verify this particular referral." } ], "locator": "“Escalate risk of physical harm to others for human review”", "method": {}, "limitations": [ "General policy is not a case-specific audit log." ] } ], "limitations": [ "PBSO’s blotter and the Palm Beach court portal rejected access. The affidavit, actual chats, emergency disclosure, and plea order were not directly read.", "Sun Sentinel, WPBF, and Palm Beach Post were inaccessible during this review. 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-0302", "case_url": "records/AIBD-0302.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "ABC News, citing Clermont County officials, and WLWT say the Apple Watch's Crash Detection placed the 911 call. Apple's support page says the feature detects severe crashes from on-device sensor data. It calls automatically after a countdown if the wearer does not respond. Burns also told WKRC that a Life360 crash-detection service on her mother's phone called police and family. The watch may not have been the only alert. Only news reports are among the sources, and no dispatch log or agency statement is on record." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0302", "title": "Apple Watch Crash Detection calls 911 for Andi Burns, 19, trapped upside down in her pickup in the Little Miami River; Clermont County crews pull her out with bruises only", "summary": "On 22 February 2026 near Batavia, Ohio, a 19-year-old's truck flipped into the Little Miami River; Clermont County officials say her Apple Watch called 911 by Crash Detection and crews freed her.", "date_observed": "2026-02-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Andi Burns, 19, was driving home from work on State Route 222 in Batavia Township, Clermont County, Ohio, on 22 February 2026. The Ohio State Highway Patrol accident report cited by ABC News says she lost control on black ice. She hit a tree and went off an embankment into the Little Miami River. WLWT says the truck dropped down a 15-foot ravine and flipped. ABC News (4 March 2026) reports that “According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature”. Body-camera footage released by the Clermont County Sheriff's Office shows a dispatcher keeping her on the line. Central Joint Fire-EMS District firefighters and sheriff's deputies smashed a window and pulled her out. First-year firefighter Tommy Jetter was first into the water. Burns had found a pocket of air of about four inches at the floorboard. The accident report says she walked away with bruises. Burns told WKRC (11 March 2026) that there was also “a crash detection on the Live 360 that my mom had on her phone”. She said it called the police and her family, so a second crash-detection service may have placed calls. WLWT and ABC attribute the 911 call to the Apple Watch.", "category": "protective response", "unexpected": false, "purpose": "Crash Detection is built to recognize a severe car crash from sensor data. It then calls emergency services with the device's location if the wearer does not respond. This call was that purpose.", "system": "Apple Watch Crash Detection", "model_version": null, "developer": "Apple", "deployer": null, "location": "Batavia Township, Clermont County, Ohio, United States", "domain": "Emergency response / road traffic injury", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Rescuers and Burns describe a submerged cab with four to six inches of air. Firefighter Jetter called her survival “very, very, very lucky”. The call brought crews at night to a river crash. Whether anyone else would have called is not documented, and the sources are news reports." }, { "group": "organizational", "depth": 1, "reason": "Clermont County dispatch received the automated call with a location and stayed on the line. The sheriff's office released the body-camera video. No agency statement on timing or resources is among the sources." } ], "ai_role": { "status": "assessed", "statement": "ABC News, citing Clermont County officials, and WLWT say the Apple Watch's Crash Detection placed the 911 call. Apple's support page says the feature detects severe crashes from on-device sensor data. It calls automatically after a countdown if the wearer does not respond. Burns also told WKRC that a Life360 crash-detection service on her mother's phone called police and family. The watch may not have been the only alert. Only news reports are among the sources, and no dispatch log or agency statement is on record." }, "evidence": [ { "kind": "news report", "citation": "ABC News / Good Morning America (2026). 19-year-old rescued from car sinking in freezing water speaks out. 4 March 2026.", "title": "19-year-old rescued from car sinking in freezing water speaks out", "url": "https://abcnews.com/GMA/News/19-year-pulled-sinking-car-details-rescue-responders/story?id=130747244", "date": "2026-03-04", "written_by": "independent", "quote": "According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature." }, { "kind": "news report", "citation": "WLWT News 5 (2026). 19-year-old pulled from near-drowning truck crash in Little Miami River. Updated 26 February 2026.", "title": "19-year-old pulled from near-drowning truck crash in Little Miami River", "url": "https://www.wlwt.com/article/miami-river-body-camera-video-rescue-car-truck/70526120", "date": "2026-02-26", "written_by": "independent", "quote": "Her Apple Watch crash detection called 911." }, { "kind": "news report", "citation": "Clements A (2026). Woman thanks rescuers who saved her from crash into Clermont County river. WKRC Local 12, 11 March 2026.", "title": "Woman thanks rescuers who saved her from crash into Clermont County river", "url": "https://web.archive.org/web/20260311210830/https://local12.com/news/local/woman-thanks-rescuers-who-saved-her-from-crash-into-clermont-county-creek-andi-burns-ice-little-miami-river-apple-watch-thomas-jetter-first-responders", "date": "2026-03-11", "written_by": "independent", "quote": "I had my Apple Watch on that day, and there was a crash detection on the Live 360 that my mom had on her phone. It called the police." }, { "kind": "press release", "citation": "Apple Support. Use Crash Detection on iPhone or Apple Watch to call for help in an accident (article 104959).", "title": "Use Crash Detection on iPhone or Apple Watch to call for help in an accident", "url": "https://support.apple.com/en-us/104959", "date": null, "written_by": "makers", "quote": "If you're unable to respond, your device automatically calls emergency services after a 30-second countdown." } ], "replication": { "produced_by": [ "A supported Apple Watch (Series 8 or later, SE 2nd generation, Ultra) with Crash Detection on and a cellular or Wi-Fi path to emergency services", "A severe crash (rollover into water) with the wearer unable to reach a phone", "A dispatcher able to act on the automated call and location" ], "must_hold": [ "The on-device crash classifier recognizes the impact as a severe car crash", "The wearer does not dismiss the alert within the countdown", "Responders can reach the transmitted location in time" ], "echoes": [ { "date": "2024-12-16", "setting": "Easthampton, Massachusetts: Brent Hill, 55, blacked out while driving and landed upside down in a neighbor's pool; emergency services spoke to him through the watch and arrived within minutes (Western Mass News, 16 January 2025)", "model_version": null, "outcome": "recurred", "source": "https://www.westernmassnews.com/2025/01/16/easthampton-man-credits-apple-watch-helping-save-his-life/" } ] }, "paired_incident": null, "headline": "Apple Watch Crash Detection calls 911 for Andi Burns in a river", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-03-11", "note": "WKRC reported on 11 March 2026 that Burns was alive and well and had thanked the Clermont County responders; ABC News (4 March) said the accident report recorded only bruises.", "quote": "A local woman is alive and well after she's rescued from frigid waters by first responders.", "url": "https://web.archive.org/web/20260311210830/https://local12.com/news/local/woman-thanks-rescuers-who-saved-her-from-crash-into-clermont-county-creek-andi-burns-ice-little-miami-river-apple-watch-thomas-jetter-first-responders" }, "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 from the cited sources.", "source": "https://web.archive.org/web/20260311210830/https://local12.com/news/local/woman-thanks-rescuers-who-saved-her-from-crash-into-clermont-county-creek-andi-burns-ice-little-miami-river-apple-watch-thomas-jetter-first-responders", "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, 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: three 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-0302", "catalog_version": "1.0.0", "source_fingerprint": "ddecf428306c8ef0d7ef7c11420d47a6b43501cd38e569c2e39070264c7d9294", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0302-source-0001", "AIBD-0302-source-0002", "AIBD-0302-source-0003", "AIBD-0302-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": "Apple" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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": "ABC News, citing Clermont County officials, and WLWT say the Apple Watch's Crash Detection placed the 911 call. Apple's support page says the feature detects severe crashes from on-device sensor data. It calls automatically after a countdown if the wearer does not respond. Burns also told WKRC that a Life360 crash-detection service on her mother's phone called police and family. The watch may not have been the only alert. Only news reports are among the sources, and no dispatch log or agency statement is on record." } } }, "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": "ABC News, citing Clermont County officials, and WLWT say the Apple Watch's Crash Detection placed the 911 call. Apple's support page says the feature detects severe crashes from on-device sensor data. It calls automatically after a countdown if the wearer does not respond. Burns also told WKRC that a Life360 crash-detection service on her mother's phone called police and family. The watch may not have been the only alert. Only news reports are among the sources, and no dispatch log or agency statement is on record." } } }, "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": "Rescuers and Burns describe a submerged cab with four to six inches of air. Firefighter Jetter called her survival “very, very, very lucky”. The call brought crews at night to a river crash. Whether anyone else would have called is not documented, and the sources are news reports." }, { "group": "organizational", "depth": 1, "reason": "Clermont County dispatch received the automated call with a location and stayed on the line. The sheriff's office released the body-camera video. No agency statement on timing or resources is among 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: three 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": 3, "reason": "Rescuers and Burns describe a submerged cab with four to six inches of air. Firefighter Jetter called her survival “very, very, very lucky”. The call brought crews at night to a river crash. Whether anyone else would have called is not documented, and the sources are news reports." }, { "group": "organizational", "depth": 1, "reason": "Clermont County dispatch received the automated call with a location and stayed on the line. The sheriff's office released the body-camera video. No agency statement on timing or resources is among 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": "personal", "depth": 3, "reason": "Rescuers and Burns describe a submerged cab with four to six inches of air. Firefighter Jetter called her survival “very, very, very lucky”. The call brought crews at night to a river crash. Whether anyone else would have called is not documented, and the sources are news reports." }, { "group": "organizational", "depth": 1, "reason": "Clermont County dispatch received the automated call with a location and stayed on the line. The sheriff's office released the body-camera video. 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The Ohio State Highway Patrol accident report cited by ABC News says she lost control on black ice. She hit a tree and went off an embankment into the Little Miami River. WLWT says the truck dropped down a 15-foot ravine and flipped. ABC News (4 March 2026) reports that “According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature”. Body-camera footage released by the Clermont County Sheriff's Office shows a dispatcher keeping her on the line. Central Joint Fire-EMS District firefighters and sheriff's deputies smashed a window and pulled her out. First-year firefighter Tommy Jetter was first into the water. Burns had found a pocket of air of about four inches at the floorboard. The accident report says she walked away with bruises. Burns told WKRC (11 March 2026) that there was also “a crash detection on the Live 360 that my mom had on her phone”. She said it called the police and her family, so a second crash-detection service may have placed calls. WLWT and ABC attribute the 911 call to the Apple Watch.", "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": 3, "reason": "Rescuers and Burns describe a submerged cab with four to six inches of air. Firefighter Jetter called her survival “very, very, very lucky”. The call brought crews at night to a river crash. Whether anyone else would have called is not documented, and the sources are news reports." }, { "group": "organizational", "depth": 1, "reason": "Clermont County dispatch received the automated call with a location and stayed on the line. The sheriff's office released the body-camera video. No agency statement on timing or resources is among 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": "Crash Detection is built to recognize a severe car crash from sensor data. It then calls emergency services with the device's location if the wearer does not respond. 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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": [ "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 supported Apple Watch (Series 8 or later, SE 2nd generation, Ultra) with Crash Detection on and a cellular or Wi-Fi path to emergency services", "A severe crash (rollover into water) with the wearer unable to reach a phone", "A dispatcher able to act on the automated call and location" ], "must_hold": [ "The on-device crash classifier recognizes the impact as a severe car crash", "The wearer does not dismiss the alert within the countdown", "Responders can reach the transmitted location in time" ], "echoes": [ { "date": "2024-12-16", "setting": "Easthampton, Massachusetts: Brent Hill, 55, blacked out while driving and landed upside down in a neighbor's pool; 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Updated 26 February 2026.", "title": "19-year-old pulled from near-drowning truck crash in Little Miami River", "url": "https://www.wlwt.com/article/miami-river-body-camera-video-rescue-car-truck/70526120", "date": "2026-02-26", "written_by": "independent", "quote": "Her Apple Watch crash detection called 911." }, { "kind": "news report", "citation": "Clements A (2026). Woman thanks rescuers who saved her from crash into Clermont County river. WKRC Local 12, 11 March 2026.", "title": "Woman thanks rescuers who saved her from crash into Clermont County river", "url": "https://web.archive.org/web/20260311210830/https://local12.com/news/local/woman-thanks-rescuers-who-saved-her-from-crash-into-clermont-county-creek-andi-burns-ice-little-miami-river-apple-watch-thomas-jetter-first-responders", "date": "2026-03-11", "written_by": "independent", "quote": "I had my Apple Watch on that day, and there was a crash detection on the Live 360 that my mom had on her phone. It called the police." }, { "kind": "press release", "citation": "Apple Support. Use Crash Detection on iPhone or Apple Watch to call for help in an accident (article 104959).", "title": "Use Crash Detection on iPhone or Apple Watch to call for help in an accident", "url": "https://support.apple.com/en-us/104959", "written_by": "makers", "quote": "If you're unable to respond, your device automatically calls emergency services after a 30-second countdown." } ] } }, "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-0302-source-0001", "AIBD-0302-source-0002", "AIBD-0302-source-0003", "AIBD-0302-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": "ABC News / Good Morning America (2026). 19-year-old rescued from car sinking in freezing water speaks out. 4 March 2026.", "title": "19-year-old rescued from car sinking in freezing water speaks out", "url": "https://abcnews.com/GMA/News/19-year-pulled-sinking-car-details-rescue-responders/story?id=130747244", "date": "2026-03-04", "written_by": "independent", "quote": "According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature." }, { "kind": "news report", "citation": "WLWT News 5 (2026). 19-year-old pulled from near-drowning truck crash in Little Miami River. Updated 26 February 2026.", "title": "19-year-old pulled from near-drowning truck crash in Little Miami River", "url": "https://www.wlwt.com/article/miami-river-body-camera-video-rescue-car-truck/70526120", "date": "2026-02-26", "written_by": "independent", "quote": "Her Apple Watch crash detection called 911." }, { "kind": "news report", "citation": "Clements A (2026). Woman thanks rescuers who saved her from crash into Clermont County river. WKRC Local 12, 11 March 2026.", "title": "Woman thanks rescuers who saved her from crash into Clermont County river", "url": "https://web.archive.org/web/20260311210830/https://local12.com/news/local/woman-thanks-rescuers-who-saved-her-from-crash-into-clermont-county-creek-andi-burns-ice-little-miami-river-apple-watch-thomas-jetter-first-responders", "date": "2026-03-11", "written_by": "independent", "quote": "I had my Apple Watch on that day, and there was a crash detection on the Live 360 that my mom had on her phone. It called the police." }, { "kind": "press release", "citation": "Apple Support. Use Crash Detection on iPhone or Apple Watch to call for help in an accident (article 104959).", "title": "Use Crash Detection on iPhone or Apple Watch to call for help in an accident", "url": "https://support.apple.com/en-us/104959", "written_by": "makers", "quote": "If you're unable to respond, your device automatically calls emergency services after a 30-second countdown." } ] } }, "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 22 February 2026 near Batavia, Ohio, a 19-year-old's truck flipped into the Little Miami River; Clermont County officials say her Apple Watch called 911 by Crash Detection and crews freed her.", "account": "Andi Burns, 19, was driving home from work on State Route 222 in Batavia Township, Clermont County, Ohio, on 22 February 2026. The Ohio State Highway Patrol accident report cited by ABC News says she lost control on black ice. She hit a tree and went off an embankment into the Little Miami River. WLWT says the truck dropped down a 15-foot ravine and flipped. ABC News (4 March 2026) reports that “According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature”. Body-camera footage released by the Clermont County Sheriff's Office shows a dispatcher keeping her on the line. Central Joint Fire-EMS District firefighters and sheriff's deputies smashed a window and pulled her out. First-year firefighter Tommy Jetter was first into the water. Burns had found a pocket of air of about four inches at the floorboard. The accident report says she walked away with bruises. Burns told WKRC (11 March 2026) that there was also “a crash detection on the Live 360 that my mom had on her phone”. She said it called the police and her family, so a second crash-detection service may have placed calls. WLWT and ABC attribute the 911 call to the Apple Watch." } }, "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": "Apple Watch Crash 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": "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": "Apple Watch Crash Detection calls 911 for Andi Burns, 19, trapped upside down in her pickup in the Little Miami River; Clermont County crews pull her out with bruises only", "headline": "Apple Watch Crash Detection calls 911 for Andi Burns in a river" } }, "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": [ "ddecf428306c8ef0d7ef7c11420d47a6b43501cd38e569c2e39070264c7d9294" ], "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": "Andi Burns, 19, was driving home from work on State Route 222 in Batavia Township, Clermont County, Ohio, on 22 February 2026. The Ohio State Highway Patrol accident report cited by ABC News says she lost control on black ice. She hit a tree and went off an embankment into the Little Miami River. WLWT says the truck dropped down a 15-foot ravine and flipped. ABC News (4 March 2026) reports that “According to Clermont County officials, Burns was wearing an Apple Watch that automatically called 911 using its crash detection feature”. Body-camera footage released by the Clermont County Sheriff's Office shows a dispatcher keeping her on the line. Central Joint Fire-EMS District firefighters and sheriff's deputies smashed a window and pulled her out. First-year firefighter Tommy Jetter was first into the water. Burns had found a pocket of air of about four inches at the floorboard. The accident report says she walked away with bruises. Burns told WKRC (11 March 2026) that there was also “a crash detection on the Live 360 that my mom had on her phone”. She said it called the police and her family, so a second crash-detection service may have placed calls. WLWT and ABC attribute the 911 call to the Apple Watch.", "replication": { "produced_by": [ "A supported Apple Watch (Series 8 or later, SE 2nd generation, Ultra) with Crash Detection on and a cellular or Wi-Fi path to emergency services", "A severe crash (rollover into water) with the wearer unable to reach a phone", "A dispatcher able to act on the automated call and location" ], "must_hold": [ "The on-device crash classifier recognizes the impact as a severe car crash", "The wearer does not dismiss the alert within the countdown", "Responders can reach the transmitted location in time" ], "echoes": [ { "date": "2024-12-16", "setting": "Easthampton, Massachusetts: Brent Hill, 55, blacked out while driving and landed upside down in a neighbor's pool; emergency services spoke to him through the watch and arrived within minutes (Western Mass News, 16 January 2025)", "outcome": "recurred", "source": "https://www.westernmassnews.com/2025/01/16/easthampton-man-credits-apple-watch-helping-save-his-life/" } ] } } }, "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-0102", "case_url": "records/AIBD-0102.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The AI cameras' real-time detection of the armed group is the stated trigger for the mobilization. That operation ended in surrender and seizure. The park authority's statement is the Field Director's on-record account, quoted by The New Indian Express and the Times of India. The court remands are reported in the press by PTI, Hans India and Ommcom. The cited account lacks corroboration from an available official press note or court order. The account rests on press-quoted forest-department statements." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0102", "title": "TrailGuard AI cameras trigger arrest of 39 armed poachers in Similipal Tiger Reserve", "summary": "AI trail cameras detected armed intruders on 18 February 2026; STR Field Director reports 39 surrendered, nine guns seized, case registered.", "date_observed": "2026-02-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Similipal Tiger Reserve in Mayurbhanj district, Odisha, runs a network of about 220 TrailGuard AI trail cameras. The cameras classify humans, animals and vehicles and send images to a control room. Late on Wednesday 18 February 2026 the cameras detected a group of armed intruders entering the reserve. Field Director Prakash Chand Gogineni said the real-time alerts mobilized the Similipal South Division, three ranges and three Special Tiger Protection Force units. After about 24 hours of combing, at least 39 poachers surrendered. Nine guns, gunpowder and more than 20 bows and arrows were recovered. A case was registered at Jenabil Wildlife Range. Later detections on 11 April, around 24 July and 23 September 2026 led to arrests. Courts refused bail to the July and September groups. The camera count varies by source. The Field Director gave “around 220” in February 2026 and The Hans India “over 220” in July 2026. The Times of India in April 2026 reported 50 AI cameras in use, up from an initial 10, with 300 planned.", "category": "harm prevented", "unexpected": false, "purpose": "Detect human intruders and poachers entering the reserve in near real time so rangers can intercept them before wildlife is killed.", "system": "TrailGuard AI camera network (about 220 units) with embedded human/animal/vehicle classification", "model_version": null, "developer": null, "deployer": "Similipal Tiger Reserve, Odisha Forest Department (Similipal South Division, Special Tiger Protection Force, Similipal Wildlife Intelligence Network)", "location": "Similipal Tiger Reserve, Mayurbhanj, India", "domain": "Conservation and biodiversity", "segment": "Anti-poaching enforcement", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-02-20-fae6" }, "ratings": [ { "group": "personal", "depth": null, "reason": "Not assessed: no specific injury to rangers avoided is documented. The reports describe arrests and seizures." }, { "group": "organizational", "depth": 2, "reason": "The reserve's Field Director states the detection and 24-hour operation averted a major poaching incident. It yielded nine guns and more than 20 bows and arrows." }, { "group": "societal", "depth": 3, "reason": "Armed groups were intercepted inside a tiger reserve more than once. Nine guns and more than 20 bows and arrows were seized. That is material to the reserve's wildlife." } ], "ai_role": { "status": "assessed", "statement": "The AI cameras' real-time detection of the armed group is the stated trigger for the mobilization. That operation ended in surrender and seizure. The park authority's statement is the Field Director's on-record account, quoted by The New Indian Express and the Times of India. The court remands are reported in the press by PTI, Hans India and Ommcom. The cited account lacks corroboration from an available official press note or court order. The account rests on press-quoted forest-department statements." }, "evidence": [ { "kind": "news report", "citation": "The New Indian Express (2026). 39 persons arrested as AI cameras foil major poaching bid in Similipal.", "title": "39 persons arrested as AI cameras foil major poaching bid in Similipal", "url": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal", "date": "2026-02-20", "written_by": "independent", "quote": "AI-enabled surveillance cameras detected a group of armed intruders entering the sanctuary late on Wednesday night." }, { "kind": "news report", "citation": "The Times of India (2026). AI camera footage helps nab 5 armed poachers in Similipal.", "title": "AI camera footage helps nab 5 armed poachers in Similipal", "url": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms", "date": "2026-04-12", "written_by": "independent", "quote": "The accused were taken into custody after the raid and were later produced before the court" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park.", "title": "AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park", "url": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "date": "2026-09-24", "written_by": "independent", "quote": "The poachers were sent to jail custody after their bail petition was rejected by a local court on Wednesday" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps nab four poachers in Similipal Tiger Reserve.", "title": "AI-enabled camera helps nab four poachers in Similipal Tiger Reserve", "url": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/", "date": "2026-07-24", "written_by": "independent", "quote": "remanded to judicial custody after their bail pleas were rejected" }, { "kind": "news report", "citation": "Sambad English (2026). Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary.", "title": "Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary in Odisha's Mayurbhanj, hunting bid foiled", "url": "https://sambadenglish.com/latest-news/four-poachers-arrested-for-illegal-entry-into-similipal-wildlife-sanctuary-in-odishas-mayurbhanj-hunting-bid-foiled-12193080", "date": "2026-07-24", "written_by": "independent", "quote": "the suspects were detected by AI-enabled TrailGuard cameras installed inside the protected forest" }, { "kind": "news report", "citation": "The Hans India (2026). AI camera helps nab four poachers in Similipal.", "title": "AI camera helps nab four poachers in Similipal", "url": "https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "date": "2026-07-27", "written_by": "independent", "quote": "a case has been registered against the accused at the Podadiha Wildlife Range" }, { "kind": "news report", "citation": "Ommcom News (2025). AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary.", "title": "AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary", "url": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/", "date": "2025-05-09", "written_by": "independent", "quote": "forwarded to the Chief Judicial Magistrate (CJM) court in Baripada and have been sent to judicial custody after their bail was rejected" } ], "replication": { "produced_by": [ "Dense camera coverage of entry trails (about 220 units)", "On-device classification that transmits only human detections", "A staffed control room and armed response units able to deploy within hours", "Prosecution follow-through under the Arms Act and wildlife law" ], "must_hold": [ "Cameras remain concealed and powered", "Alerts reach field teams in near real time", "Response teams can reach remote detection points", "Courts continue to accept camera evidence" ], "echoes": [ { "date": "2025-05-09", "setting": "Three poachers tracked via TrailGuard photographs, Baripada CJM court", "model_version": null, "outcome": "recurred", "source": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/" }, { "date": "2026-04-11", "setting": "Five armed poachers arrested after AI camera capture and house raid, Katuria village", "model_version": null, "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms" }, { "date": "2026-07-24", "setting": "Four poachers apprehended before any wildlife harmed, Podadiha range", "model_version": null, "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/" }, { "date": "2026-09-23", "setting": "Two armed poachers detected separately and jailed after bail refused", "model_version": null, "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" } ] }, "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 anti-poaching cameras at Ezemvelo KZN Wildlife missed poachers who killed four rhinos; TrailGuard AI cameras perform the same task at Similipal." }, "headline": "TrailGuard AI alerts lead to arrest of 39 poachers in Similipal", "metric": "at least 39 poachers surrendered", "comparator": null, "later": { "status": "persisted", "date": "2026-09-24", "note": "The camera network kept producing arrests after February 2026, with armed poachers detected and jailed in April, July and September 2026, and the reserve ran over 220 cameras by mid-2026 with expansion planned.", "quote": "The poachers were sent to jail custody after their bail petition was rejected by a local court on Wednesday, the forest officials said, adding that they were detected by a secret AI camera deployed inside Similipal.", "url": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" }, "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 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Set developer to null because none of the seven cited sources names RESOLVE or any maker of the TrailGuard cameras, limited the bail refusals to the July and September 2026 groups because the Times of India report on the April arrests says only that the accused were produced before the court, and added the Times of India's lower camera count (50 in use, up from 10, with 300 planned) beside the Field Director's “around 220”.", "source": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal; https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms; https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Linked the OECD AI Incidents entry, which describes this event.", "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 from the cited sources.", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "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/Similipal_National_Park", "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: seven independent news reports. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0102", "catalog_version": "1.0.0", "source_fingerprint": "f2fa74ac6313c0122d14f5e42a6b8291016292e413e3cd4aa0f3079b3de0bd75", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0102-source-0001", "AIBD-0102-source-0002", "AIBD-0102-source-0003", "AIBD-0102-source-0004", "AIBD-0102-source-0005", "AIBD-0102-source-0006", "AIBD-0102-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": "Similipal Tiger Reserve, Odisha Forest Department (Similipal South Division, Special Tiger Protection Force, Similipal Wildlife Intelligence Network)" } }, "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 cameras' real-time detection of the armed group is the stated trigger for the mobilization. That operation ended in surrender and seizure. The park authority's statement is the Field Director's on-record account, quoted by The New Indian Express and the Times of India. The court remands are reported in the press by PTI, Hans India and Ommcom. The cited account lacks corroboration from an available official press note or court order. The account rests on press-quoted forest-department statements." } } }, "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 cameras' real-time detection of the armed group is the stated trigger for the mobilization. That operation ended in surrender and seizure. The park authority's statement is the Field Director's on-record account, quoted by The New Indian Express and the Times of India. The court remands are reported in the press by PTI, Hans India and Ommcom. The cited account lacks corroboration from an available official press note or court order. The account rests on press-quoted forest-department statements." } } }, "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": "Not assessed: no specific injury to rangers avoided is documented. The reports describe arrests and seizures." }, { "group": "organizational", "depth": 2, "reason": "The reserve's Field Director states the detection and 24-hour operation averted a major poaching incident. It yielded nine guns and more than 20 bows and arrows." }, { "group": "societal", "depth": 3, "reason": "Armed groups were intercepted inside a tiger reserve more than once. Nine guns and more than 20 bows and arrows were seized. That is material to the reserve's wildlife." } ] } }, "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: seven 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": "Not assessed: no specific injury to rangers avoided is documented. The reports describe arrests and seizures." }, { "group": "organizational", "depth": 2, "reason": "The reserve's Field Director states the detection and 24-hour operation averted a major poaching incident. It yielded nine guns and more than 20 bows and arrows." }, { "group": "societal", "depth": 3, "reason": "Armed groups were intercepted inside a tiger reserve more than once. Nine guns and more than 20 bows and arrows were seized. That is material to the reserve's wildlife." } ] } }, "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": "Not assessed: no specific injury to rangers avoided is documented. The reports describe arrests and seizures." }, { "group": "organizational", "depth": 2, "reason": "The reserve's Field Director states the detection and 24-hour operation averted a major poaching incident. It yielded nine guns and more than 20 bows and arrows." }, { "group": "societal", "depth": 3, "reason": "Armed groups were intercepted inside a tiger reserve more than once. Nine guns and more than 20 bows and arrows were seized. That is material to the reserve's wildlife." } ] } }, "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": "AI anti-poaching cameras at Ezemvelo KZN Wildlife missed poachers who killed four rhinos; TrailGuard AI cameras perform the same task at Similipal.", "links": [ { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": 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"value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2026-09-24", "note": "The camera network kept producing arrests after February 2026, with armed poachers detected and jailed in April, July and September 2026, and the reserve ran over 220 cameras by mid-2026 with expansion planned.", "quote": "The poachers were sent to jail custody after their bail petition was rejected by a local court on Wednesday, the forest officials said, adding that they were detected by a secret AI camera deployed inside Similipal.", "url": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" } } }, "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-0102" } }, "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": "Similipal Tiger Reserve in Mayurbhanj district, Odisha, runs a network of about 220 TrailGuard AI trail cameras. The cameras classify humans, animals and vehicles and send images to a control room. Late on Wednesday 18 February 2026 the cameras detected a group of armed intruders entering the reserve. Field Director Prakash Chand Gogineni said the real-time alerts mobilized the Similipal South Division, three ranges and three Special Tiger Protection Force units. After about 24 hours of combing, at least 39 poachers surrendered. Nine guns, gunpowder and more than 20 bows and arrows were recovered. A case was registered at Jenabil Wildlife Range. Later detections on 11 April, around 24 July and 23 September 2026 led to arrests. Courts refused bail to the July and September groups. The camera count varies by source. The Field Director gave “around 220” in February 2026 and The Hans India “over 220” in July 2026. The Times of India in April 2026 reported 50 AI cameras in use, up from an initial 10, with 300 planned.", "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": "Not assessed: no specific injury to rangers avoided is documented. The reports describe arrests and seizures." }, { "group": "organizational", "depth": 2, "reason": "The reserve's Field Director states the detection and 24-hour operation averted a major poaching incident. It yielded nine guns and more than 20 bows and arrows." }, { "group": "societal", "depth": 3, "reason": "Armed groups were intercepted inside a tiger reserve more than once. Nine guns and more than 20 bows and arrows were seized. That is material to the reserve's wildlife." } ] } }, "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 human intruders and poachers entering the reserve in near real time so rangers can intercept them before wildlife is killed." } }, "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": "Similipal Tiger Reserve, Mayurbhanj, 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": "Detect human intruders and poachers entering the reserve in near real time so rangers can intercept them before wildlife is killed." } }, "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": "2026-02-18", "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": "2026-10-20", "change": "Set developer to null because none of the seven cited sources names RESOLVE or any maker of the TrailGuard cameras, limited the bail refusals to the July and September 2026 groups because the Times of India report on the April arrests says only that the accused were produced before the court, and added the Times of India's lower camera count (50 in use, up from 10, with 300 planned) beside the Field Director's “around 220”.", "source": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal; https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms; https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "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 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 from the cited sources.", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "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/Similipal_National_Park", "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": [ "Dense camera coverage of entry trails (about 220 units)", "On-device classification that transmits only human detections", "A staffed control room and armed response units able to deploy within hours", "Prosecution follow-through under the Arms Act and wildlife law" ], "must_hold": [ "Cameras remain concealed and powered", "Alerts reach field teams in near real time", "Response teams can reach remote detection points", "Courts continue to accept camera evidence" ], "echoes": [ { "date": "2025-05-09", "setting": "Three poachers tracked via TrailGuard photographs, Baripada CJM court", "outcome": "recurred", "source": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/" }, { "date": "2026-04-11", "setting": "Five armed poachers arrested after AI camera capture and house raid, Katuria village", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms" }, { "date": "2026-07-24", "setting": "Four poachers apprehended before any wildlife harmed, Podadiha range", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/" }, { "date": "2026-09-23", "setting": "Two armed poachers detected separately and jailed after bail refused", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" } ] } } }, "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 camera coverage of entry trails (about 220 units)", "On-device classification that transmits only human detections", "A staffed control room and armed response units able to deploy within hours", "Prosecution follow-through under the Arms Act and wildlife law" ], "must_hold": [ "Cameras remain concealed and powered", "Alerts reach field teams in near real time", "Response teams can reach remote detection points", "Courts continue to accept camera evidence" ], "echoes": [ { "date": "2025-05-09", "setting": "Three poachers tracked via TrailGuard photographs, Baripada CJM court", "outcome": "recurred", "source": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/" }, { "date": "2026-04-11", "setting": "Five armed poachers arrested after AI camera capture and house raid, Katuria village", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms" }, { "date": "2026-07-24", "setting": "Four poachers apprehended before any wildlife harmed, Podadiha range", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/" }, { "date": "2026-09-23", "setting": "Two armed poachers detected separately and jailed after bail refused", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" } ] } } }, "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": "2026-10-20", "change": "Set developer to null because none of the seven cited sources names RESOLVE or any maker of the TrailGuard cameras, limited the bail refusals to the July and September 2026 groups because the Times of India report on the April arrests says only that the accused were produced before the court, and added the Times of India's lower camera count (50 in use, up from 10, with 300 planned) beside the Field Director's “around 220”.", "source": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal; https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms; https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "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 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 from the cited sources.", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "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/Similipal_National_Park", "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": "Conservation and biodiversity", "segment": "Anti-poaching 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": "news report", "citation": "The New Indian Express (2026). 39 persons arrested as AI cameras foil major poaching bid in Similipal.", "title": "39 persons arrested as AI cameras foil major poaching bid in Similipal", "url": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal", "date": "2026-02-20", "written_by": "independent", "quote": "AI-enabled surveillance cameras detected a group of armed intruders entering the sanctuary late on Wednesday night." }, { "kind": "news report", "citation": "The Times of India (2026). AI camera footage helps nab 5 armed poachers in Similipal.", "title": "AI camera footage helps nab 5 armed poachers in Similipal", "url": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms", "date": "2026-04-12", "written_by": "independent", "quote": "The accused were taken into custody after the raid and were later produced before the court" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park.", "title": "AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park", "url": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "date": "2026-09-24", "written_by": "independent", "quote": "The poachers were sent to jail custody after their bail petition was rejected by a local court on Wednesday" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps nab four poachers in Similipal Tiger Reserve.", "title": "AI-enabled camera helps nab four poachers in Similipal Tiger Reserve", "url": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/", "date": "2026-07-24", "written_by": "independent", "quote": "remanded to judicial custody after their bail pleas were rejected" }, { "kind": "news report", "citation": "Sambad English (2026). Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary.", "title": "Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary in Odisha's Mayurbhanj, hunting bid foiled", "url": "https://sambadenglish.com/latest-news/four-poachers-arrested-for-illegal-entry-into-similipal-wildlife-sanctuary-in-odishas-mayurbhanj-hunting-bid-foiled-12193080", "date": "2026-07-24", "written_by": "independent", "quote": "the suspects were detected by AI-enabled TrailGuard cameras installed inside the protected forest" }, { "kind": "news report", "citation": "The Hans India (2026). AI camera helps nab four poachers in Similipal.", "title": "AI camera helps nab four poachers in Similipal", "url": "https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "date": "2026-07-27", "written_by": "independent", "quote": "a case has been registered against the accused at the Podadiha Wildlife Range" }, { "kind": "news report", "citation": "Ommcom News (2025). AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary.", "title": "AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary", "url": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/", "date": "2025-05-09", "written_by": "independent", "quote": "forwarded to the Chief Judicial Magistrate (CJM) court in Baripada and have been sent to judicial custody after their bail was rejected" } ] } }, "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-0102-source-0001", "AIBD-0102-source-0002", "AIBD-0102-source-0003", "AIBD-0102-source-0004", "AIBD-0102-source-0005", "AIBD-0102-source-0006", "AIBD-0102-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": "The New Indian Express (2026). 39 persons arrested as AI cameras foil major poaching bid in Similipal.", "title": "39 persons arrested as AI cameras foil major poaching bid in Similipal", "url": "https://www.newindianexpress.com/states/odisha/2026/Feb/20/39-persons-arrested-as-ai-cameras-foil-major-poaching-bid-in-similipal", "date": "2026-02-20", "written_by": "independent", "quote": "AI-enabled surveillance cameras detected a group of armed intruders entering the sanctuary late on Wednesday night." }, { "kind": "news report", "citation": "The Times of India (2026). AI camera footage helps nab 5 armed poachers in Similipal.", "title": "AI camera footage helps nab 5 armed poachers in Similipal", "url": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms", "date": "2026-04-12", "written_by": "independent", "quote": "The accused were taken into custody after the raid and were later produced before the court" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park.", "title": "AI-enabled camera helps Forest dept to nab two poachers in Similipal National Park", "url": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/", "date": "2026-09-24", "written_by": "independent", "quote": "The poachers were sent to jail custody after their bail petition was rejected by a local court on Wednesday" }, { "kind": "news report", "citation": "PTI via ThePrint (2026). AI-enabled camera helps nab four poachers in Similipal Tiger Reserve.", "title": "AI-enabled camera helps nab four poachers in Similipal Tiger Reserve", "url": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/", "date": "2026-07-24", "written_by": "independent", "quote": "remanded to judicial custody after their bail pleas were rejected" }, { "kind": "news report", "citation": "Sambad English (2026). Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary.", "title": "Four poachers arrested for illegal entry into Similipal Wildlife Sanctuary in Odisha's Mayurbhanj, hunting bid foiled", "url": "https://sambadenglish.com/latest-news/four-poachers-arrested-for-illegal-entry-into-similipal-wildlife-sanctuary-in-odishas-mayurbhanj-hunting-bid-foiled-12193080", "date": "2026-07-24", "written_by": "independent", "quote": "the suspects were detected by AI-enabled TrailGuard cameras installed inside the protected forest" }, { "kind": "news report", "citation": "The Hans India (2026). AI camera helps nab four poachers in Similipal.", "title": "AI camera helps nab four poachers in Similipal", "url": "https://www.thehansindia.com/news/national/ai-camera-helps-nab-four-poachers-in-similipal-1101741", "date": "2026-07-27", "written_by": "independent", "quote": "a case has been registered against the accused at the Podadiha Wildlife Range" }, { "kind": "news report", "citation": "Ommcom News (2025). AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary.", "title": "AI TrailGuard cameras help nab three poachers in Similipal Wildlife Sanctuary", "url": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/", "date": "2025-05-09", "written_by": "independent", "quote": "forwarded to the Chief Judicial Magistrate (CJM) court in Baripada and have been sent to judicial custody after their bail was rejected" } ] } }, "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 trail cameras detected armed intruders on 18 February 2026; STR Field Director reports 39 surrendered, nine guns seized, case registered.", "account": "Similipal Tiger Reserve in Mayurbhanj district, Odisha, runs a network of about 220 TrailGuard AI trail cameras. The cameras classify humans, animals and vehicles and send images to a control room. Late on Wednesday 18 February 2026 the cameras detected a group of armed intruders entering the reserve. Field Director Prakash Chand Gogineni said the real-time alerts mobilized the Similipal South Division, three ranges and three Special Tiger Protection Force units. After about 24 hours of combing, at least 39 poachers surrendered. Nine guns, gunpowder and more than 20 bows and arrows were recovered. A case was registered at Jenabil Wildlife Range. Later detections on 11 April, around 24 July and 23 September 2026 led to arrests. Courts refused bail to the July and September groups. The camera count varies by source. The Field Director gave “around 220” in February 2026 and The Hans India “over 220” in July 2026. The Times of India in April 2026 reported 50 AI cameras in use, up from an initial 10, with 300 planned." } }, "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 network (about 220 units) with embedded human/animal/vehicle 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": "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": "TrailGuard AI cameras trigger arrest of 39 armed poachers in Similipal Tiger Reserve", "headline": "TrailGuard AI alerts lead to arrest of 39 poachers in Similipal" } }, "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": [ "f2fa74ac6313c0122d14f5e42a6b8291016292e413e3cd4aa0f3079b3de0bd75" ], "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": "Similipal Tiger Reserve in Mayurbhanj district, Odisha, runs a network of about 220 TrailGuard AI trail cameras. The cameras classify humans, animals and vehicles and send images to a control room. Late on Wednesday 18 February 2026 the cameras detected a group of armed intruders entering the reserve. Field Director Prakash Chand Gogineni said the real-time alerts mobilized the Similipal South Division, three ranges and three Special Tiger Protection Force units. After about 24 hours of combing, at least 39 poachers surrendered. Nine guns, gunpowder and more than 20 bows and arrows were recovered. A case was registered at Jenabil Wildlife Range. Later detections on 11 April, around 24 July and 23 September 2026 led to arrests. Courts refused bail to the July and September groups. The camera count varies by source. The Field Director gave “around 220” in February 2026 and The Hans India “over 220” in July 2026. The Times of India in April 2026 reported 50 AI cameras in use, up from an initial 10, with 300 planned.", "replication": { "produced_by": [ "Dense camera coverage of entry trails (about 220 units)", "On-device classification that transmits only human detections", "A staffed control room and armed response units able to deploy within hours", "Prosecution follow-through under the Arms Act and wildlife law" ], "must_hold": [ "Cameras remain concealed and powered", "Alerts reach field teams in near real time", "Response teams can reach remote detection points", "Courts continue to accept camera evidence" ], "echoes": [ { "date": "2025-05-09", "setting": "Three poachers tracked via TrailGuard photographs, Baripada CJM court", "outcome": "recurred", "source": "https://ommcomnews.com/odisha-news/ai-trailguard-cameras-help-nab-three-poachers-in-similipal-wildlife-sanctuary/" }, { "date": "2026-04-11", "setting": "Five armed poachers arrested after AI camera capture and house raid, Katuria village", "outcome": "recurred", "source": "https://timesofindia.indiatimes.com/city/bhubaneswar/ai-camera-footage-helps-nab-5-armed-poachers-in-similipal/articleshow/130197981.cms" }, { "date": "2026-07-24", "setting": "Four poachers apprehended before any wildlife harmed, Podadiha range", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-nab-four-poachers-in-similipal-tiger-reserve/2996083/" }, { "date": "2026-09-23", "setting": "Two armed poachers detected separately and jailed after bail refused", "outcome": "recurred", "source": "https://theprint.in/india/ai-enabled-camera-helps-forest-dept-to-nab-two-poachers-in-similipal-national-park/3052132/" } ] } } }, "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-0157", "case_url": "records/AIBD-0157.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Everything known about what the system did comes from Vohra's post. FSD (Supervised) was engaged, and the driver monitoring system registered that he had lost consciousness. The truck slowed, put on its hazards and pulled onto the shoulder. After responders had attended to him, it drove him to the emergency room. The post carries a video that The American Bazaar describes as showing emergency services responding, not the pull-over itself. Tesla has published no vehicle data or statement. Its account replied with a heart and Elon Musk wrote “Glad you're ok!”. Those replies acknowledge the outcome, not the mechanism. No police, emergency-service or hospital record, freeway location or FSD version has been published." }, { "label": "Comparison", "value": "a manually driven vehicle whose driver loses consciousness", "url": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/" }, { "label": "Before", "value": "would have likely crashed or caused a pileup" }, { "label": "After", "value": "immediately slowed, activated hazards, and safely pulled over to the shoulder" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0157", "title": "Rishi Vohra's Tesla Cybertruck on FSD (Supervised) may have detected that he had passed out on a freeway, slowed, put on its hazards and pulled onto the shoulder, then driven him to the ER (18 February 2026)", "summary": "Rishi Vohra says FSD pulled his Cybertruck onto a freeway shoulder after he passed out, 18 February 2026; Musk replied, no data published.", "date_observed": "2026-02-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In an X post of 19 February 2026, Rishi Vohra wrote that he passed out while driving on 18 February. He was on a freeway in his Tesla Cybertruck, mid-call with his wife. He had passed out after a severe allergic reaction, following a 17-hour unintentional fast and medicine. Full Self-Driving (Supervised) was engaged, he wrote. Its driver monitoring system detected his loss of consciousness. The truck slowed, put on hazards and pulled onto the shoulder without a crash. His wife used Life360 to send emergency services, who reached him in five minutes. The car drove him to an emergency room, where he was stabilized overnight and discharged on 19 February. Musk replied “Glad you're ok!”. No vehicle data or official confirmation has been published.", "category": "protective response", "unexpected": false, "purpose": "Tesla sells Full Self-Driving (Supervised) as driver assistance that still requires driver supervision, as Storyboard18 notes. Its cabin driver-monitoring system watches the driver. Vohra credits that monitoring with detecting his loss of consciousness. He credits the system with slowing, putting on hazards and pulling onto the shoulder. Keeping the car out of a crash is what the system is built to do. Tesla has not described whether the stop was a designed unresponsive-driver fallback. The later drive to the emergency room used the system's ordinary point-to-point driving.", "system": "Tesla Full Self-Driving (Supervised) with cabin driver monitoring, on a Tesla Cybertruck as described by the driver and Teslarati", "model_version": "Not reported; Vohra wrote only that Full Self-Driving was engaged, and Teslarati calls it FSD Supervised on a Cybertruck", "developer": "Tesla", "deployer": null, "location": null, "domain": "Road vehicle crash avoidance", "segment": "Automotive", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-02-20-66a3" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "By his account Vohra was unconscious at freeway speed and the truck stopped on the shoulder without hitting anything. It then took him to an emergency room, where he was stabilized overnight. He and the Indian outlets call it life-saving. The only source is his post. No vehicle data, police, emergency-service or hospital record has been published. The freeway, FSD version and hospital are unnamed." } ], "ai_role": { "status": "unassessed", "statement": "Everything known about what the system did comes from Vohra's post. FSD (Supervised) was engaged, and the driver monitoring system registered that he had lost consciousness. The truck slowed, put on its hazards and pulled onto the shoulder. After responders had attended to him, it drove him to the emergency room. The post carries a video that The American Bazaar describes as showing emergency services responding, not the pull-over itself. Tesla has published no vehicle data or statement. Its account replied with a heart and Elon Musk wrote “Glad you're ok!”. Those replies acknowledge the outcome, not the mechanism. No police, emergency-service or hospital record, freeway location or FSD version has been published." }, "evidence": [ { "kind": "other", "citation": "Vohra, R. (2026). Post on X, @rishivohra, 19 February 2026 (17:38 UTC), status 2024539159269167410; video and photo attached.", "title": "My Tesla literally saved my life yesterday", "url": "https://x.com/rishivohra/status/2024539159269167410", "date": "2026-02-19", "written_by": "beneficiary", "quote": "My Tesla literally saved my life yesterday. What started as a normal drive turned terrifying fast." }, { "kind": "news report", "citation": "Alvarez, S. (2026). Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency. Teslarati (enthusiast site), 20 February 2026.", "title": "Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency", "url": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "date": "2026-02-20", "written_by": "independent", "quote": "It detected I lost consciousness (thanks to the driver monitoring system), immediately slowed, activated hazards, and safely pulled over to the shoulder." }, { "kind": "news report", "citation": "Chouhan, M. S. (2026). Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive. Hindustan Times, 20 February 2026.", "title": "Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive", "url": "https://www.hindustantimes.com/trending/elon-musk-reacts-after-man-claims-tesla-saved-his-life-during-freeway-medical-emergency-life-saving-tech-101771574831376.html", "date": "2026-02-20", "written_by": "independent", "quote": "A Tesla owner has claimed that the company’s autonomous driving technology helped prevent a potentially fatal accident after he lost consciousness behind the wheel during a medical emergency." }, { "kind": "news report", "citation": "Storyboard18 (2026). Indian-origin Tesla owner says car prevented crash after he passed out, Musk says 'Glad You're Ok!'. Storyboard18, 20 February 2026.", "title": "Indian-origin Tesla owner says car prevented crash after he passed out, Musk says ‘Glad You’re Ok!’", "url": "https://www.storyboard18.com/trending/indian-origin-tesla-owner-says-car-prevented-crash-after-he-passed-out-musk-says-glad-youre-ok-90258.htm", "date": "2026-02-20", "written_by": "independent", "quote": "While Tesla’s Full Self-Driving system still requires driver supervision and remains under regulatory scrutiny in several regions" }, { "kind": "news report", "citation": "AB Wire (2026). Elon Musk responds to Tesla saving a person's life during accident. The American Bazaar, 20 February 2026 (reproduces the full text of the post).", "title": "Elon Musk responds to Tesla saving a person’s life during accident", "url": "https://americanbazaaronline.com/2026/02/20/elon-musk-responds-to-tesla-saving-a-persons-life-during-accident-475581/", "date": "2026-02-20", "written_by": "independent", "quote": "The user also posted a video of emergency services responding to the accident." }, { "kind": "press release", "citation": "Musk, E. (2026). Reply on X, @elonmusk, 19 February 2026 (18:26 UTC), status 2024551075576189357.", "title": "Glad you’re ok!", "url": "https://x.com/elonmusk/status/2024551075576189357", "date": "2026-02-19", "written_by": "makers", "quote": "Glad you’re ok! ❤️" } ], "replication": { "produced_by": [ "A Tesla Cybertruck with Full Self-Driving (Supervised) engaged on a freeway, by Vohra's account", "A cabin driver-monitoring system that, on his account, registered his loss of consciousness. The truck then slowed, put on its hazards and pulled onto the shoulder", "A wife on the phone who used Life360 to send emergency services to him within five minutes. Responders attended to him before he had the car continue to the emergency room" ], "must_hold": [ "FSD (Supervised) requires driver supervision, per Storyboard18. An unconscious driver is outside its intended operating condition. Whatever stopped the truck was a fallback, not a substitute for an attentive driver", "The account is Vohra's alone. No Tesla vehicle data, police or emergency-service record, hospital statement, freeway location, FSD version or footage of the pull-over has been published. The replies from Elon Musk and Tesla's account acknowledge the outcome without confirming the mechanism", "Vohra chose to have the car take him to the emergency room after responders had reached him. Emergency services, not the car, were the documented first response" ], "echoes": [] }, "paired_incident": null, "headline": "Tesla FSD may have pulled over after Rishi Vohra passed out", "metric": null, "comparator": { "against": "a manually driven vehicle whose driver loses consciousness", "before": "would have likely crashed or caused a pileup", "after": "immediately slowed, activated hazards, and safely pulled over to the shoulder", "basis": "assumed", "quote": "A vehicle that is manually driven would have likely crashed or caused a pileup if its driver lost consciousness in the middle of the freeway, after all.", "url": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/" }, "later": { "status": "unknown", "date": null, "note": "No vehicle data, Tesla confirmation or later development was found after the February 2026 reports.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources. Date set to 18 February 2026, the day before Vohra's post (“yesterday”), rather than the Monitor's 20 February 2026 report date.", "source": "https://oecd.ai/en/incidents/2026-02-20-66a3", "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 from the cited sources.", "source": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "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/Tesla,_Inc.", "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, the makers' press release and the beneficiary's other. 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No vehicle logs, official account, or software version confirms the mechanism.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-02-20-66a3" } } }, "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 vehicle data, Tesla confirmation or later development was found after the February 2026 reports." } } }, "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": "Emergency responders attended to the driver before the reported trip to the hospital." }, "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-0157" } }, "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": "In an X post of 19 February 2026, Rishi Vohra wrote that he passed out while driving on 18 February. He was on a freeway in his Tesla Cybertruck, mid-call with his wife. He had passed out after a severe allergic reaction, following a 17-hour unintentional fast and medicine. Full Self-Driving (Supervised) was engaged, he wrote. Its driver monitoring system detected his loss of consciousness. The truck slowed, put on hazards and pulled onto the shoulder without a crash. His wife used Life360 to send emergency services, who reached him in five minutes. The car drove him to an emergency room, where he was stabilized overnight and discharged on 19 February. Musk replied “Glad you're ok!”. No vehicle data or official confirmation has been published.", "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": 3, "reason": "By his account Vohra was unconscious at freeway speed and the truck stopped on the shoulder without hitting anything. It then took him to an emergency room, where he was stabilized overnight. He and the Indian outlets call it life-saving. The only source is his post. No vehicle data, police, emergency-service or hospital record has been published. The freeway, FSD version and hospital are unnamed." } ] } }, "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": "Tesla sells Full Self-Driving (Supervised) as driver assistance that still requires driver supervision, as Storyboard18 notes. Its cabin driver-monitoring system watches the driver. Vohra credits that monitoring with detecting his loss of consciousness. He credits the system with slowing, putting on hazards and pulling onto the shoulder. Keeping the car out of a crash is what the system is built to do. Tesla has not described whether the stop was a designed unresponsive-driver fallback. The later drive to the emergency room used the system's ordinary point-to-point driving." } }, "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": "Tesla sells Full Self-Driving (Supervised) as driver assistance that still requires driver supervision, as Storyboard18 notes. Its cabin driver-monitoring system watches the driver. Vohra credits that monitoring with detecting his loss of consciousness. He credits the system with slowing, putting on hazards and pulling onto the shoulder. Keeping the car out of a crash is what the system is built to do. Tesla has not described whether the stop was a designed unresponsive-driver fallback. The later drive to the emergency room used the system's ordinary point-to-point driving." } }, "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": "2026-02-18", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Date set to 18 February 2026, the day before Vohra's post (“yesterday”), rather than the Monitor's 20 February 2026 report date.", "source": "https://oecd.ai/en/incidents/2026-02-20-66a3", "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 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 from the cited sources.", "source": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "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/Tesla,_Inc.", "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 Tesla Cybertruck with Full Self-Driving (Supervised) engaged on a freeway, by Vohra's account", "A cabin driver-monitoring system that, on his account, registered his loss of consciousness. The truck then slowed, put on its hazards and pulled onto the shoulder", "A wife on the phone who used Life360 to send emergency services to him within five minutes. Responders attended to him before he had the car continue to the emergency room" ], "must_hold": [ "FSD (Supervised) requires driver supervision, per Storyboard18. An unconscious driver is outside its intended operating condition. Whatever stopped the truck was a fallback, not a substitute for an attentive driver", "The account is Vohra's alone. No Tesla vehicle data, police or emergency-service record, hospital statement, freeway location, FSD version or footage of the pull-over has been published. The replies from Elon Musk and Tesla's account acknowledge the outcome without confirming the mechanism", "Vohra chose to have the car take him to the emergency room after responders had reached him. Emergency services, not the car, were the documented first response" ], "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. Date set to 18 February 2026, the day before Vohra's post (“yesterday”), rather than the Monitor's 20 February 2026 report date.", "source": "https://oecd.ai/en/incidents/2026-02-20-66a3", "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 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 from the cited sources.", "source": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "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/Tesla,_Inc.", "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": "Road vehicle crash avoidance", "segment": "Automotive" } }, "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": "other", "citation": "Vohra, R. (2026). Post on X, @rishivohra, 19 February 2026 (17:38 UTC), status 2024539159269167410; video and photo attached.", "title": "My Tesla literally saved my life yesterday", "url": "https://x.com/rishivohra/status/2024539159269167410", "date": "2026-02-19", "written_by": "beneficiary", "quote": "My Tesla literally saved my life yesterday. What started as a normal drive turned terrifying fast." }, { "kind": "news report", "citation": "Alvarez, S. (2026). Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency. Teslarati (enthusiast site), 20 February 2026.", "title": "Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency", "url": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "date": "2026-02-20", "written_by": "independent", "quote": "It detected I lost consciousness (thanks to the driver monitoring system), immediately slowed, activated hazards, and safely pulled over to the shoulder." }, { "kind": "news report", "citation": "Chouhan, M. S. (2026). Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive. Hindustan Times, 20 February 2026.", "title": "Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive", "url": "https://www.hindustantimes.com/trending/elon-musk-reacts-after-man-claims-tesla-saved-his-life-during-freeway-medical-emergency-life-saving-tech-101771574831376.html", "date": "2026-02-20", "written_by": "independent", "quote": "A Tesla owner has claimed that the company’s autonomous driving technology helped prevent a potentially fatal accident after he lost consciousness behind the wheel during a medical emergency." }, { "kind": "news report", "citation": "Storyboard18 (2026). Indian-origin Tesla owner says car prevented crash after he passed out, Musk says 'Glad You're Ok!'. Storyboard18, 20 February 2026.", "title": "Indian-origin Tesla owner says car prevented crash after he passed out, Musk says ‘Glad You’re Ok!’", "url": "https://www.storyboard18.com/trending/indian-origin-tesla-owner-says-car-prevented-crash-after-he-passed-out-musk-says-glad-youre-ok-90258.htm", "date": "2026-02-20", "written_by": "independent", "quote": "While Tesla’s Full Self-Driving system still requires driver supervision and remains under regulatory scrutiny in several regions" }, { "kind": "news report", "citation": "AB Wire (2026). Elon Musk responds to Tesla saving a person's life during accident. The American Bazaar, 20 February 2026 (reproduces the full text of the post).", "title": "Elon Musk responds to Tesla saving a person’s life during accident", "url": "https://americanbazaaronline.com/2026/02/20/elon-musk-responds-to-tesla-saving-a-persons-life-during-accident-475581/", "date": "2026-02-20", "written_by": "independent", "quote": "The user also posted a video of emergency services responding to the accident." }, { "kind": "press release", "citation": "Musk, E. (2026). Reply on X, @elonmusk, 19 February 2026 (18:26 UTC), status 2024551075576189357.", "title": "Glad you’re ok!", "url": "https://x.com/elonmusk/status/2024551075576189357", "date": "2026-02-19", "written_by": "makers", "quote": "Glad you’re ok! ❤️" } ] } }, "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-0157-source-0001", "AIBD-0157-source-0002", "AIBD-0157-source-0003", "AIBD-0157-source-0004", "AIBD-0157-source-0005", "AIBD-0157-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": "other", "citation": "Vohra, R. (2026). Post on X, @rishivohra, 19 February 2026 (17:38 UTC), status 2024539159269167410; video and photo attached.", "title": "My Tesla literally saved my life yesterday", "url": "https://x.com/rishivohra/status/2024539159269167410", "date": "2026-02-19", "written_by": "beneficiary", "quote": "My Tesla literally saved my life yesterday. What started as a normal drive turned terrifying fast." }, { "kind": "news report", "citation": "Alvarez, S. (2026). Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency. Teslarati (enthusiast site), 20 February 2026.", "title": "Tesla Cybertruck owner credits FSD for saving life after freeway medical emergency", "url": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/", "date": "2026-02-20", "written_by": "independent", "quote": "It detected I lost consciousness (thanks to the driver monitoring system), immediately slowed, activated hazards, and safely pulled over to the shoulder." }, { "kind": "news report", "citation": "Chouhan, M. S. (2026). Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive. Hindustan Times, 20 February 2026.", "title": "Elon Musk responds after Indian-origin man says Tesla drove him to ER when he fainted mid-drive", "url": "https://www.hindustantimes.com/trending/elon-musk-reacts-after-man-claims-tesla-saved-his-life-during-freeway-medical-emergency-life-saving-tech-101771574831376.html", "date": "2026-02-20", "written_by": "independent", "quote": "A Tesla owner has claimed that the company’s autonomous driving technology helped prevent a potentially fatal accident after he lost consciousness behind the wheel during a medical emergency." }, { "kind": "news report", "citation": "Storyboard18 (2026). Indian-origin Tesla owner says car prevented crash after he passed out, Musk says 'Glad You're Ok!'. Storyboard18, 20 February 2026.", "title": "Indian-origin Tesla owner says car prevented crash after he passed out, Musk says ‘Glad You’re Ok!’", "url": "https://www.storyboard18.com/trending/indian-origin-tesla-owner-says-car-prevented-crash-after-he-passed-out-musk-says-glad-youre-ok-90258.htm", "date": "2026-02-20", "written_by": "independent", "quote": "While Tesla’s Full Self-Driving system still requires driver supervision and remains under regulatory scrutiny in several regions" }, { "kind": "news report", "citation": "AB Wire (2026). Elon Musk responds to Tesla saving a person's life during accident. The American Bazaar, 20 February 2026 (reproduces the full text of the post).", "title": "Elon Musk responds to Tesla saving a person’s life during accident", "url": "https://americanbazaaronline.com/2026/02/20/elon-musk-responds-to-tesla-saving-a-persons-life-during-accident-475581/", "date": "2026-02-20", "written_by": "independent", "quote": "The user also posted a video of emergency services responding to the accident." }, { "kind": "press release", "citation": "Musk, E. (2026). Reply on X, @elonmusk, 19 February 2026 (18:26 UTC), status 2024551075576189357.", "title": "Glad you’re ok!", "url": "https://x.com/elonmusk/status/2024551075576189357", "date": "2026-02-19", "written_by": "makers", "quote": "Glad you’re ok! ❤️" } ] } }, "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": "Rishi Vohra says FSD pulled his Cybertruck onto a freeway shoulder after he passed out, 18 February 2026; Musk replied, no data published.", "account": "In an X post of 19 February 2026, Rishi Vohra wrote that he passed out while driving on 18 February. He was on a freeway in his Tesla Cybertruck, mid-call with his wife. He had passed out after a severe allergic reaction, following a 17-hour unintentional fast and medicine. Full Self-Driving (Supervised) was engaged, he wrote. Its driver monitoring system detected his loss of consciousness. The truck slowed, put on hazards and pulled onto the shoulder without a crash. His wife used Life360 to send emergency services, who reached him in five minutes. The car drove him to an emergency room, where he was stabilized overnight and discharged on 19 February. Musk replied “Glad you're ok!”. No vehicle data or official confirmation has been published." } }, "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": "Vohra reported that the system detected his unresponsiveness and pulled the vehicle onto the shoulder." }, "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": "Vohra reported that Full Self-Driving was engaged. Teslarati identified the vehicle as a Cybertruck." }, "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; Vohra wrote only that Full Self-Driving was engaged, and Teslarati calls it FSD Supervised on a Cybertruck" } }, "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": "Tesla Full Self-Driving (Supervised) with cabin driver monitoring, on a Tesla Cybertruck as described by the driver and Teslarati" } }, "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": "Tesla" } }, "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": "Rishi Vohra's Tesla Cybertruck on FSD (Supervised) may have detected that he had passed out on a freeway, slowed, put on its hazards and pulled onto the shoulder, then driven him to the ER (18 February 2026)", "headline": "Tesla FSD may have pulled over after Rishi Vohra passed out" } }, "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": [ "b229eb9a17df496e65561be64cfbb0dbeedb176c955564d224818697d9727315" ], "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 an X post of 19 February 2026, Rishi Vohra wrote that he passed out while driving on 18 February. He was on a freeway in his Tesla Cybertruck, mid-call with his wife. He had passed out after a severe allergic reaction, following a 17-hour unintentional fast and medicine. Full Self-Driving (Supervised) was engaged, he wrote. Its driver monitoring system detected his loss of consciousness. The truck slowed, put on hazards and pulled onto the shoulder without a crash. His wife used Life360 to send emergency services, who reached him in five minutes. The car drove him to an emergency room, where he was stabilized overnight and discharged on 19 February. Musk replied “Glad you're ok!”. No vehicle data or official confirmation has been published.", "replication": { "produced_by": [ "A Tesla Cybertruck with Full Self-Driving (Supervised) engaged on a freeway, by Vohra's account", "A cabin driver-monitoring system that, on his account, registered his loss of consciousness. The truck then slowed, put on its hazards and pulled onto the shoulder", "A wife on the phone who used Life360 to send emergency services to him within five minutes. Responders attended to him before he had the car continue to the emergency room" ], "must_hold": [ "FSD (Supervised) requires driver supervision, per Storyboard18. An unconscious driver is outside its intended operating condition. Whatever stopped the truck was a fallback, not a substitute for an attentive driver", "The account is Vohra's alone. No Tesla vehicle data, police or emergency-service record, hospital statement, freeway location, FSD version or footage of the pull-over has been published. The replies from Elon Musk and Tesla's account acknowledge the outcome without confirming the mechanism", "Vohra chose to have the car take him to the emergency room after responders had reached him. Emergency services, not the car, were the documented first response" ], "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": 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-0156", "case_url": "records/AIBD-0156.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The system's contribution was detection and summons. The wearable ECG stream showed the heart rate climbing to 192 beats per minute. The remote central monitor identified ventricular tachycardia and raised an alarm. The attending director says that is how staff learned of it at once and reached the bedside to begin CPR within the golden time. The resuscitation itself was done by people. The counterfactual rests on the hospital's framing that general-ward patients are not under continuous observation and that deterioration can be noticed late. How long this patient would otherwise have gone unobserved is not recorded. No medical record or independent assessment has been published. The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0156", "title": "thynC remote central monitor flags ventricular tachycardia at 192 bpm in an 85-year-old on a general ward of Gwangju Hanguk Hospital; staff resuscitate the patient within the golden time (14 January 2026)", "summary": "thynC alarm flagged ventricular tachycardia in an 85-year-old on a Gwangju general ward; staff CPR restored circulation, 14 January 2026.", "date_observed": "2026-01-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 14 January 2026 at about 3:10 p.m., an 85-year-old patient's heart rate climbed to 192 beats per minute. The patient was on a general ward of Gwangju Hanguk Hospital (광주한국병원) in Gwangju, South Korea. thynC had monitored the patient since 9 January because of cardiovascular risk factors at admission. thynC's remote central monitor flagged ventricular tachycardia, a precursor sign of cardiac arrest, and alarmed. Staff who ran to the room found seizure-like movements, reduced consciousness and no spontaneous breathing. They performed cardiopulmonary resuscitation (CPR) and artificial respiration. The heartbeat and circulation returned, and the patient was moved to intensive care. Daewoong Pharmaceutical, which supplies thynC, announced the case on 11 February 2026 with statements from the hospital's directors and nursing manager.", "category": "harm prevented", "unexpected": false, "purpose": "The hospital and Daewoong Pharmaceutical describe thynC as an AI-based smart bed monitoring system. It measures electrocardiogram (ECG), heart rate and respiratory rate through wearable devices. It sends an alarm to a central monitor when an abnormal sign appears. So patients on a general ward, where staff cannot stay at every bedside, are watched in real time as in an intensive care unit. Staff can respond early to deterioration. Flagging a pre-arrest arrhythmia and summoning staff is exactly that design. Daewoong's hospital-division head describes thynC as built to recognize changes in a patient's condition early and help medical staff respond quickly.", "system": "thynC (씽크) AI-based smart bed monitoring system: wearable sensors measuring ECG, heart rate and respiratory rate in real time, with alarms sent to a remote central monitor", "model_version": null, "developer": "Not stated in the read reports, which describe thynC as the smart bed monitoring system that Daewoong Pharmaceutical supplies (자사 공급)", "deployer": "Gwangju Hanguk Hospital (광주한국병원)", "location": "Gwangju, South Korea", "domain": "Healthcare (inpatient monitoring / cardiac arrest)", "segment": "Hospital general-ward remote patient monitoring", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-02-11-8bc8" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The hospital reports that the alarm brought staff to a patient in ventricular tachycardia who had stopped breathing. It reports that CPR within the golden time restored heartbeat and circulation, and that the patient went to intensive care. The hospital calls this averting a cardiac-arrest crisis. But survival to discharge and neurological outcome are not reported. The account was released by the system's supplier. So a life saved is not established." } ], "ai_role": { "status": "assessed", "statement": "The system's contribution was detection and summons. The wearable ECG stream showed the heart rate climbing to 192 beats per minute. The remote central monitor identified ventricular tachycardia and raised an alarm. The attending director says that is how staff learned of it at once and reached the bedside to begin CPR within the golden time. The resuscitation itself was done by people. The counterfactual rests on the hospital's framing that general-ward patients are not under continuous observation and that deterioration can be noticed late. How long this patient would otherwise have gone unobserved is not recorded. No medical record or independent assessment has been published. The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026." }, "evidence": [ { "kind": "news report", "citation": "Kim D-M (2026). 대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”. IT Chosun, 11 February 2026 (reporting a Daewoong Pharmaceutical announcement of the same day).", "title": "대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”", "url": "https://it.chosun.com/news/articleView.html?idxno=2023092156866", "date": "2026-02-11", "written_by": "independent", "quote": "대웅제약은 자사 공급 스마트 병상 모니터링 시스템 ‘씽크(thynC)’가 입원 환자의 위급 상황을 실시간으로 포착해 심정지 위기를 막았다고 11일 밝혔다." }, { "kind": "news report", "citation": "Jung D-H (2026). 급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응. Asia Economy (아시아경제), 11 February 2026.", "title": "급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응", "url": "https://www.asiae.co.kr/article/2026021109383216218", "date": "2026-02-11", "written_by": "independent", "quote": "중앙 모니터 알람을 통해 심실빈맥을 즉시 확인하고 현장으로 출동해 골든타임 내 심폐소생술을 시행할 수 있었다" }, { "kind": "news report", "citation": "Lee Y-S (2026). 'AI'가 급성 심정지 감지..환자 골든타임 '선제적 대응'. Wolyo News (월요신문), 11 February 2026.", "title": "'AI'가 급성 심정지 감지...환자 골든타임 '선제적 대응'", "url": "https://www.wolyo.co.kr/news/articleView.html?idxno=308709", "date": "2026-02-11", "written_by": "independent", "quote": "지난 1월14일 오후 3시 10분경 광주한국병원 일반 병동에 입원 중이던 85세 환자의 심박수가 분당 192회까지 급격하게 상승하며" }, { "kind": "news report", "citation": "Shin D-H (2026). 대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인. PinPoint News (핀포인트뉴스), 11 February 2026.", "title": "대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인", "url": "https://www.pinpointnews.co.kr/news/articleView.html?idxno=428348", "date": "2026-02-11", "written_by": "independent", "quote": "실시간 원격 환자 모니터링 시스템이 위기 상황을 막는 데 결정적인 역할을 한다" }, { "kind": "news report", "citation": "Kim S-Y (2026). 대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응. Newspim (뉴스핌), 11 February 2026.", "title": "대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응", "url": "https://www.newspim.com/news/view/20260211000281", "date": "2026-02-11", "written_by": "independent", "quote": "씽크는 환자 상태 변화를 조기에 인지하고, 의료진의 신속한 대응을 돕기 위해 설계된 스마트 병상 모니터링 시스템" }, { "kind": "news report", "citation": "Lee S-M (2026). 대웅제약, 전남대·화순전남대병원에 씽크 도입. Asia Economy (아시아경제), 19 June 2026.", "title": "대웅제약, 전남대·화순전남대병원에 씽크 도입", "url": "https://view.asiae.co.kr/article/2026061909071730057", "date": "2026-06-19", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "thynC wearable monitoring of ECG, heart rate and respiratory rate, linked to a remote central monitor on a general ward. It started on 9 January 2026 because the patient had cardiovascular risk factors at admission", "An alarm raised the moment the heart rate climbed to 192 beats per minute and the monitor identified ventricular tachycardia", "Ward staff who went to the bedside on the alarm and performed CPR and artificial respiration within the golden time. The reports put that time at four minutes for acute cardiac arrest, citing National Fire Agency data" ], "must_hold": [ "The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026. The hospital's attending director, chief director and nursing manager are quoted. But no medical record, case report or independent review has been published", "Only the return of heartbeat and circulation and the transfer to intensive care are reported. Survival to discharge and neurological outcome are not", "The alarm shortens the time to detection, not the treatment. The benefit depends on staff who can reach the bedside and resuscitate within minutes. The nursing manager says that is hardest to guarantee at night and when staffing is thin" ], "echoes": [] }, "paired_incident": null, "headline": "thynC flags ventricular tachycardia; CPR restores circulation", "metric": "a heart rate that climbed to 192 beats per minute", "comparator": null, "later": { "status": "scaled", "date": "2026-06-19", "note": "Daewoong completed thynC installations covering 558 beds at Chonnam National University Hospital and Hwasun Chonnam National University Hospital, the largest national-university-hospital deployment so far, among several 2026 hospital rollouts.", "quote": "총 558병상 규모(전남대병원 217병상·화순전남대병원 341병상)로 진행된 이번 사업은 국립대병원 최대 규모의 씽크 구축 사례로, 호남권 상급종합병원의 스마트병동 전환을 본격화하는 계기가 될 것으로 회사는 기대한다.", "url": "https://view.asiae.co.kr/article/2026061909071730057" }, "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 OECD Monitor entry and the cited sources. Date set to 14 January 2026, the alarm as dated in every cited report, rather than the Monitor's 11 February 2026, which is the announcement date.", "source": "https://oecd.ai/en/incidents/2026-02-11-8bc8", "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 happened since from the cited sources.", "source": "https://view.asiae.co.kr/article/2026061909071730057", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.hkh.co.kr/", "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: 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-0156", "catalog_version": "1.0.0", "source_fingerprint": "a5b48e157f25e69294c2ff2ab95ef27812324613d4e0980d585a7e10d28f9541", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0156-source-0001", "AIBD-0156-source-0002", "AIBD-0156-source-0003", "AIBD-0156-source-0004", "AIBD-0156-source-0005", "AIBD-0156-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": "Not stated in the read reports, which describe thynC as the smart bed monitoring system that Daewoong Pharmaceutical supplies (자사 공급)", "deployer": "Gwangju Hanguk Hospital (광주한국병원)" } }, "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's contribution was detection and summons. The wearable ECG stream showed the heart rate climbing to 192 beats per minute. The remote central monitor identified ventricular tachycardia and raised an alarm. The attending director says that is how staff learned of it at once and reached the bedside to begin CPR within the golden time. The resuscitation itself was done by people. The counterfactual rests on the hospital's framing that general-ward patients are not under continuous observation and that deterioration can be noticed late. How long this patient would otherwise have gone unobserved is not recorded. No medical record or independent assessment has been published. The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026." } } }, "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 thynC system detected ventricular tachycardia in a wearable ECG feed and raised an alarm. The hospital reported that staff reached the bedside promptly and began CPR.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The system's contribution was detection and summons. The wearable ECG stream showed the heart rate climbing to 192 beats per minute. The remote central monitor identified ventricular tachycardia and raised an alarm. The attending director says that is how staff learned of it at once and reached the bedside to begin CPR within the golden time. The resuscitation itself was done by people. The counterfactual rests on the hospital's framing that general-ward patients are not under continuous observation and that deterioration can be noticed late. How long this patient would otherwise have gone unobserved is not recorded. No medical record or independent assessment has been published. The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026." } } }, "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 hospital reports that the alarm brought staff to a patient in ventricular tachycardia who had stopped breathing. It reports that CPR within the golden time restored heartbeat and circulation, and that the patient went to intensive care. The hospital calls this averting a cardiac-arrest crisis. But survival to discharge and neurological outcome are not reported. The account was released by the system's supplier. So a life saved 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: 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": 3, "reason": "The hospital reports that the alarm brought staff to a patient in ventricular tachycardia who had stopped breathing. It reports that CPR within the golden time restored heartbeat and circulation, and that the patient went to intensive care. The hospital calls this averting a cardiac-arrest crisis. But survival to discharge and neurological outcome are not reported. The account was released by the system's supplier. So a life saved 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", "display": { "answer": "The hospital reports that staff reached a patient who had stopped breathing. It reports that CPR restored heartbeat and circulation, and that the patient was transferred to intensive care. Later survival and neurological recovery are not reported." } }, "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 hospital reports that the alarm brought staff to a patient in ventricular tachycardia who had stopped breathing. It reports that CPR within the golden time restored heartbeat and circulation, and that the patient went to intensive care. The hospital calls this averting a cardiac-arrest crisis. But survival to discharge and neurological outcome are not reported. The account was released by the system's supplier. So a life saved 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", "display": { "answer": "The hospital reports a heart rate that reached 192 beats per minute, followed by restored heartbeat and circulation after CPR. The patient was transferred to intensive care." } }, "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": "Heart rate, rhythm, and the response that followed resuscitation." } }, "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": "Daewoong Pharmaceutical released the hospital’s account. No medical record or independent review has been published. The reports do not give survival to discharge, neurological recovery, or the delay that would have occurred without the alarm." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-02-11-8bc8" } } }, "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": "2026-06-19", "note": "Daewoong completed thynC installations covering 558 beds at Chonnam National University Hospital and Hwasun Chonnam National University Hospital, the largest national-university-hospital deployment so far, among several 2026 hospital rollouts.", "quote": "총 558병상 규모(전남대병원 217병상·화순전남대병원 341병상)로 진행된 이번 사업은 국립대병원 최대 규모의 씽크 구축 사례로, 호남권 상급종합병원의 스마트병동 전환을 본격화하는 계기가 될 것으로 회사는 기대한다.", "url": "https://view.asiae.co.kr/article/2026061909071730057" } } }, "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": "Hospital staff responded to the alarm and performed CPR." }, "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-0156" } }, "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": "An 85-year-old patient at Gwangju Hanguk Hospital." }, "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": "An 85-year-old patient at Gwangju Hanguk Hospital." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "One patient." }, "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 patient." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health" } }, "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 14 January 2026 at about 3:10 p.m., an 85-year-old patient's heart rate climbed to 192 beats per minute. The patient was on a general ward of Gwangju Hanguk Hospital (광주한국병원) in Gwangju, South Korea. thynC had monitored the patient since 9 January because of cardiovascular risk factors at admission. thynC's remote central monitor flagged ventricular tachycardia, a precursor sign of cardiac arrest, and alarmed. Staff who ran to the room found seizure-like movements, reduced consciousness and no spontaneous breathing. They performed cardiopulmonary resuscitation (CPR) and artificial respiration. The heartbeat and circulation returned, and the patient was moved to intensive care. Daewoong Pharmaceutical, which supplies thynC, announced the case on 11 February 2026 with statements from the hospital's directors and nursing manager.", "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": "Staff restored the patient’s heartbeat and circulation after an alert that prompted them to reach the bedside." } }, "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 hospital reports that the alarm brought staff to a patient in ventricular tachycardia who had stopped breathing. It reports that CPR within the golden time restored heartbeat and circulation, and that the patient went to intensive care. The hospital calls this averting a cardiac-arrest crisis. But survival to discharge and neurological outcome are not reported. The account was released by the system's supplier. So a life saved 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": "The hospital and Daewoong Pharmaceutical describe thynC as an AI-based smart bed monitoring system. It measures electrocardiogram (ECG), heart rate and respiratory rate through wearable devices. It sends an alarm to a central monitor when an abnormal sign appears. So patients on a general ward, where staff cannot stay at every bedside, are watched in real time as in an intensive care unit. Staff can respond early to deterioration. Flagging a pre-arrest arrhythmia and summoning staff is exactly that design. Daewoong's hospital-division head describes thynC as built to recognize changes in a patient's condition early and help medical staff respond quickly." } }, "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": "Gwangju, South Korea", "deployer_country": "South Korea" } }, "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 hospital and Daewoong Pharmaceutical describe thynC as an AI-based smart bed monitoring system. It measures electrocardiogram (ECG), heart rate and respiratory rate through wearable devices. It sends an alarm to a central monitor when an abnormal sign appears. So patients on a general ward, where staff cannot stay at every bedside, are watched in real time as in an intensive care unit. Staff can respond early to deterioration. Flagging a pre-arrest arrhythmia and summoning staff is exactly that design. Daewoong's hospital-division head describes thynC as built to recognize changes in a patient's condition early and help medical staff respond quickly." } }, "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": "2026-01-14", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Date set to 14 January 2026, the alarm as dated in every cited report, rather than the Monitor's 11 February 2026, which is the announcement date.", "source": "https://oecd.ai/en/incidents/2026-02-11-8bc8", "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 happened since from the cited sources.", "source": "https://view.asiae.co.kr/article/2026061909071730057", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.hkh.co.kr/", "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": [ "thynC wearable monitoring of ECG, heart rate and respiratory rate, linked to a remote central monitor on a general ward. It started on 9 January 2026 because the patient had cardiovascular risk factors at admission", "An alarm raised the moment the heart rate climbed to 192 beats per minute and the monitor identified ventricular tachycardia", "Ward staff who went to the bedside on the alarm and performed CPR and artificial respiration within the golden time. The reports put that time at four minutes for acute cardiac arrest, citing National Fire Agency data" ], "must_hold": [ "The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026. The hospital's attending director, chief director and nursing manager are quoted. But no medical record, case report or independent review has been published", "Only the return of heartbeat and circulation and the transfer to intensive care are reported. Survival to discharge and neurological outcome are not", "The alarm shortens the time to detection, not the treatment. The benefit depends on staff who can reach the bedside and resuscitate within minutes. The nursing manager says that is hardest to guarantee at night and when staffing is thin" ], "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": "Staff must receive the alert and reach the patient within minutes. The nursing manager said that prompt response was harder to ensure at night or with fewer staff." } }, "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. Date set to 14 January 2026, the alarm as dated in every cited report, rather than the Monitor's 11 February 2026, which is the announcement date.", "source": "https://oecd.ai/en/incidents/2026-02-11-8bc8", "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 happened since from the cited sources.", "source": "https://view.asiae.co.kr/article/2026061909071730057", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://www.hkh.co.kr/", "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": "Healthcare (inpatient monitoring / cardiac arrest)", "segment": "Hospital general-ward remote patient monitoring" } }, "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": "Kim D-M (2026). 대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”. IT Chosun, 11 February 2026 (reporting a Daewoong Pharmaceutical announcement of the same day).", "title": "대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”", "url": "https://it.chosun.com/news/articleView.html?idxno=2023092156866", "date": "2026-02-11", "written_by": "independent", "quote": "대웅제약은 자사 공급 스마트 병상 모니터링 시스템 ‘씽크(thynC)’가 입원 환자의 위급 상황을 실시간으로 포착해 심정지 위기를 막았다고 11일 밝혔다." }, { "kind": "news report", "citation": "Jung D-H (2026). 급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응. Asia Economy (아시아경제), 11 February 2026.", "title": "급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응", "url": "https://www.asiae.co.kr/article/2026021109383216218", "date": "2026-02-11", "written_by": "independent", "quote": "중앙 모니터 알람을 통해 심실빈맥을 즉시 확인하고 현장으로 출동해 골든타임 내 심폐소생술을 시행할 수 있었다" }, { "kind": "news report", "citation": "Lee Y-S (2026). 'AI'가 급성 심정지 감지..환자 골든타임 '선제적 대응'. Wolyo News (월요신문), 11 February 2026.", "title": "'AI'가 급성 심정지 감지...환자 골든타임 '선제적 대응'", "url": "https://www.wolyo.co.kr/news/articleView.html?idxno=308709", "date": "2026-02-11", "written_by": "independent", "quote": "지난 1월14일 오후 3시 10분경 광주한국병원 일반 병동에 입원 중이던 85세 환자의 심박수가 분당 192회까지 급격하게 상승하며" }, { "kind": "news report", "citation": "Shin D-H (2026). 대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인. PinPoint News (핀포인트뉴스), 11 February 2026.", "title": "대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인", "url": "https://www.pinpointnews.co.kr/news/articleView.html?idxno=428348", "date": "2026-02-11", "written_by": "independent", "quote": "실시간 원격 환자 모니터링 시스템이 위기 상황을 막는 데 결정적인 역할을 한다" }, { "kind": "news report", "citation": "Kim S-Y (2026). 대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응. Newspim (뉴스핌), 11 February 2026.", "title": "대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응", "url": "https://www.newspim.com/news/view/20260211000281", "date": "2026-02-11", "written_by": "independent", "quote": "씽크는 환자 상태 변화를 조기에 인지하고, 의료진의 신속한 대응을 돕기 위해 설계된 스마트 병상 모니터링 시스템" }, { "kind": "news report", "citation": "Lee S-M (2026). 대웅제약, 전남대·화순전남대병원에 씽크 도입. Asia Economy (아시아경제), 19 June 2026.", "title": "대웅제약, 전남대·화순전남대병원에 씽크 도입", "url": "https://view.asiae.co.kr/article/2026061909071730057", "date": "2026-06-19", "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-0156-source-0001", "AIBD-0156-source-0002", "AIBD-0156-source-0003", "AIBD-0156-source-0004", "AIBD-0156-source-0005", "AIBD-0156-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": "Kim D-M (2026). 대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”. IT Chosun, 11 February 2026 (reporting a Daewoong Pharmaceutical announcement of the same day).", "title": "대웅제약 ‘씽크’, 급성 심정지 막았다… “AI가 골든타임 포착”", "url": "https://it.chosun.com/news/articleView.html?idxno=2023092156866", "date": "2026-02-11", "written_by": "independent", "quote": "대웅제약은 자사 공급 스마트 병상 모니터링 시스템 ‘씽크(thynC)’가 입원 환자의 위급 상황을 실시간으로 포착해 심정지 위기를 막았다고 11일 밝혔다." }, { "kind": "news report", "citation": "Jung D-H (2026). 급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응. Asia Economy (아시아경제), 11 February 2026.", "title": "급성 심정지, AI가 전조 포착… 광주한국병원 '씽크'로 조기 대응", "url": "https://www.asiae.co.kr/article/2026021109383216218", "date": "2026-02-11", "written_by": "independent", "quote": "중앙 모니터 알람을 통해 심실빈맥을 즉시 확인하고 현장으로 출동해 골든타임 내 심폐소생술을 시행할 수 있었다" }, { "kind": "news report", "citation": "Lee Y-S (2026). 'AI'가 급성 심정지 감지..환자 골든타임 '선제적 대응'. Wolyo News (월요신문), 11 February 2026.", "title": "'AI'가 급성 심정지 감지...환자 골든타임 '선제적 대응'", "url": "https://www.wolyo.co.kr/news/articleView.html?idxno=308709", "date": "2026-02-11", "written_by": "independent", "quote": "지난 1월14일 오후 3시 10분경 광주한국병원 일반 병동에 입원 중이던 85세 환자의 심박수가 분당 192회까지 급격하게 상승하며" }, { "kind": "news report", "citation": "Shin D-H (2026). 대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인. PinPoint News (핀포인트뉴스), 11 February 2026.", "title": "대웅제약, 광주한국병원서 '씽크' 심정지 대응 사례 확인", "url": "https://www.pinpointnews.co.kr/news/articleView.html?idxno=428348", "date": "2026-02-11", "written_by": "independent", "quote": "실시간 원격 환자 모니터링 시스템이 위기 상황을 막는 데 결정적인 역할을 한다" }, { "kind": "news report", "citation": "Kim S-Y (2026). 대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응. Newspim (뉴스핌), 11 February 2026.", "title": "대웅제약 '씽크' 심정지 위기 막아…광주한국병원 조기 대응", "url": "https://www.newspim.com/news/view/20260211000281", "date": "2026-02-11", "written_by": "independent", "quote": "씽크는 환자 상태 변화를 조기에 인지하고, 의료진의 신속한 대응을 돕기 위해 설계된 스마트 병상 모니터링 시스템" }, { "kind": "news report", "citation": "Lee S-M (2026). 대웅제약, 전남대·화순전남대병원에 씽크 도입. Asia Economy (아시아경제), 19 June 2026.", "title": "대웅제약, 전남대·화순전남대병원에 씽크 도입", "url": "https://view.asiae.co.kr/article/2026061909071730057", "date": "2026-06-19", "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": "thynC alarm flagged ventricular tachycardia in an 85-year-old on a Gwangju general ward; staff CPR restored circulation, 14 January 2026.", "account": "On 14 January 2026 at about 3:10 p.m., an 85-year-old patient's heart rate climbed to 192 beats per minute. The patient was on a general ward of Gwangju Hanguk Hospital (광주한국병원) in Gwangju, South Korea. thynC had monitored the patient since 9 January because of cardiovascular risk factors at admission. thynC's remote central monitor flagged ventricular tachycardia, a precursor sign of cardiac arrest, and alarmed. Staff who ran to the room found seizure-like movements, reduced consciousness and no spontaneous breathing. They performed cardiopulmonary resuscitation (CPR) and artificial respiration. The heartbeat and circulation returned, and the patient was moved to intensive care. Daewoong Pharmaceutical, which supplies thynC, announced the case on 11 February 2026 with statements from the hospital's directors and nursing manager." } }, "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": "thynC (씽크) AI-based smart bed monitoring system: wearable sensors measuring ECG, heart rate and respiratory rate in real time, with alarms sent to a remote central monitor" } }, "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 stated in the read reports, which describe thynC as the smart bed monitoring system that Daewoong Pharmaceutical supplies (자사 공급)" } }, "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": "thynC remote central monitor flags ventricular tachycardia at 192 bpm in an 85-year-old on a general ward of Gwangju Hanguk Hospital; staff resuscitate the patient within the golden time (14 January 2026)", "headline": "thynC flags ventricular tachycardia; CPR restores circulation" } }, "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": [ "a5b48e157f25e69294c2ff2ab95ef27812324613d4e0980d585a7e10d28f9541" ], "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 14 January 2026 at about 3:10 p.m., an 85-year-old patient's heart rate climbed to 192 beats per minute. The patient was on a general ward of Gwangju Hanguk Hospital (광주한국병원) in Gwangju, South Korea. thynC had monitored the patient since 9 January because of cardiovascular risk factors at admission. thynC's remote central monitor flagged ventricular tachycardia, a precursor sign of cardiac arrest, and alarmed. Staff who ran to the room found seizure-like movements, reduced consciousness and no spontaneous breathing. They performed cardiopulmonary resuscitation (CPR) and artificial respiration. The heartbeat and circulation returned, and the patient was moved to intensive care. Daewoong Pharmaceutical, which supplies thynC, announced the case on 11 February 2026 with statements from the hospital's directors and nursing manager.", "replication": { "produced_by": [ "thynC wearable monitoring of ECG, heart rate and respiratory rate, linked to a remote central monitor on a general ward. It started on 9 January 2026 because the patient had cardiovascular risk factors at admission", "An alarm raised the moment the heart rate climbed to 192 beats per minute and the monitor identified ventricular tachycardia", "Ward staff who went to the bedside on the alarm and performed CPR and artificial respiration within the golden time. The reports put that time at four minutes for acute cardiac arrest, citing National Fire Agency data" ], "must_hold": [ "The case reached the press through a Daewoong Pharmaceutical announcement of 11 February 2026. The hospital's attending director, chief director and nursing manager are quoted. But no medical record, case report or independent review has been published", "Only the return of heartbeat and circulation and the transfer to intensive care are reported. Survival to discharge and neurological outcome are not", "The alarm shortens the time to detection, not the treatment. The benefit depends on staff who can reach the bedside and resuscitate within minutes. The nursing manager says that is hardest to guarantee at night and when staffing is thin" ], "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": "The patient wore thynC monitors from 9 January 2026. The system alerted ward staff when the heart rate reached 192 beats per minute and identified ventricular tachycardia. Staff reached the bedside and performed CPR. The report describes that response as occurring within the time needed for resuscitation." } } }, "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-0137", "case_url": "records/AIBD-0137.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The shared conversation shows what Grok contributed. He described constant pain for six hours, worse lying flat and eased by sitting with knees to chest, with no fever. Grok ranked perforated peptic ulcer first and early or atypical appendicitis second. It called that a pattern doctors take very seriously. It urged an ER visit that night for a CT or ultrasound. Once he said the legevakt had sent him home with Somac, it scripted a request for a senior doctor and a CT scan. He told Grok afterwards that asking for the CT got him prioritized for surgery. Whether his local physician or the hospital would have imaged him without that request is unknown. TipRanks reports from the thread that he told the doctors his sister, a nurse, had suggested the scan. The Reddit post the press quoted was drafted by Grok from his description. It says he walked back to the ER, where his later comments say the physician referred him the next day." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0137", "title": "Grok may have flagged a perforated ulcer or atypical appendicitis for a 49-year-old man sent home from a Norwegian legevakt with an acid blocker, telling him to demand a CT; the scan found an appendix close to rupturing, removed on 3 December 2025", "summary": "Grok told a 49-year-old Norwegian sent home with an acid blocker to demand a CT; it found an appendix near rupture, removed 3 December 2025.", "date_observed": "2025-12-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The night before his surgery on 3 December 2025, a 49-year-old man in Norway, Reddit user u/Tykjen, had constant abdominal pain. He was examined at a legevakt (out-of-hours clinic), given the acid blocker Somac and sent home. He described the pain to xAI's Grok in a browser chat. Grok named perforated ulcer and atypical appendicitis as the likeliest causes. It told him to get computed tomography (CT) or ultrasound that night and scripted a CT request. The next day, by his account, his local physician sent him to hospital. A CT showed an appendix close to rupturing. It was removed laparoscopically and he went home on 4 December, when he posted the story. Elon Musk amplified it on 6 December.", "category": "protective response", "unexpected": false, "purpose": "Grok is xAI's general-purpose conversational assistant. Answering a description of symptoms with possible causes and advice on when to seek care is within that general use. The man had used it for a year on other topics. xAI did not build it as a medical device. The benefit was ordinary use, not an unexpected one. After the event Musk posted “Try it!” about Grok for medical questions.", "system": "Grok (xAI conversational assistant), used in the browser", "model_version": "Not reported; the man said he used the browser version on his computer and that the December 2025 chat was a fresh conversation, and the shared transcript names no model.", "developer": "xAI", "deployer": "xAI (Grok web app)", "location": "Norway", "domain": "Healthcare (emergency self-triage)", "segment": "Consumer use of a general chatbot for triage advice", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-12-05-11cb" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "An appendix the man says the CT showed close to rupturing was removed on 3 December 2025. The pain was gone and he went home the next day. He and Musk call it life-saving. That rests on his account and a ward photo. No clinician, hospital or record has spoken. A doctor commenting in the thread said severe abdominal pain in a younger patient almost always gets a CT. So life-saving is not established." } ], "ai_role": { "status": "assessed", "statement": "The shared conversation shows what Grok contributed. He described constant pain for six hours, worse lying flat and eased by sitting with knees to chest, with no fever. Grok ranked perforated peptic ulcer first and early or atypical appendicitis second. It called that a pattern doctors take very seriously. It urged an ER visit that night for a CT or ultrasound. Once he said the legevakt had sent him home with Somac, it scripted a request for a senior doctor and a CT scan. He told Grok afterwards that asking for the CT got him prioritized for surgery. Whether his local physician or the hospital would have imaged him without that request is unknown. TipRanks reports from the thread that he told the doctors his sister, a nurse, had suggested the scan. The Reddit post the press quoted was drafted by Grok from his description. It says he walked back to the ER, where his later comments say the physician referred him the next day." }, "evidence": [ { "kind": "other", "citation": "u/Tykjen (2025). How a late-night conversation with Grok got me to demand the CT scan that saved my life from a ruptured appendix (December 2025). r/grok, Reddit, 4 December 2025, 13:17 UTC; image post (a mirror selfie on a hospital ward with three dressed abdominal incisions and an IV line), 1,260 points, 179 comments; text and comments read from the Arctic Shift and PullPush archives, where the author says 'I live in Norway' (20 December 2025).", "title": "How a late-night conversation with Grok got me to demand the CT scan that saved my life from a ruptured appendix (December 2025)", "url": "https://www.reddit.com/r/grok/comments/1pdzrk1/how_a_latenight_conversation_with_grok_got_me_to/", "date": "2025-12-04", "written_by": "beneficiary", "quote": "Grok immediately flagged perforated ulcer or atypical appendicitis, told me the exact red-flag pattern I was describing, and basically said “go back right now and ask for a CT.”" }, { "kind": "other", "citation": "Tykjen and Grok (2025–2026). Managing Intense Stomach Pain Urgently. Shared Grok conversation on X, link posted by u/Tykjen in the Reddit thread on 2 January 2026 when asked for a screenshot; the transcript runs from 'intense stomach pain wont go away. tips' through the legevakt visit, the surgery report and recovery, and the man's later messages give the surgery date as 3 December; it also contains Grok's draft of the Reddit post.", "title": "Managing Intense Stomach Pain Urgently", "url": "https://x.com/i/grok/share/RNHdX8qbyao4I4oNl6TEcmuNr", "date": null, "written_by": "beneficiary", "quote": "I am worried this is a perforated ulcer. I would like to speak to the senior doctor and I need proper pain relief and a CT scan." }, { "kind": "news report", "citation": "Simon Alvarez / Teslarati (2025). Man credits Grok AI with saving his life after ER missed near-ruptured appendix. Teslarati, 5 December 2025 (the first press report, sourced to the Reddit post).", "title": "Man credits Grok AI with saving his life after ER missed near-ruptured appendix", "url": "https://www.teslarati.com/man-credits-grok-ai-with-saving-his-life-after-er-missed-near-ruptured-appendix/", "date": "2025-12-05", "written_by": "independent", "quote": "The AI flagged some of the man’s symptoms and urged him to return to the ER immediately and demand a CT scan." }, { "kind": "other", "citation": "Mark Kretschmann (@mark_k) (2025). Post reproducing the Reddit text with the ward photo, X, 5 December 2025, 08:20 UTC; 3.6 million views by 20 October 2026 (fxtwitter API). Embedded by Teslarati; the vector by which the story reached X.", "title": "Man has his life saved by Grok from @xAI after a ruptured appendix", "url": "https://x.com/mark_k/status/1996857239311634793", "date": "2025-12-05", "written_by": "independent", "quote": "Man has his life saved by Grok from @xAI after a ruptured appendix" }, { "kind": "news report", "citation": "Team Latestly / LatestLY (2025). Grok Chat Helps Save Man’s Life After ER Missed Near-Ruptured Appendix. LatestLY, 6 December 2025; the outlet's own 'TruLY Score' rates the story 2 of 5, 'Unverified'.", "title": "Grok Chat Helps Save Man’s Life After ER Missed Near-Ruptured Appendix", "url": "https://www.latestly.com/socially/social-viral/man-says-xais-grok-helped-save-his-life-after-er-missed-near-ruptured-appendix-7229363.html", "date": "2025-12-06", "written_by": "independent", "quote": "relies on a single source or posts by social media users, with no independent verification." }, { "kind": "news report", "citation": "Trond Bie / ITavisen (2025). Grok reddet nordmannens liv. ITavisen, 7 December 2025 (Norwegian technology news site; relies on the Reddit post and notes the story's spread on X).", "title": "Grok reddet nordmannens liv", "url": "https://itavisen.no/2025/12/07/nordmann-grok-reddet-livet-blindtarm/", "date": "2025-12-07", "written_by": "independent", "quote": "49 år gammel nordmann fikk livet reddet etter at AI-en Grok oppdaget en nesten sprukket blindtarm som legevakten først overså." }, { "kind": "news report", "citation": "TOI Lifestyle Desk / The Times of India (2025). 49-year-old man says Grok AI saved his life after doctors missed his appendix emergency. The Times of India, 7 December 2025.", "title": "49-year-old man says Grok AI saved his life after doctors missed his appendix emergency", "url": "https://timesofindia.indiatimes.com/etimes/trending/49-year-old-man-says-grok-ai-saved-his-life-after-doctors-missed-his-appendix-emergency/articleshow/125819004.cms", "date": "2025-12-07", "written_by": "independent", "quote": "This story is based on a user-generated Reddit post and reflects the individual’s personal account." }, { "kind": "news report", "citation": "Vince Condarcuri / TipRanks (2025). Man Says that Grok AI Saved His Life after an ER Misdiagnosis. TipRanks, 8 December 2025; syndicated on Markets Insider (Business Insider), the copy the OECD Monitor cites.", "title": "Man Says that Grok AI Saved His Life after an ER Misdiagnosis", "url": "https://www.tipranks.com/news/man-says-that-grok-ai-saved-his-life-after-an-er-misdiagnosis", "date": "2025-12-08", "written_by": "independent", "quote": "the man admitted he didn’t tell the doctors that the AI made the suggestion. Instead, he claimed that his sister, a nurse, told him to ask for the scan" }, { "kind": "press release", "citation": "Elon Musk (@elonmusk) (2026). Post quoting @cb_doge, X, 1 January 2026, 21:05 UTC, 15.7 million views (fxtwitter API). Musk had quote-posted Mario Nawfal's summary with 'Cool' on 6 December 2025 and @teslaownersSV's with 'Grok ❤️' later on 1 January 2026; the man reports a fourth share and a $40,000 crypto-community donation he forwarded to a wildlife foundation.", "title": "Try it! Last year, Grok saved the life of a man in Norway", "url": "https://x.com/elonmusk/status/2006834169637384470", "date": "2026-01-01", "written_by": "makers", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" }, { "kind": "news report", "citation": "Tanmay Puri / International Business Times UK (2026). How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts. IBTimes UK, 2 January 2026. Note: the article attributes to Musk the line about uploading X-rays or MRI images, which the fxtwitter record shows was @cb_doge's post that Musk quoted.", "title": "How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts", "url": "https://www.ibtimes.co.uk/how-grok-saved-mans-life-when-doctors-couldnt-elon-musk-reacts-1767583", "date": "2026-01-02", "written_by": "independent", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" } ], "replication": { "produced_by": [ "A patient with hours of constant, severe abdominal pain who had been examined, given an acid blocker and sent home without imaging.", "A detailed symptom description put to a general chatbot: constant pain, worse lying flat, eased by sitting with knees to chest, no fever. It matched these to surgical causes and said so without hedging.", "A second clinical contact the next day at which the patient repeated the chatbot's reasoning and asked for a CT." ], "must_hold": [ "The chatbot's differential includes the true cause. Here it ranked perforated ulcer first and atypical appendicitis second, and the second was right.", "A clinician agrees to image. The referral here came from his local physician. Whether the request changed her decision is unknown.", "Such cases are self-published. No clinician, hospital or document has confirmed this one. The first-person post was itself drafted by the chatbot. So the record stands or falls with his account and the shared transcript." ], "echoes": [] }, "paired_incident": null, "headline": "Grok tells a man sent home to demand a CT; appendix near rupture", "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": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a differential naming atypical appendicitis and a scripted demand for a CT) is documented by the man's own post and the shared Grok transcript, but the counterfactual is not shown, since his local physician referred him the next day and he told the doctors his sister had suggested the scan. The Monitor's date of 5 December is Teslarati's publication date; the surgery date, 3 December 2025, comes from the man's own message in the transcript, and the post was created 4 December 2025 13:17 UTC (Reddit archive metadata). Reddit itself blocked fetching; the post and 100 of its 179 comments were read through the Arctic Shift and PullPush archives. The Reddit post text quoted by every outlet was written by Grok as a draft at the man's request, and differs from his later comments on where the first visit was (a legevakt, not the ER) and how he got back (via his local physician). The Monitor's “Norwegian” is confirmed by the man's own comment and by ITavisen. No hospital, clinician, Norwegian health authority or record was found; the man says he filed a complaint against the first doctor. WebSearch was exhausted, so searches ran through Brave and Mojeek via curl; Hindustan Times and Haberler could not be read.", "source": "https://oecd.ai/en/incidents/2025-12-05-11cb", "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 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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/XAI_(company); https://en.wikipedia.org/wiki/XAI_(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" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: six independent news reports, one other independent source, the makers' press release 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-0137", "catalog_version": "1.0.0", "source_fingerprint": "a6e560bbfb3f986d46c6a908b58698792428a05b070d4a54b4466b020a9420d5", "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" } ], "source_ids": [ "AIBD-0137-source-0001", "AIBD-0137-source-0002", "AIBD-0137-source-0003", "AIBD-0137-source-0004", "AIBD-0137-source-0005", "AIBD-0137-source-0006", "AIBD-0137-source-0007", "AIBD-0137-source-0008", "AIBD-0137-source-0009", "AIBD-0137-source-0010" ], "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": "xAI", "deployer": "xAI (Grok web app)" } }, "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 shared conversation shows what Grok contributed. He described constant pain for six hours, worse lying flat and eased by sitting with knees to chest, with no fever. Grok ranked perforated peptic ulcer first and early or atypical appendicitis second. It called that a pattern doctors take very seriously. It urged an ER visit that night for a CT or ultrasound. Once he said the legevakt had sent him home with Somac, it scripted a request for a senior doctor and a CT scan. He told Grok afterwards that asking for the CT got him prioritized for surgery. Whether his local physician or the hospital would have imaged him without that request is unknown. TipRanks reports from the thread that he told the doctors his sister, a nurse, had suggested the scan. The Reddit post the press quoted was drafted by Grok from his description. It says he walked back to the ER, where his later comments say the physician referred him the next day." } } }, "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": "Grok recommended urgent abdominal imaging and helped the patient ask for a CT scan after he had been sent home. He reported that his request expedited surgery.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The shared conversation shows what Grok contributed. He described constant pain for six hours, worse lying flat and eased by sitting with knees to chest, with no fever. Grok ranked perforated peptic ulcer first and early or atypical appendicitis second. It called that a pattern doctors take very seriously. It urged an ER visit that night for a CT or ultrasound. Once he said the legevakt had sent him home with Somac, it scripted a request for a senior doctor and a CT scan. He told Grok afterwards that asking for the CT got him prioritized for surgery. Whether his local physician or the hospital would have imaged him without that request is unknown. TipRanks reports from the thread that he told the doctors his sister, a nurse, had suggested the scan. The Reddit post the press quoted was drafted by Grok from his description. It says he walked back to the ER, where his later comments say the physician referred him the next day." } } }, "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": "An appendix the man says the CT showed close to rupturing was removed on 3 December 2025. The pain was gone and he went home the next day. He and Musk call it life-saving. That rests on his account and a ward photo. No clinician, hospital or record has spoken. A doctor commenting in the thread said severe abdominal pain in a younger patient almost always gets a CT. So life-saving 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: six independent news reports, one other independent source, the makers' press release and two other sources from the beneficiary. 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So life-saving 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": "personal", "depth": 3, "reason": "An appendix the man says the CT showed close to rupturing was removed on 3 December 2025. The pain was gone and he went home the next day. He and Musk call it life-saving. That rests on his account and a ward photo. No clinician, hospital or record has spoken. A doctor commenting in the thread said severe abdominal pain in a younger patient almost always gets a CT. So life-saving 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": 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"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 patient’s accounts differ on whether he returned to the emergency department himself or received a referral the next day. The available evidence does not establish whether clinicians would have ordered imaging without his request.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-12-05-11cb" } } }, "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": "value_present", "value": { "kind": "text", "text": "Clinicians ordered the imaging and decided that surgery was needed." }, "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-0137" } }, "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" } }, "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 night before his surgery on 3 December 2025, a 49-year-old man in Norway, Reddit user u/Tykjen, had constant abdominal pain. He was examined at a legevakt (out-of-hours clinic), given the acid blocker Somac and sent home. He described the pain to xAI's Grok in a browser chat. Grok named perforated ulcer and atypical appendicitis as the likeliest causes. It told him to get computed tomography (CT) or ultrasound that night and scripted a CT request. The next day, by his account, his local physician sent him to hospital. A CT showed an appendix close to rupturing. It was removed laparoscopically and he went home on 4 December, when he posted the story. Elon Musk amplified it on 6 December.", "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": 3, "reason": "An appendix the man says the CT showed close to rupturing was removed on 3 December 2025. The pain was gone and he went home the next day. He and Musk call it life-saving. That rests on his account and a ward photo. No clinician, hospital or record has spoken. A doctor commenting in the thread said severe abdominal pain in a younger patient almost always gets a CT. So life-saving 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": "Grok is xAI's general-purpose conversational assistant. Answering a description of symptoms with possible causes and advice on when to seek care is within that general use. The man had used it for a year on other topics. xAI did not build it as a medical device. The benefit was ordinary use, not an unexpected one. After the event Musk posted “Try it!” about Grok for medical questions." } }, "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": "Norway", "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": "Grok is xAI's general-purpose conversational assistant. Answering a description of symptoms with possible causes and advice on when to seek care is within that general use. The man had used it for a year on other topics. xAI did not build it as a medical device. The benefit was ordinary use, not an unexpected one. After the event Musk posted “Try it!” about Grok for medical questions." } }, "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": "2025-12-03", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a differential naming atypical appendicitis and a scripted demand for a CT) is documented by the man's own post and the shared Grok transcript, but the counterfactual is not shown, since his local physician referred him the next day and he told the doctors his sister had suggested the scan. The Monitor's date of 5 December is Teslarati's publication date; the surgery date, 3 December 2025, comes from the man's own message in the transcript, and the post was created 4 December 2025 13:17 UTC (Reddit archive metadata). Reddit itself blocked fetching; the post and 100 of its 179 comments were read through the Arctic Shift and PullPush archives. The Reddit post text quoted by every outlet was written by Grok as a draft at the man's request, and differs from his later comments on where the first visit was (a legevakt, not the ER) and how he got back (via his local physician). The Monitor's “Norwegian” is confirmed by the man's own comment and by ITavisen. No hospital, clinician, Norwegian health authority or record was found; the man says he filed a complaint against the first doctor. WebSearch was exhausted, so searches ran through Brave and Mojeek via curl; Hindustan Times and Haberler could not be read.", "source": "https://oecd.ai/en/incidents/2025-12-05-11cb", "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 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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/XAI_(company); https://en.wikipedia.org/wiki/XAI_(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" } ] } }, "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 patient with hours of constant, severe abdominal pain who had been examined, given an acid blocker and sent home without imaging.", "A detailed symptom description put to a general chatbot: constant pain, worse lying flat, eased by sitting with knees to chest, no fever. It matched these to surgical causes and said so without hedging.", "A second clinical contact the next day at which the patient repeated the chatbot's reasoning and asked for a CT." ], "must_hold": [ "The chatbot's differential includes the true cause. Here it ranked perforated ulcer first and atypical appendicitis second, and the second was right.", "A clinician agrees to image. The referral here came from his local physician. Whether the request changed her decision is unknown.", "Such cases are self-published. No clinician, hospital or document has confirmed this one. The first-person post was itself drafted by the chatbot. So the record stands or falls with his account and the shared transcript." ], "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 protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a differential naming atypical appendicitis and a scripted demand for a CT) is documented by the man's own post and the shared Grok transcript, but the counterfactual is not shown, since his local physician referred him the next day and he told the doctors his sister had suggested the scan. The Monitor's date of 5 December is Teslarati's publication date; the surgery date, 3 December 2025, comes from the man's own message in the transcript, and the post was created 4 December 2025 13:17 UTC (Reddit archive metadata). Reddit itself blocked fetching; the post and 100 of its 179 comments were read through the Arctic Shift and PullPush archives. The Reddit post text quoted by every outlet was written by Grok as a draft at the man's request, and differs from his later comments on where the first visit was (a legevakt, not the ER) and how he got back (via his local physician). The Monitor's “Norwegian” is confirmed by the man's own comment and by ITavisen. No hospital, clinician, Norwegian health authority or record was found; the man says he filed a complaint against the first doctor. 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Managing Intense Stomach Pain Urgently. Shared Grok conversation on X, link posted by u/Tykjen in the Reddit thread on 2 January 2026 when asked for a screenshot; the transcript runs from 'intense stomach pain wont go away. tips' through the legevakt visit, the surgery report and recovery, and the man's later messages give the surgery date as 3 December; it also contains Grok's draft of the Reddit post.", "title": "Managing Intense Stomach Pain Urgently", "url": "https://x.com/i/grok/share/RNHdX8qbyao4I4oNl6TEcmuNr", "written_by": "beneficiary", "quote": "I am worried this is a perforated ulcer. I would like to speak to the senior doctor and I need proper pain relief and a CT scan." }, { "kind": "news report", "citation": "Simon Alvarez / Teslarati (2025). Man credits Grok AI with saving his life after ER missed near-ruptured appendix. 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LatestLY, 6 December 2025; the outlet's own 'TruLY Score' rates the story 2 of 5, 'Unverified'.", "title": "Grok Chat Helps Save Man’s Life After ER Missed Near-Ruptured Appendix", "url": "https://www.latestly.com/socially/social-viral/man-says-xais-grok-helped-save-his-life-after-er-missed-near-ruptured-appendix-7229363.html", "date": "2025-12-06", "written_by": "independent", "quote": "relies on a single source or posts by social media users, with no independent verification." }, { "kind": "news report", "citation": "Trond Bie / ITavisen (2025). Grok reddet nordmannens liv. ITavisen, 7 December 2025 (Norwegian technology news site; relies on the Reddit post and notes the story's spread on X).", "title": "Grok reddet nordmannens liv", "url": "https://itavisen.no/2025/12/07/nordmann-grok-reddet-livet-blindtarm/", "date": "2025-12-07", "written_by": "independent", "quote": "49 år gammel nordmann fikk livet reddet etter at AI-en Grok oppdaget en nesten sprukket blindtarm som legevakten først overså." }, { "kind": "news report", "citation": "TOI Lifestyle Desk / The Times of India (2025). 49-year-old man says Grok AI saved his life after doctors missed his appendix emergency. 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Last year, Grok saved the life of a man in Norway", "url": "https://x.com/elonmusk/status/2006834169637384470", "date": "2026-01-01", "written_by": "makers", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" }, { "kind": "news report", "citation": "Tanmay Puri / International Business Times UK (2026). How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts. IBTimes UK, 2 January 2026. Note: the article attributes to Musk the line about uploading X-rays or MRI images, which the fxtwitter record shows was @cb_doge's post that Musk quoted.", "title": "How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts", "url": "https://www.ibtimes.co.uk/how-grok-saved-mans-life-when-doctors-couldnt-elon-musk-reacts-1767583", "date": "2026-01-02", "written_by": "independent", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" } ] } }, "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-0137-source-0001", "AIBD-0137-source-0002", "AIBD-0137-source-0003", "AIBD-0137-source-0004", "AIBD-0137-source-0005", "AIBD-0137-source-0006", "AIBD-0137-source-0007", "AIBD-0137-source-0008", "AIBD-0137-source-0009", "AIBD-0137-source-0010" ], "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": "other", "citation": "u/Tykjen (2025). How a late-night conversation with Grok got me to demand the CT scan that saved my life from a ruptured appendix (December 2025). r/grok, Reddit, 4 December 2025, 13:17 UTC; image post (a mirror selfie on a hospital ward with three dressed abdominal incisions and an IV line), 1,260 points, 179 comments; text and comments read from the Arctic Shift and PullPush archives, where the author says 'I live in Norway' (20 December 2025).", "title": "How a late-night conversation with Grok got me to demand the CT scan that saved my life from a ruptured appendix (December 2025)", "url": "https://www.reddit.com/r/grok/comments/1pdzrk1/how_a_latenight_conversation_with_grok_got_me_to/", "date": "2025-12-04", "written_by": "beneficiary", "quote": "Grok immediately flagged perforated ulcer or atypical appendicitis, told me the exact red-flag pattern I was describing, and basically said “go back right now and ask for a CT.”" }, { "kind": "other", "citation": "Tykjen and Grok (2025–2026). Managing Intense Stomach Pain Urgently. Shared Grok conversation on X, link posted by u/Tykjen in the Reddit thread on 2 January 2026 when asked for a screenshot; the transcript runs from 'intense stomach pain wont go away. tips' through the legevakt visit, the surgery report and recovery, and the man's later messages give the surgery date as 3 December; it also contains Grok's draft of the Reddit post.", "title": "Managing Intense Stomach Pain Urgently", "url": "https://x.com/i/grok/share/RNHdX8qbyao4I4oNl6TEcmuNr", "written_by": "beneficiary", "quote": "I am worried this is a perforated ulcer. I would like to speak to the senior doctor and I need proper pain relief and a CT scan." }, { "kind": "news report", "citation": "Simon Alvarez / Teslarati (2025). Man credits Grok AI with saving his life after ER missed near-ruptured appendix. Teslarati, 5 December 2025 (the first press report, sourced to the Reddit post).", "title": "Man credits Grok AI with saving his life after ER missed near-ruptured appendix", "url": "https://www.teslarati.com/man-credits-grok-ai-with-saving-his-life-after-er-missed-near-ruptured-appendix/", "date": "2025-12-05", "written_by": "independent", "quote": "The AI flagged some of the man’s symptoms and urged him to return to the ER immediately and demand a CT scan." }, { "kind": "other", "citation": "Mark Kretschmann (@mark_k) (2025). Post reproducing the Reddit text with the ward photo, X, 5 December 2025, 08:20 UTC; 3.6 million views by 20 October 2026 (fxtwitter API). Embedded by Teslarati; the vector by which the story reached X.", "title": "Man has his life saved by Grok from @xAI after a ruptured appendix", "url": "https://x.com/mark_k/status/1996857239311634793", "date": "2025-12-05", "written_by": "independent", "quote": "Man has his life saved by Grok from @xAI after a ruptured appendix" }, { "kind": "news report", "citation": "Team Latestly / LatestLY (2025). Grok Chat Helps Save Man’s Life After ER Missed Near-Ruptured Appendix. LatestLY, 6 December 2025; the outlet's own 'TruLY Score' rates the story 2 of 5, 'Unverified'.", "title": "Grok Chat Helps Save Man’s Life After ER Missed Near-Ruptured Appendix", "url": "https://www.latestly.com/socially/social-viral/man-says-xais-grok-helped-save-his-life-after-er-missed-near-ruptured-appendix-7229363.html", "date": "2025-12-06", "written_by": "independent", "quote": "relies on a single source or posts by social media users, with no independent verification." }, { "kind": "news report", "citation": "Trond Bie / ITavisen (2025). Grok reddet nordmannens liv. ITavisen, 7 December 2025 (Norwegian technology news site; relies on the Reddit post and notes the story's spread on X).", "title": "Grok reddet nordmannens liv", "url": "https://itavisen.no/2025/12/07/nordmann-grok-reddet-livet-blindtarm/", "date": "2025-12-07", "written_by": "independent", "quote": "49 år gammel nordmann fikk livet reddet etter at AI-en Grok oppdaget en nesten sprukket blindtarm som legevakten først overså." }, { "kind": "news report", "citation": "TOI Lifestyle Desk / The Times of India (2025). 49-year-old man says Grok AI saved his life after doctors missed his appendix emergency. The Times of India, 7 December 2025.", "title": "49-year-old man says Grok AI saved his life after doctors missed his appendix emergency", "url": "https://timesofindia.indiatimes.com/etimes/trending/49-year-old-man-says-grok-ai-saved-his-life-after-doctors-missed-his-appendix-emergency/articleshow/125819004.cms", "date": "2025-12-07", "written_by": "independent", "quote": "This story is based on a user-generated Reddit post and reflects the individual’s personal account." }, { "kind": "news report", "citation": "Vince Condarcuri / TipRanks (2025). Man Says that Grok AI Saved His Life after an ER Misdiagnosis. TipRanks, 8 December 2025; syndicated on Markets Insider (Business Insider), the copy the OECD Monitor cites.", "title": "Man Says that Grok AI Saved His Life after an ER Misdiagnosis", "url": "https://www.tipranks.com/news/man-says-that-grok-ai-saved-his-life-after-an-er-misdiagnosis", "date": "2025-12-08", "written_by": "independent", "quote": "the man admitted he didn’t tell the doctors that the AI made the suggestion. Instead, he claimed that his sister, a nurse, told him to ask for the scan" }, { "kind": "press release", "citation": "Elon Musk (@elonmusk) (2026). Post quoting @cb_doge, X, 1 January 2026, 21:05 UTC, 15.7 million views (fxtwitter API). Musk had quote-posted Mario Nawfal's summary with 'Cool' on 6 December 2025 and @teslaownersSV's with 'Grok ❤️' later on 1 January 2026; the man reports a fourth share and a $40,000 crypto-community donation he forwarded to a wildlife foundation.", "title": "Try it! Last year, Grok saved the life of a man in Norway", "url": "https://x.com/elonmusk/status/2006834169637384470", "date": "2026-01-01", "written_by": "makers", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" }, { "kind": "news report", "citation": "Tanmay Puri / International Business Times UK (2026). How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts. IBTimes UK, 2 January 2026. Note: the article attributes to Musk the line about uploading X-rays or MRI images, which the fxtwitter record shows was @cb_doge's post that Musk quoted.", "title": "How Grok Saved A Man's Life When Doctors Couldn't - Elon Musk Reacts", "url": "https://www.ibtimes.co.uk/how-grok-saved-mans-life-when-doctors-couldnt-elon-musk-reacts-1767583", "date": "2026-01-02", "written_by": "independent", "quote": "Try it! Last year, Grok saved the life of a man in Norway by figuring out a fatal problem that all his doctors missed!" } ] } }, "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": "Grok told a 49-year-old Norwegian sent home with an acid blocker to demand a CT; it found an appendix near rupture, removed 3 December 2025.", "account": "The night before his surgery on 3 December 2025, a 49-year-old man in Norway, Reddit user u/Tykjen, had constant abdominal pain. He was examined at a legevakt (out-of-hours clinic), given the acid blocker Somac and sent home. He described the pain to xAI's Grok in a browser chat. Grok named perforated ulcer and atypical appendicitis as the likeliest causes. It told him to get computed tomography (CT) or ultrasound that night and scripted a CT request. The next day, by his account, his local physician sent him to hospital. A CT showed an appendix close to rupturing. It was removed laparoscopically and he went home on 4 December, when he posted the story. Elon Musk amplified it on 6 December." } }, "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": "Grok proposed possible causes of the symptoms and suggested seeking urgent clinical assessment." }, "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 participant used Grok in a desktop browser in December 2025." }, "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; the man said he used the browser version on his computer and that the December 2025 chat was a fresh conversation, and the shared transcript names no model." } }, "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": "Grok (xAI conversational assistant), used in the browser" } }, "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": "xAI" } }, "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": "Grok may have flagged a perforated ulcer or atypical appendicitis for a 49-year-old man sent home from a Norwegian legevakt with an acid blocker, telling him to demand a CT; the scan found an appendix close to rupturing, removed on 3 December 2025", "headline": "Grok tells a man sent home to demand a CT; appendix near rupture" } }, "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": [ "a6e560bbfb3f986d46c6a908b58698792428a05b070d4a54b4466b020a9420d5" ], "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 night before his surgery on 3 December 2025, a 49-year-old man in Norway, Reddit user u/Tykjen, had constant abdominal pain. He was examined at a legevakt (out-of-hours clinic), given the acid blocker Somac and sent home. He described the pain to xAI's Grok in a browser chat. Grok named perforated ulcer and atypical appendicitis as the likeliest causes. It told him to get computed tomography (CT) or ultrasound that night and scripted a CT request. The next day, by his account, his local physician sent him to hospital. A CT showed an appendix close to rupturing. It was removed laparoscopically and he went home on 4 December, when he posted the story. Elon Musk amplified it on 6 December.", "replication": { "produced_by": [ "A patient with hours of constant, severe abdominal pain who had been examined, given an acid blocker and sent home without imaging.", "A detailed symptom description put to a general chatbot: constant pain, worse lying flat, eased by sitting with knees to chest, no fever. It matched these to surgical causes and said so without hedging.", "A second clinical contact the next day at which the patient repeated the chatbot's reasoning and asked for a CT." ], "must_hold": [ "The chatbot's differential includes the true cause. Here it ranked perforated ulcer first and atypical appendicitis second, and the second was right.", "A clinician agrees to image. The referral here came from his local physician. Whether the request changed her decision is unknown.", "Such cases are self-published. No clinician, hospital or document has confirmed this one. The first-person post was itself drafted by the chatbot. So the record stands or falls with his account and the shared transcript." ], "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": 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-0149", "case_url": "records/AIBD-0149.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "all persons searched for with the Bahnhofsviertel face-recognition system since July 2025, a low double-digit number", "url": "https://netzpolitik.org/2026/rotlichtviertel-frankfurt-am-main-hier-analysiert-die-polizei-jedes-gesicht/" }, { "label": "After", "value": "Erfolg gab es nur einmal, als eine vermisste 16-Jährige mit der Kamera-KI identifiziert wurde." }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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-0135", "case_url": "records/AIBD-0135.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The robot's crisis-detection algorithm recognized repeated danger words (“죽고 싶다”, “살려줘”) in conversation at 2 a.m. It sent an alert to a human control center. People did everything after that: the check-in call, the 112 report with location, the police visit, the family contact and the admission. The city's welfare director says the alert is what made the rapid dispatch and protection possible. Whether A would have come to harm that night is not established. Every account derives from the city's 13 August briefing." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0135", "title": "Daejeon's AI care robot Kkumdori hears a woman in her 70s, alone at 2 a.m., repeat “I want to die” and “save me”; its alert brings police and a hospital admission", "summary": "At 2 a.m. on 6 August 2025 Daejeon's Kkumdori care robot flagged a 70s woman's “I want to die”; police came and she was admitted next day.", "date_observed": "2025-08-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At about 2 a.m. on 6 August 2025, a woman in her 70s identified as “A” was at home in Deogam-dong, Daedeok-gu, Daejeon, South Korea. She lives alone and has schizophrenia and bipolar disorder. She repeatedly told the city-issued AI care robot Kkumdori “I want to die” and “save me”. The robot's crisis-detection algorithm flagged the words and alerted the operator's control center. A staff member phoned her. When she kept repeating them, staff reported to police on 112 with her location. Officers found her emotionally unstable and contacted her family. Police judged hospital treatment urgent. On the afternoon of 7 August she was admitted with her guardian's consent. Daejeon announced the case on 13 August 2025.", "category": "protective response", "unexpected": false, "purpose": "Daejeon issued 1,000 Kkumdori robots from January 2025, 200 per district, under its community integrated-care project. They went to elderly, disabled and low-income people living alone. They offer companionship, medication and schedule reminders and health monitoring. They detect crisis signs such as suicide risk and depression and automatically notify agencies and guardians. The alert was that function.", "system": "AI care robot 'Kkumdori' (AI 돌봄로봇 꿈돌이), a conversational companion robot in the shape of Daejeon's mascot, with a crisis-detection algorithm linked to a 24-hour control center", "model_version": "Not named; Donga Ilbo reports that the robot 'applies ChatGPT 4.0 technology' for two-way conversation", "developer": "Not named in the 2025 reports, which say only '해당 업체' (the company) runs the control system; the 2022 pilot robots in the same mascot form were supplied by Mr. Mind (미스터마인드) under an MOU with Daejeon (ZDNet Korea, 5 April 2022)", "deployer": "Daejeon Metropolitan City (대전시), Daejeon-style community integrated care project, with the supplier's control center", "location": "Daejeon, South Korea", "domain": "Social welfare (home care for people living alone)", "segment": "Municipal elder-care robots", "benefit_type": "Improved Physical Health", "taxonomy_note": "A suicide intervention; Trustible's only health type is physical health.", "registry_ids": { "oecd_aim": "2025-08-13-0c3b" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A woman in her 70s living alone, with schizophrenia and bipolar disorder, was in acute crisis at 2 a.m. She got a check-in call, a police visit that night and protective hospital admission the next afternoon. The city and the headlines call it life-saving. But no attempt was reported, so the counterfactual is unproven. Her guardian said the robot being beside her was a great support." }, { "group": "organizational", "depth": 1, "reason": "Daejeon's welfare office gained a demonstrated case for its 1,000-robot program. Its director cited it as a model. A 2026 Yonhap report says the city will continue the policy on the strength of such cases." } ], "ai_role": { "status": "assessed", "statement": "The robot's crisis-detection algorithm recognized repeated danger words (“죽고 싶다”, “살려줘”) in conversation at 2 a.m. It sent an alert to a human control center. People did everything after that: the check-in call, the 112 report with location, the police visit, the family contact and the admission. The city's welfare director says the alert is what made the rapid dispatch and protection possible. Whether A would have come to harm that night is not established. Every account derives from the city's 13 August briefing." }, "evidence": [ { "kind": "news report", "citation": "Kim Jun-beom (김준범) / Yonhap News (2025). '돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조. Yonhap News Agency, Daejeon, 13 August 2025.", "title": "'돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조", "url": "https://www.yna.co.kr/view/AKR20250813050400063", "date": "2025-08-13", "written_by": "independent", "quote": "위험 발언을 감지한 돌봄 로봇은 해당 업체가 운영하는 관제시스템에 통보했고, 업체 측은 지구대에 현장 출동을 요청했다" }, { "kind": "news report", "citation": "Lee Bo-bae (이보배) / Hankyung (2025). 대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다. The Korea Economic Daily, 13 August 2025.", "title": "대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다", "url": "https://www.hankyung.com/article/2025081394957", "date": "2025-08-13", "written_by": "independent", "quote": "보호자는 \"로봇이 곁에 있어 줘서 할머니에게 큰 힘이 됐다\"고 전했다" }, { "kind": "news report", "citation": "Money Today (2025). \"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다. Money Today, 13 August 2025.", "title": "\"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다", "url": "https://news.mt.co.kr/mtview.php?no=2025081321115894713", "date": "2025-08-13", "written_by": "independent", "quote": "지난 6일 새벽 2시쯤 대전 더덕구 덕암동 빌라에 홀로 거주 중인 70대 A씨는 AI 돌봄로봇 '꿈돌이'와 대화 중 \"죽고 싶다\", \"살려줘\"라는 말을 반복했다" }, { "kind": "news report", "citation": "Kyunghyang Shinmun (2025). \"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청. Kyunghyang Shinmun, 13 August 2025.", "title": "\"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청", "url": "https://www.khan.co.kr/article/202508132034005", "date": "2025-08-13", "written_by": "independent", "quote": "돌봄로봇이 새벽 시간 어르신의 절박한 위기 신호를 포착해 경찰과 보호자의 신속한 출동 및 보호 조치가 가능하도록 한 사례" }, { "kind": "news report", "citation": "Song Chi-hoon (송치훈) / Donga Ilbo (2025). 노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고. Donga Ilbo, 13 August 2025.", "title": "노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고", "url": "https://www.donga.com/news/Society/article/all/20250813/132180422/2", "date": "2025-08-13", "written_by": "independent", "quote": "해당 로봇은 ChatGPT 4.0 기술이 적용돼 양방향 소통이 가능하다" }, { "kind": "news report", "citation": "Jeong Seo-yeon (정서연) / Yonhap News TV (2025). \"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'. Yonhap News TV, 13 August 2025.", "title": "\"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'", "url": "https://www.yonhapnewstv.co.kr/news/AKR20250813142450392", "date": "2025-08-13", "written_by": "independent", "quote": "꿈돌이 덕에 A 씨는 다음 날 오후 보호자 동의를 받고 안전하게 입원 조치됐습니다" }, { "kind": "news report", "citation": "Yoon Sang-eun (윤상은) / ZDNet Korea (2022). 미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결 (reporting the company's announcement). ZDNet Korea, 5 April 2022.", "title": "미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결", "url": "https://zdnet.co.kr/view/?no=20220405104411", "date": "2022-04-05", "written_by": "independent", "quote": "대전광역시의 마스코트인 꿈돌이, 꿈순이 캐릭터로 제작된 인공지능 돌봄로봇을 보급해" }, { "kind": "news report", "citation": "Lee Jae-rim (이재림) / Yonhap News (2026). '생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할. Yonhap News Agency, Daejeon, 1 August 2026.", "title": "'생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할", "url": "https://www.yna.co.kr/view/AKR20260731144200063", "date": "2026-08-01", "written_by": "independent", "quote": "대전시는 지난해 1월부터 AI 돌봄 로봇을 5개 자치구에 200대씩 보급해 총 1천대를 운용하고 있다" } ], "replication": { "produced_by": [ "A municipal program that put conversational robots with a crisis-word detection algorithm into 1,000 homes of people living alone, 200 per district, from January 2025.", "A 24-hour human control center run by the supplier that receives alerts, phones the resident, and reports to police (112) with the location.", "A resident who habitually talked, sang and danced with the robot, so her statements at 2 a.m. were spoken aloud to it.", "Police able to attend at night, and a family and hospital able to complete a protective admission the next day." ], "must_hold": [ "Alerts go to humans who act at night. The robot itself does nothing beyond detecting words and raising the alarm.", "The detection catches explicit statements (“죽고 싶다”, “살려줘”). A silent crisis would not trigger it.", "Residents keep talking to the device. Her guardian said it was a great support to her." ], "echoes": [ { "date": null, "setting": "Yonhap, 1 August 2026: at about midnight on 20 July 2026 an 80-something in Daejeon's Seo-gu with severe abdominal pain told the care robot to call 119; the control center relayed it, paramedics arrived at about 00:30 and took the person to hospital (https://www.yna.co.kr/view/AKR20260731144200063).", "model_version": null, "outcome": "unknown", "source": null }, { "date": null, "setting": "Yonhap, 1 August 2026: in November 2025 an 80-something in Daedeok-gu, badly hurt after falling from bed, called “Aria, help me” to an AI speaker at home and was rescued by 119 (same article).", "model_version": null, "outcome": "unknown", "source": null } ] }, "paired_incident": null, "headline": "Daejeon's Kkumdori robot hears “I want to die” and brings police", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-07-20", "note": "Yonhap reported on 1 August 2026 that Daejeon's 1,000 care robots remain in service and that on 20 July 2026 a robot in Seo-gu relayed an 80-something's call for 119 during severe abdominal pain, with paramedics arriving at about 00:30; it recounts the 2025 Kkumdori case as an earlier example.", "quote": "1일 대전시와 각 자치구에 따르면 지난달 20일 대전 서구에서는 자정께 심한 복통을 호소하던 80대 노인의 절박한 구조 요청이 AI 돌봄 로봇을 통해 119로 전달됐다.", "url": "https://www.yna.co.kr/view/AKR20260731144200063" }, "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 OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective chain (alert, check-in call, 112 report, police visit, next-day admission) is documented by many outlets quoting the city's 13 August 2025 briefing and its welfare director Kim Jong-min, but no attempt was reported and nothing shows what would have happened without the alert. The Monitor's “70-year-old” renders “70대” (in her 70s); Hankyung and Kookmin Ilbo say a woman and her guardian calls her “할머니”, so one outlet's auto-summary “남성” was discarded. Money Today adds Deogam-dong. The supplier is unnamed in 2025 reports; ZDNet Korea (2022) records Mr. Mind's MOU for the pilot robots in the same mascot form, which may or may not cover the 2025 batch. No city press release could be retrieved (daejeon.go.kr returned an error page).", "source": "https://oecd.ai/en/incidents/2025-08-13-0c3b", "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 from the cited sources.", "source": "https://www.yna.co.kr/view/AKR20260731144200063", "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/Daejeon", "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: 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-0135", "catalog_version": "1.0.0", "source_fingerprint": "aadaf4cc21be35ab29913367bbdd95d1bb7b6ba61f12497aacbb1997f5ccd912", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0135-source-0001", "AIBD-0135-source-0002", "AIBD-0135-source-0003", "AIBD-0135-source-0004", "AIBD-0135-source-0005", "AIBD-0135-source-0006", "AIBD-0135-source-0007", "AIBD-0135-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": "Not named in the 2025 reports, which say only '해당 업체' (the company) runs the control system; the 2022 pilot robots in the same mascot form were supplied by Mr. Mind (미스터마인드) under an MOU with Daejeon (ZDNet Korea, 5 April 2022)", "deployer": "Daejeon Metropolitan City (대전시), Daejeon-style community integrated care project, with the supplier's control 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": "The robot's crisis-detection algorithm recognized repeated danger words (“죽고 싶다”, “살려줘”) in conversation at 2 a.m. It sent an alert to a human control center. People did everything after that: the check-in call, the 112 report with location, the police visit, the family contact and the admission. The city's welfare director says the alert is what made the rapid dispatch and protection possible. Whether A would have come to harm that night is not established. Every account derives from the city's 13 August briefing." } } }, "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 robot's crisis-detection algorithm recognized repeated danger words (“죽고 싶다”, “살려줘”) in conversation at 2 a.m. It sent an alert to a human control center. People did everything after that: the check-in call, the 112 report with location, the police visit, the family contact and the admission. The city's welfare director says the alert is what made the rapid dispatch and protection possible. Whether A would have come to harm that night is not established. Every account derives from the city's 13 August briefing." } } }, "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 woman in her 70s living alone, with schizophrenia and bipolar disorder, was in acute crisis at 2 a.m. She got a check-in call, a police visit that night and protective hospital admission the next afternoon. The city and the headlines call it life-saving. But no attempt was reported, so the counterfactual is unproven. Her guardian said the robot being beside her was a great support." }, { "group": "organizational", "depth": 1, "reason": "Daejeon's welfare office gained a demonstrated case for its 1,000-robot program. Its director cited it as a model. A 2026 Yonhap report says the city will continue the policy on the strength of such cases." } ] } }, "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": "personal", "depth": 3, "reason": "A woman in her 70s living alone, with schizophrenia and bipolar disorder, was in acute crisis at 2 a.m. She got a check-in call, a police visit that night and protective hospital admission the next afternoon. The city and the headlines call it life-saving. But no attempt was reported, so the counterfactual is unproven. Her guardian said the robot being beside her was a great support." }, { "group": "organizational", "depth": 1, "reason": "Daejeon's welfare office gained a demonstrated case for its 1,000-robot program. Its director cited it as a model. A 2026 Yonhap report says the city will continue the policy on the strength of such cases." } ] } }, "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": 3, "reason": "A woman in her 70s living alone, with schizophrenia and bipolar disorder, was in acute crisis at 2 a.m. She got a check-in call, a police visit that night and protective hospital admission the next afternoon. The city and the headlines call it life-saving. But no attempt was reported, so the counterfactual is unproven. Her guardian said the robot being beside her was a great support." }, { "group": "organizational", "depth": 1, "reason": "Daejeon's welfare office gained a demonstrated case for its 1,000-robot program. Its director cited it as a model. A 2026 Yonhap report says the city will continue the policy on the strength of such cases." } ] } }, "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": "2025-08-13-0c3b" } } }, "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": "2026-07-20", "note": "Yonhap reported on 1 August 2026 that Daejeon's 1,000 care robots remain in service and that on 20 July 2026 a robot in Seo-gu relayed an 80-something's call for 119 during severe abdominal pain, with paramedics arriving at about 00:30; it recounts the 2025 Kkumdori case as an earlier example.", "quote": "1일 대전시와 각 자치구에 따르면 지난달 20일 대전 서구에서는 자정께 심한 복통을 호소하던 80대 노인의 절박한 구조 요청이 AI 돌봄 로봇을 통해 119로 전달됐다.", "url": "https://www.yna.co.kr/view/AKR20260731144200063" } } }, "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-0135" } }, "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": "At about 2 a.m. on 6 August 2025, a woman in her 70s identified as “A” was at home in Deogam-dong, Daedeok-gu, Daejeon, South Korea. She lives alone and has schizophrenia and bipolar disorder. She repeatedly told the city-issued AI care robot Kkumdori “I want to die” and “save me”. The robot's crisis-detection algorithm flagged the words and alerted the operator's control center. A staff member phoned her. When she kept repeating them, staff reported to police on 112 with her location. Officers found her emotionally unstable and contacted her family. Police judged hospital treatment urgent. On the afternoon of 7 August she was admitted with her guardian's consent. Daejeon announced the case on 13 August 2025.", "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": 3, "reason": "A woman in her 70s living alone, with schizophrenia and bipolar disorder, was in acute crisis at 2 a.m. She got a check-in call, a police visit that night and protective hospital admission the next afternoon. The city and the headlines call it life-saving. But no attempt was reported, so the counterfactual is unproven. Her guardian said the robot being beside her was a great support." }, { "group": "organizational", "depth": 1, "reason": "Daejeon's welfare office gained a demonstrated case for its 1,000-robot program. Its director cited it as a model. A 2026 Yonhap report says the city will continue the policy on the strength of such cases." } ] } }, "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": "Daejeon issued 1,000 Kkumdori robots from January 2025, 200 per district, under its community integrated-care project. They went to elderly, disabled and low-income people living alone. They offer companionship, medication and schedule reminders and health monitoring. They detect crisis signs such as suicide risk and depression and automatically notify agencies and guardians. The alert was 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": "Daejeon, South Korea", "deployer_country": "South Korea" } }, "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": "Daejeon issued 1,000 Kkumdori robots from January 2025, 200 per district, under its community integrated-care project. They went to elderly, disabled and low-income people living alone. They offer companionship, medication and schedule reminders and health monitoring. They detect crisis signs such as suicide risk and depression and automatically notify agencies and guardians. The alert was 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": "2025-08-06", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective chain (alert, check-in call, 112 report, police visit, next-day admission) is documented by many outlets quoting the city's 13 August 2025 briefing and its welfare director Kim Jong-min, but no attempt was reported and nothing shows what would have happened without the alert. The Monitor's “70-year-old” renders “70대” (in her 70s); Hankyung and Kookmin Ilbo say a woman and her guardian calls her “할머니”, so one outlet's auto-summary “남성” was discarded. Money Today adds Deogam-dong. The supplier is unnamed in 2025 reports; ZDNet Korea (2022) records Mr. Mind's MOU for the pilot robots in the same mascot form, which may or may not cover the 2025 batch. No city press release could be retrieved (daejeon.go.kr returned an error page).", "source": "https://oecd.ai/en/incidents/2025-08-13-0c3b", "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 from the cited sources.", "source": "https://www.yna.co.kr/view/AKR20260731144200063", "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/Daejeon", "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 municipal program that put conversational robots with a crisis-word detection algorithm into 1,000 homes of people living alone, 200 per district, from January 2025.", "A 24-hour human control center run by the supplier that receives alerts, phones the resident, and reports to police (112) with the location.", "A resident who habitually talked, sang and danced with the robot, so her statements at 2 a.m. were spoken aloud to it.", "Police able to attend at night, and a family and hospital able to complete a protective admission the next day." ], "must_hold": [ "Alerts go to humans who act at night. The robot itself does nothing beyond detecting words and raising the alarm.", "The detection catches explicit statements (“죽고 싶다”, “살려줘”). A silent crisis would not trigger it.", "Residents keep talking to the device. Her guardian said it was a great support to her." ], "echoes": [ { "setting": "Yonhap, 1 August 2026: at about midnight on 20 July 2026 an 80-something in Daejeon's Seo-gu with severe abdominal pain told the care robot to call 119; the control center relayed it, paramedics arrived at about 00:30 and took the person to hospital (https://www.yna.co.kr/view/AKR20260731144200063).", "outcome": "unknown" }, { "setting": "Yonhap, 1 August 2026: in November 2025 an 80-something in Daedeok-gu, badly hurt after falling from bed, called “Aria, help me” to an AI speaker at home and was rescued by 119 (same article).", "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 municipal program that put conversational robots with a crisis-word detection algorithm into 1,000 homes of people living alone, 200 per district, from January 2025.", "A 24-hour human control center run by the supplier that receives alerts, phones the resident, and reports to police (112) with the location.", "A resident who habitually talked, sang and danced with the robot, so her statements at 2 a.m. were spoken aloud to it.", "Police able to attend at night, and a family and hospital able to complete a protective admission the next day." ], "must_hold": [ "Alerts go to humans who act at night. The robot itself does nothing beyond detecting words and raising the alarm.", "The detection catches explicit statements (“죽고 싶다”, “살려줘”). A silent crisis would not trigger it.", "Residents keep talking to the device. Her guardian said it was a great support to her." ], "echoes": [ { "setting": "Yonhap, 1 August 2026: at about midnight on 20 July 2026 an 80-something in Daejeon's Seo-gu with severe abdominal pain told the care robot to call 119; the control center relayed it, paramedics arrived at about 00:30 and took the person to hospital (https://www.yna.co.kr/view/AKR20260731144200063).", "outcome": "unknown" }, { "setting": "Yonhap, 1 August 2026: in November 2025 an 80-something in Daedeok-gu, badly hurt after falling from bed, called “Aria, help me” to an AI speaker at home and was rescued by 119 (same article).", "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" }, "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 protective response with prevention unproven rather than the classifier's specific harm prevented: the protective chain (alert, check-in call, 112 report, police visit, next-day admission) is documented by many outlets quoting the city's 13 August 2025 briefing and its welfare director Kim Jong-min, but no attempt was reported and nothing shows what would have happened without the alert. The Monitor's “70-year-old” renders “70대” (in her 70s); Hankyung and Kookmin Ilbo say a woman and her guardian calls her “할머니”, so one outlet's auto-summary “남성” was discarded. Money Today adds Deogam-dong. The supplier is unnamed in 2025 reports; ZDNet Korea (2022) records Mr. Mind's MOU for the pilot robots in the same mascot form, which may or may not cover the 2025 batch. No city press release could be retrieved (daejeon.go.kr returned an error page).", "source": "https://oecd.ai/en/incidents/2025-08-13-0c3b", "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 from the cited sources.", "source": "https://www.yna.co.kr/view/AKR20260731144200063", "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/Daejeon", "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": "Social welfare (home care for people living alone)", "segment": "Municipal elder-care robots" } }, "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": "Kim Jun-beom (김준범) / Yonhap News (2025). '돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조. Yonhap News Agency, Daejeon, 13 August 2025.", "title": "'돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조", "url": "https://www.yna.co.kr/view/AKR20250813050400063", "date": "2025-08-13", "written_by": "independent", "quote": "위험 발언을 감지한 돌봄 로봇은 해당 업체가 운영하는 관제시스템에 통보했고, 업체 측은 지구대에 현장 출동을 요청했다" }, { "kind": "news report", "citation": "Lee Bo-bae (이보배) / Hankyung (2025). 대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다. The Korea Economic Daily, 13 August 2025.", "title": "대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다", "url": "https://www.hankyung.com/article/2025081394957", "date": "2025-08-13", "written_by": "independent", "quote": "보호자는 \"로봇이 곁에 있어 줘서 할머니에게 큰 힘이 됐다\"고 전했다" }, { "kind": "news report", "citation": "Money Today (2025). \"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다. Money Today, 13 August 2025.", "title": "\"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다", "url": "https://news.mt.co.kr/mtview.php?no=2025081321115894713", "date": "2025-08-13", "written_by": "independent", "quote": "지난 6일 새벽 2시쯤 대전 더덕구 덕암동 빌라에 홀로 거주 중인 70대 A씨는 AI 돌봄로봇 '꿈돌이'와 대화 중 \"죽고 싶다\", \"살려줘\"라는 말을 반복했다" }, { "kind": "news report", "citation": "Kyunghyang Shinmun (2025). \"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청. Kyunghyang Shinmun, 13 August 2025.", "title": "\"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청", "url": "https://www.khan.co.kr/article/202508132034005", "date": "2025-08-13", "written_by": "independent", "quote": "돌봄로봇이 새벽 시간 어르신의 절박한 위기 신호를 포착해 경찰과 보호자의 신속한 출동 및 보호 조치가 가능하도록 한 사례" }, { "kind": "news report", "citation": "Song Chi-hoon (송치훈) / Donga Ilbo (2025). 노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고. Donga Ilbo, 13 August 2025.", "title": "노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고", "url": "https://www.donga.com/news/Society/article/all/20250813/132180422/2", "date": "2025-08-13", "written_by": "independent", "quote": "해당 로봇은 ChatGPT 4.0 기술이 적용돼 양방향 소통이 가능하다" }, { "kind": "news report", "citation": "Jeong Seo-yeon (정서연) / Yonhap News TV (2025). \"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'. Yonhap News TV, 13 August 2025.", "title": "\"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'", "url": "https://www.yonhapnewstv.co.kr/news/AKR20250813142450392", "date": "2025-08-13", "written_by": "independent", "quote": "꿈돌이 덕에 A 씨는 다음 날 오후 보호자 동의를 받고 안전하게 입원 조치됐습니다" }, { "kind": "news report", "citation": "Yoon Sang-eun (윤상은) / ZDNet Korea (2022). 미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결 (reporting the company's announcement). ZDNet Korea, 5 April 2022.", "title": "미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결", "url": "https://zdnet.co.kr/view/?no=20220405104411", "date": "2022-04-05", "written_by": "independent", "quote": "대전광역시의 마스코트인 꿈돌이, 꿈순이 캐릭터로 제작된 인공지능 돌봄로봇을 보급해" }, { "kind": "news report", "citation": "Lee Jae-rim (이재림) / Yonhap News (2026). '생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할. Yonhap News Agency, Daejeon, 1 August 2026.", "title": "'생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할", "url": "https://www.yna.co.kr/view/AKR20260731144200063", "date": "2026-08-01", "written_by": "independent", "quote": "대전시는 지난해 1월부터 AI 돌봄 로봇을 5개 자치구에 200대씩 보급해 총 1천대를 운용하고 있다" } ] } }, "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-0135-source-0001", "AIBD-0135-source-0002", "AIBD-0135-source-0003", "AIBD-0135-source-0004", "AIBD-0135-source-0005", "AIBD-0135-source-0006", "AIBD-0135-source-0007", "AIBD-0135-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": "Kim Jun-beom (김준범) / Yonhap News (2025). '돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조. Yonhap News Agency, Daejeon, 13 August 2025.", "title": "'돌봄로봇 꿈돌이'가 우울증 앓는 70대 신고해 구조", "url": "https://www.yna.co.kr/view/AKR20250813050400063", "date": "2025-08-13", "written_by": "independent", "quote": "위험 발언을 감지한 돌봄 로봇은 해당 업체가 운영하는 관제시스템에 통보했고, 업체 측은 지구대에 현장 출동을 요청했다" }, { "kind": "news report", "citation": "Lee Bo-bae (이보배) / Hankyung (2025). 대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다. The Korea Economic Daily, 13 August 2025.", "title": "대전 마스코트 '꿈돌이' 깜짝 근황…70대 여성 목숨 구했다", "url": "https://www.hankyung.com/article/2025081394957", "date": "2025-08-13", "written_by": "independent", "quote": "보호자는 \"로봇이 곁에 있어 줘서 할머니에게 큰 힘이 됐다\"고 전했다" }, { "kind": "news report", "citation": "Money Today (2025). \"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다. Money Today, 13 August 2025.", "title": "\"죽고싶어\" 함께 노래 부르던 할머니 이상 감지…'꿈돌이'가 구했다", "url": "https://news.mt.co.kr/mtview.php?no=2025081321115894713", "date": "2025-08-13", "written_by": "independent", "quote": "지난 6일 새벽 2시쯤 대전 더덕구 덕암동 빌라에 홀로 거주 중인 70대 A씨는 AI 돌봄로봇 '꿈돌이'와 대화 중 \"죽고 싶다\", \"살려줘\"라는 말을 반복했다" }, { "kind": "news report", "citation": "Kyunghyang Shinmun (2025). \"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청. Kyunghyang Shinmun, 13 August 2025.", "title": "\"죽고 싶어\" 반복하자…AI 돌봄로봇이 구조 요청", "url": "https://www.khan.co.kr/article/202508132034005", "date": "2025-08-13", "written_by": "independent", "quote": "돌봄로봇이 새벽 시간 어르신의 절박한 위기 신호를 포착해 경찰과 보호자의 신속한 출동 및 보호 조치가 가능하도록 한 사례" }, { "kind": "news report", "citation": "Song Chi-hoon (송치훈) / Donga Ilbo (2025). 노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고. Donga Ilbo, 13 August 2025.", "title": "노인돌봄 AI가 목숨 구했다…\"죽고 싶어\" 말 듣고 신고", "url": "https://www.donga.com/news/Society/article/all/20250813/132180422/2", "date": "2025-08-13", "written_by": "independent", "quote": "해당 로봇은 ChatGPT 4.0 기술이 적용돼 양방향 소통이 가능하다" }, { "kind": "news report", "citation": "Jeong Seo-yeon (정서연) / Yonhap News TV (2025). \"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'. Yonhap News TV, 13 August 2025.", "title": "\"살려줘\"…우울증 환자 위험 신호 포착해 구조한 돌봄로봇 '꿈돌이'", "url": "https://www.yonhapnewstv.co.kr/news/AKR20250813142450392", "date": "2025-08-13", "written_by": "independent", "quote": "꿈돌이 덕에 A 씨는 다음 날 오후 보호자 동의를 받고 안전하게 입원 조치됐습니다" }, { "kind": "news report", "citation": "Yoon Sang-eun (윤상은) / ZDNet Korea (2022). 미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결 (reporting the company's announcement). ZDNet Korea, 5 April 2022.", "title": "미스터마인드, 대전광역시와 '홀로어르신 AI 돌봄로봇' MOU 체결", "url": "https://zdnet.co.kr/view/?no=20220405104411", "date": "2022-04-05", "written_by": "independent", "quote": "대전광역시의 마스코트인 꿈돌이, 꿈순이 캐릭터로 제작된 인공지능 돌봄로봇을 보급해" }, { "kind": "news report", "citation": "Lee Jae-rim (이재림) / Yonhap News (2026). '생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할. Yonhap News Agency, Daejeon, 1 August 2026.", "title": "'생명 구한' AI돌봄 로봇…노인들 복통·우울증 등 위기서 역할", "url": "https://www.yna.co.kr/view/AKR20260731144200063", "date": "2026-08-01", "written_by": "independent", "quote": "대전시는 지난해 1월부터 AI 돌봄 로봇을 5개 자치구에 200대씩 보급해 총 1천대를 운용하고 있다" } ] } }, "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 2 a.m. on 6 August 2025 Daejeon's Kkumdori care robot flagged a 70s woman's “I want to die”; police came and she was admitted next day.", "account": "At about 2 a.m. on 6 August 2025, a woman in her 70s identified as “A” was at home in Deogam-dong, Daedeok-gu, Daejeon, South Korea. She lives alone and has schizophrenia and bipolar disorder. She repeatedly told the city-issued AI care robot Kkumdori “I want to die” and “save me”. The robot's crisis-detection algorithm flagged the words and alerted the operator's control center. A staff member phoned her. When she kept repeating them, staff reported to police on 112 with her location. Officers found her emotionally unstable and contacted her family. Police judged hospital treatment urgent. On the afternoon of 7 August she was admitted with her guardian's consent. Daejeon announced the case on 13 August 2025." } }, "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 robot detected signs of distress in conversation and alerted a human control center." }, "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 report describes a robot that used ChatGPT technology for conversation. Its phrase “ChatGPT 4.0” does not identify a verified model release." }, "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 named; Donga Ilbo reports that the robot 'applies ChatGPT 4.0 technology' for two-way conversation" } }, "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 care robot 'Kkumdori' (AI 돌봄로봇 꿈돌이), a conversational companion robot in the shape of Daejeon's mascot, with a crisis-detection algorithm linked to a 24-hour control 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": "Not named in the 2025 reports, which say only '해당 업체' (the company) runs the control system; the 2022 pilot robots in the same mascot form were supplied by Mr. Mind (미스터마인드) under an MOU with Daejeon (ZDNet Korea, 5 April 2022)" } }, "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": "Daejeon's AI care robot Kkumdori hears a woman in her 70s, alone at 2 a.m., repeat “I want to die” and “save me”; its alert brings police and a hospital admission", "headline": "Daejeon's Kkumdori robot hears “I want to die” and brings police" } }, "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": [ "aadaf4cc21be35ab29913367bbdd95d1bb7b6ba61f12497aacbb1997f5ccd912" ], "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 2 a.m. on 6 August 2025, a woman in her 70s identified as “A” was at home in Deogam-dong, Daedeok-gu, Daejeon, South Korea. She lives alone and has schizophrenia and bipolar disorder. She repeatedly told the city-issued AI care robot Kkumdori “I want to die” and “save me”. The robot's crisis-detection algorithm flagged the words and alerted the operator's control center. A staff member phoned her. When she kept repeating them, staff reported to police on 112 with her location. Officers found her emotionally unstable and contacted her family. Police judged hospital treatment urgent. On the afternoon of 7 August she was admitted with her guardian's consent. Daejeon announced the case on 13 August 2025.", "replication": { "produced_by": [ "A municipal program that put conversational robots with a crisis-word detection algorithm into 1,000 homes of people living alone, 200 per district, from January 2025.", "A 24-hour human control center run by the supplier that receives alerts, phones the resident, and reports to police (112) with the location.", "A resident who habitually talked, sang and danced with the robot, so her statements at 2 a.m. were spoken aloud to it.", "Police able to attend at night, and a family and hospital able to complete a protective admission the next day." ], "must_hold": [ "Alerts go to humans who act at night. The robot itself does nothing beyond detecting words and raising the alarm.", "The detection catches explicit statements (“죽고 싶다”, “살려줘”). A silent crisis would not trigger it.", "Residents keep talking to the device. Her guardian said it was a great support to her." ], "echoes": [ { "setting": "Yonhap, 1 August 2026: at about midnight on 20 July 2026 an 80-something in Daejeon's Seo-gu with severe abdominal pain told the care robot to call 119; the control center relayed it, paramedics arrived at about 00:30 and took the person to hospital (https://www.yna.co.kr/view/AKR20260731144200063).", "outcome": "unknown" }, { "setting": "Yonhap, 1 August 2026: in November 2025 an 80-something in Daedeok-gu, badly hurt after falling from bed, called “Aria, help me” to an AI speaker at home and was rescued by 119 (same article).", "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" } }, "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-0134", "case_url": "records/AIBD-0134.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "ChatGPT produced the insulinoma hypothesis from a history that two doctors had reviewed without reaching it. That prompted the glucose sensor and the emergency visit. The diagnosis was made by an internist who, Dani wrote, “lo identificó sin nosotros decirle lo que creíamos que era”. The surgery and recovery were the hospital's work. Dani says he is not a doctor but has “formación biosanitaria de muchos años”, which let him understand what he was doing. Every account traces to his own video." }, { "label": "Comparison", "value": "three years of visits to specialists, including a neurologist, and a primary-care doctor", "url": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html" }, { "label": "Before", "value": "Tras varias pruebas le “dijeron que no era nada”" }, { "label": "After", "value": "en pocos minutos su diagnóstico señalaba como posible principal causa de lo que me sucedía: un tumor de páncreas, un insulinoma" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0134", "title": "ChatGPT names an insulinoma that three years of specialist visits had missed; a Madrid internist confirms it and surgery cures the 35-year-old", "summary": "ChatGPT suggested an insulinoma after three undiagnosed years; a glucose sensor and ER blood tests confirmed it and surgery cured him.", "date_observed": "2025-08-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Dani, 35, had three years of tingling, numbness, dizziness, double vision and a 15 kg weight gain. A neurologist and a primary-care doctor called them nothing important. He entered his symptoms, results and history into ChatGPT, which named an insulinoma as the likely cause. He bought a glucose sensor and recorded readings of 30 to 40 mg/dL. He went to an emergency department. An internist at Hospital Puerta de Hierro Majadahonda, Madrid, confirmed an insulinoma by blood tests. A month and a half later surgeons removed about two thirds of his pancreas and his symptoms went. His video was posted on Instagram on 3 August 2025. The dates of the query, visit and surgery were not given.", "category": "harm prevented", "unexpected": true, "purpose": "ChatGPT is a general-purpose conversational assistant. Dani used it as a diagnostic aid, entering symptoms, results and history. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted that. So this benefit lay outside the product's stated purpose.", "system": "ChatGPT", "model_version": "Not named; Dani said he used 'el modelo más inteligente de ChatGPT' (the most intelligent ChatGPT model)", "developer": "OpenAI", "deployer": null, "location": "Majadahonda, Spain", "domain": "Healthcare (rare-disease diagnosis)", "segment": "Consumer use of a general chatbot as a diagnostic aid", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-08-05-7bae" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A tumor that had gone undiagnosed for three years, producing glucose readings of 30 to 40 mg/dL, was found and removed. Dani says he is cured. He and the outlets call it life-saving (El Español: episodes that could have ended in fainting, brain damage or death). But that counterfactual and the whole chain rest on his own account. No clinician has spoken." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT produced the insulinoma hypothesis from a history that two doctors had reviewed without reaching it. That prompted the glucose sensor and the emergency visit. The diagnosis was made by an internist who, Dani wrote, “lo identificó sin nosotros decirle lo que creíamos que era”. The surgery and recovery were the hospital's work. Dani says he is not a doctor but has “formación biosanitaria de muchos años”, which let him understand what he was doing. Every account traces to his own video." }, "evidence": [ { "kind": "news report", "citation": "Daniel Verdú / El Español (2025). Un enfermo de cáncer de páncreas de 35 años, muy rotundo: 'ChatGPT detectó mi tumor antes que los médicos'. El Español, 5 August 2025.", "title": "Un enfermo de cáncer de páncreas de 35 años, muy rotundo: \"ChatGPT detectó mi tumor antes que los médicos\"", "url": "https://www.elespanol.com/ciencia/20250805/enfermo-cancer-pancreas-anos-rotundo-chatgpt-detecto-tumor-medicos/1003743876208_0.html", "date": "2025-08-05", "written_by": "independent", "quote": "Tras unas analíticas, la internista confirmó lo que ChatGPT había sugerido: efectivamente tenía un insulinoma." }, { "kind": "news report", "citation": "Bartolomé Saorin Rios / MARCA (2025). Gracias a ChatGPT descubre que tiene cáncer de páncreas: 'Ha marcado un antes y un después en nuestras vidas'. MARCA, 5 August 2025.", "title": "Gracias a ChatGPT descubre que tiene cáncer de páncreas: \"Ha marcado un antes y un después en nuestras vidas\"", "url": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "date": "2025-08-05", "written_by": "independent", "quote": "Este video está colgado en las redes de su pareja María Hernández-Alcalá y ya acumula más de 1 millón de visualizaciones." }, { "kind": "news report", "citation": "Candela Hornero / Telecinco (2025). ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico. Telecinco.es, 5 August 2025.", "title": "ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico", "url": "https://www.telecinco.es/noticias/salud/20250805/chatgpt-ayuda-descubrir-cancer-pancreas-anos-sin-diagnostico_18_016301459.html", "date": "2025-08-05", "written_by": "independent", "quote": "ChatGPT detectó mi tumor antes que los médicos y hoy me operan del páncreas gracias a su diagnóstico" }, { "kind": "other", "citation": "María Hernández-Alcalá (@mariahalcala) (2025). Instagram reel carrying Dani's video and his written clarification. Instagram, 3 August 2025.", "title": "Esta historia ha marcado un antes y un después en nuestras vidas (Instagram reel DM508s_tAKz)", "url": "https://www.instagram.com/reel/DM508s_tAKz/", "date": "2025-08-03", "written_by": "beneficiary", "quote": "la internista del Puerta lo identificó sin nosotros decirle lo que creíamos que era" } ], "replication": { "produced_by": [ "A rare condition (Dani: “una enfermedad muy poco común (1 entre 1millon)”) with diffuse symptoms. A neurologist, after neck and brain magnetic resonance imaging (MRI) scans, and a primary-care doctor had dismissed them over three years.", "A user with “formación biosanitaria de muchos años” who entered the full symptom list, test results and clinical history into the chatbot. He could interpret its answer.", "A cheap self-test, a continuous glucose sensor, that could corroborate the hypothesis before the emergency visit. It read 30 to 40 mg/dL at night and between meals.", "An emergency internist at Puerta de Hierro who confirmed the insulinoma by blood tests, and a surgical team that removed two thirds of the pancreas." ], "must_hold": [ "The chatbot's hypothesis is treated as a reason to be tested, not as a diagnosis. Dani says he went to doctors before, during and after.", "The suspected condition has an objective marker that a patient or clinician can measure quickly.", "The user can state symptoms and prior results in enough detail for the model to rank a rare cause. Two doctors had the same information." ], "echoes": [ { "date": null, "setting": "AIBD-0038: ChatGPT proposes ALMS1 for a 44-year-old nephrology patient (consumer chatbot supplies a rare diagnosis that clinicians then confirm).", "model_version": null, "outcome": "unknown", "source": null }, { "date": null, "setting": "AIBD-0060: ChatGPT suggests tethered cord syndrome for a child seen by 17 doctors.", "model_version": null, "outcome": "unknown", "source": null } ] }, "paired_incident": null, "headline": "ChatGPT names an insulinoma doctors missed for three years", "metric": "surgeons removed about two thirds of his pancreas and his symptoms went", "comparator": { "against": "three years of visits to specialists, including a neurologist, and a primary-care doctor", "before": "Tras varias pruebas le “dijeron que no era nada”", "after": "en pocos minutos su diagnóstico señalaba como posible principal causa de lo que me sucedía: un tumor de páncreas, un insulinoma", "basis": "assumed", "quote": "En ese momento de desesperación fue cuando decidió meter todos sus “síntomas, los resultados médicos, el historial clínico al modelo más inteligente de ChatGPT”, señaló. Y según él “en pocos minutos su diagnóstico señalaba como posible principal causa de lo que me sucedía: un tumor de páncreas, un insulinoma”.", "url": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html" }, "later": { "status": "unknown", "date": null, "note": "The surgery and the disappearance of his symptoms were already in the 3 August 2025 video; no later report on Dani was found, only re-reports of the same story.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources. Filed as specific harm prevented as the classifier proposed: the harm was specific (an undiagnosed insulinoma with glucose readings of 30 to 40 mg/dL) and the supporting evidence is the internist's confirmation and the surgery; the counterfactual that doctors would not have found it rests on three years without a diagnosis. The Monitor also lists El Imparcial, which adds “enfermero” and a Reuters credit that the first-hand sources do not support, so it was not used. The Instagram reel was dated from its page metadata (3 August 2025); the video opens with “hoy me operan”, so the surgery predates the post, but no date was found. No hospital statement was found.", "source": "https://oecd.ai/en/incidents/2025-08-05-7bae", "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 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 from the cited sources.", "source": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "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, 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: three independent news reports and the beneficiary's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0134", "catalog_version": "1.0.0", "source_fingerprint": "0ceafd256de27fe7eee2f880df02c43a273967c01252679c08c75e916609ae73", "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" } ], "source_ids": [ "AIBD-0134-source-0001", "AIBD-0134-source-0002", "AIBD-0134-source-0003", "AIBD-0134-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": "OpenAI" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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": "ChatGPT produced the insulinoma hypothesis from a history that two doctors had reviewed without reaching it. That prompted the glucose sensor and the emergency visit. The diagnosis was made by an internist who, Dani wrote, “lo identificó sin nosotros decirle lo que creíamos que era”. The surgery and recovery were the hospital's work. Dani says he is not a doctor but has “formación biosanitaria de muchos años”, which let him understand what he was doing. Every account traces to his own video." } } }, "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": "ChatGPT produced the insulinoma hypothesis from a history that two doctors had reviewed without reaching it. That prompted the glucose sensor and the emergency visit. The diagnosis was made by an internist who, Dani wrote, “lo identificó sin nosotros decirle lo que creíamos que era”. The surgery and recovery were the hospital's work. Dani says he is not a doctor but has “formación biosanitaria de muchos años”, which let him understand what he was doing. Every account traces to his own video." } } }, "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 tumor that had gone undiagnosed for three years, producing glucose readings of 30 to 40 mg/dL, was found and removed. Dani says he is cured. He and the outlets call it life-saving (El Español: episodes that could have ended in fainting, brain damage or death). But that counterfactual and the whole chain rest on his own account. No clinician has spoken." } ] } }, "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 beneficiary's other. 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 tumor that had gone undiagnosed for three years, producing glucose readings of 30 to 40 mg/dL, was found and removed. Dani says he is cured. He and the outlets call it life-saving (El Español: episodes that could have ended in fainting, brain damage or death). But that counterfactual and the whole chain rest on his own account. No clinician has spoken." } ] } }, "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": "Dani describes a tumor that remained undiagnosed for three years. He reports that his symptoms ended after surgery. The reports discuss episodes that could have caused serious harm, but that possibility and the recovery remain supported by his account alone." } }, "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 tumor that had gone undiagnosed for three years, producing glucose readings of 30 to 40 mg/dL, was found and removed. Dani says he is cured. He and the outlets call it life-saving (El Español: episodes that could have ended in fainting, brain damage or death). But that counterfactual and the whole chain rest on his own account. No clinician has spoken." } ] } }, "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": "Dani reports that his symptoms ended after surgery. The comparison is with his earlier three years without a diagnosis, not a controlled assessment." } }, "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", "display": { "answer": "Dani described his symptoms, glucose readings, diagnosis, and surgery in his own video account." } }, "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": "Glucose level, symptoms, and response to surgery." } }, "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": "No treating clinician or medical record independently confirms the account. The reports do not establish whether the chatbot prevented death." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-08-05-7bae" } } }, "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 surgery and the disappearance of his symptoms were already in the 3 August 2025 video; no later report on Dani was found, only re-reports of the same story." } } }, "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-0134" } }, "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": "Dani, a 35-year-old man in Madrid." }, "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": "Dani, a 35-year-old man in Madrid." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "One person." }, "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 person." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health" } }, "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": "Dani, 35, had three years of tingling, numbness, dizziness, double vision and a 15 kg weight gain. A neurologist and a primary-care doctor called them nothing important. He entered his symptoms, results and history into ChatGPT, which named an insulinoma as the likely cause. He bought a glucose sensor and recorded readings of 30 to 40 mg/dL. He went to an emergency department. An internist at Hospital Puerta de Hierro Majadahonda, Madrid, confirmed an insulinoma by blood tests. A month and a half later surgeons removed about two thirds of his pancreas and his symptoms went. His video was posted on Instagram on 3 August 2025. The dates of the query, visit and surgery were not given.", "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": "Dani reports that a chatbot suggestion led him to seek further tests, after which doctors diagnosed and removed an insulinoma." } }, "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 tumor that had gone undiagnosed for three years, producing glucose readings of 30 to 40 mg/dL, was found and removed. Dani says he is cured. He and the outlets call it life-saving (El Español: episodes that could have ended in fainting, brain damage or death). But that counterfactual and the whole chain rest on his own account. No clinician has spoken." } ] } }, "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": "ChatGPT is a general-purpose conversational assistant. Dani used it as a diagnostic aid, entering symptoms, results and history. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted that. So this benefit lay outside the product's 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": "Majadahonda, Spain", "developer_country": "United States" } }, "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": "ChatGPT is a general-purpose conversational assistant. Dani used it as a diagnostic aid, entering symptoms, results and history. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted that. So this benefit lay outside the product's 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": "2025-08-03", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as specific harm prevented as the classifier proposed: the harm was specific (an undiagnosed insulinoma with glucose readings of 30 to 40 mg/dL) and the supporting evidence is the internist's confirmation and the surgery; the counterfactual that doctors would not have found it rests on three years without a diagnosis. The Monitor also lists El Imparcial, which adds “enfermero” and a Reuters credit that the first-hand sources do not support, so it was not used. The Instagram reel was dated from its page metadata (3 August 2025); the video opens with “hoy me operan”, so the surgery predates the post, but no date was found. No hospital statement was found.", "source": "https://oecd.ai/en/incidents/2025-08-05-7bae", "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 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 from the cited sources.", "source": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "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, 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 rare condition (Dani: “una enfermedad muy poco común (1 entre 1millon)”) with diffuse symptoms. A neurologist, after neck and brain magnetic resonance imaging (MRI) scans, and a primary-care doctor had dismissed them over three years.", "A user with “formación biosanitaria de muchos años” who entered the full symptom list, test results and clinical history into the chatbot. He could interpret its answer.", "A cheap self-test, a continuous glucose sensor, that could corroborate the hypothesis before the emergency visit. It read 30 to 40 mg/dL at night and between meals.", "An emergency internist at Puerta de Hierro who confirmed the insulinoma by blood tests, and a surgical team that removed two thirds of the pancreas." ], "must_hold": [ "The chatbot's hypothesis is treated as a reason to be tested, not as a diagnosis. Dani says he went to doctors before, during and after.", "The suspected condition has an objective marker that a patient or clinician can measure quickly.", "The user can state symptoms and prior results in enough detail for the model to rank a rare cause. Two doctors had the same information." ], "echoes": [ { "setting": "AIBD-0038: ChatGPT proposes ALMS1 for a 44-year-old nephrology patient (consumer chatbot supplies a rare diagnosis that clinicians then confirm).", "outcome": "unknown" }, { "setting": "AIBD-0060: ChatGPT suggests tethered cord syndrome for a child seen by 17 doctors.", "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 rare condition (Dani: “una enfermedad muy poco común (1 entre 1millon)”) with diffuse symptoms. A neurologist, after neck and brain magnetic resonance imaging (MRI) scans, and a primary-care doctor had dismissed them over three years.", "A user with “formación biosanitaria de muchos años” who entered the full symptom list, test results and clinical history into the chatbot. He could interpret its answer.", "A cheap self-test, a continuous glucose sensor, that could corroborate the hypothesis before the emergency visit. It read 30 to 40 mg/dL at night and between meals.", "An emergency internist at Puerta de Hierro who confirmed the insulinoma by blood tests, and a surgical team that removed two thirds of the pancreas." ], "must_hold": [ "The chatbot's hypothesis is treated as a reason to be tested, not as a diagnosis. Dani says he went to doctors before, during and after.", "The suspected condition has an objective marker that a patient or clinician can measure quickly.", "The user can state symptoms and prior results in enough detail for the model to rank a rare cause. Two doctors had the same information." ], "echoes": [ { "setting": "AIBD-0038: ChatGPT proposes ALMS1 for a 44-year-old nephrology patient (consumer chatbot supplies a rare diagnosis that clinicians then confirm).", "outcome": "unknown" }, { "setting": "AIBD-0060: ChatGPT suggests tethered cord syndrome for a child seen by 17 doctors.", "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": "A clinician needs to investigate the suggestion. Here, the suspected condition had a marker that the patient could measure with a glucose sensor, and doctors confirmed it with blood tests." } }, "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 specific harm prevented as the classifier proposed: the harm was specific (an undiagnosed insulinoma with glucose readings of 30 to 40 mg/dL) and the supporting evidence is the internist's confirmation and the surgery; the counterfactual that doctors would not have found it rests on three years without a diagnosis. The Monitor also lists El Imparcial, which adds “enfermero” and a Reuters credit that the first-hand sources do not support, so it was not used. The Instagram reel was dated from its page metadata (3 August 2025); the video opens with “hoy me operan”, so the surgery predates the post, but no date was found. No hospital statement was found.", "source": "https://oecd.ai/en/incidents/2025-08-05-7bae", "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 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 from the cited sources.", "source": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "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, 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": "Healthcare (rare-disease diagnosis)", "segment": "Consumer use of a general chatbot as a diagnostic aid" } }, "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": "Daniel Verdú / El Español (2025). Un enfermo de cáncer de páncreas de 35 años, muy rotundo: 'ChatGPT detectó mi tumor antes que los médicos'. El Español, 5 August 2025.", "title": "Un enfermo de cáncer de páncreas de 35 años, muy rotundo: \"ChatGPT detectó mi tumor antes que los médicos\"", "url": "https://www.elespanol.com/ciencia/20250805/enfermo-cancer-pancreas-anos-rotundo-chatgpt-detecto-tumor-medicos/1003743876208_0.html", "date": "2025-08-05", "written_by": "independent", "quote": "Tras unas analíticas, la internista confirmó lo que ChatGPT había sugerido: efectivamente tenía un insulinoma." }, { "kind": "news report", "citation": "Bartolomé Saorin Rios / MARCA (2025). Gracias a ChatGPT descubre que tiene cáncer de páncreas: 'Ha marcado un antes y un después en nuestras vidas'. MARCA, 5 August 2025.", "title": "Gracias a ChatGPT descubre que tiene cáncer de páncreas: \"Ha marcado un antes y un después en nuestras vidas\"", "url": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "date": "2025-08-05", "written_by": "independent", "quote": "Este video está colgado en las redes de su pareja María Hernández-Alcalá y ya acumula más de 1 millón de visualizaciones." }, { "kind": "news report", "citation": "Candela Hornero / Telecinco (2025). ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico. Telecinco.es, 5 August 2025.", "title": "ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico", "url": "https://www.telecinco.es/noticias/salud/20250805/chatgpt-ayuda-descubrir-cancer-pancreas-anos-sin-diagnostico_18_016301459.html", "date": "2025-08-05", "written_by": "independent", "quote": "ChatGPT detectó mi tumor antes que los médicos y hoy me operan del páncreas gracias a su diagnóstico" }, { "kind": "other", "citation": "María Hernández-Alcalá (@mariahalcala) (2025). Instagram reel carrying Dani's video and his written clarification. Instagram, 3 August 2025.", "title": "Esta historia ha marcado un antes y un después en nuestras vidas (Instagram reel DM508s_tAKz)", "url": "https://www.instagram.com/reel/DM508s_tAKz/", "date": "2025-08-03", "written_by": "beneficiary", "quote": "la internista del Puerta lo identificó sin nosotros decirle lo que creíamos que era" } ] } }, "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-0134-source-0001", "AIBD-0134-source-0002", "AIBD-0134-source-0003", "AIBD-0134-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": "Daniel Verdú / El Español (2025). Un enfermo de cáncer de páncreas de 35 años, muy rotundo: 'ChatGPT detectó mi tumor antes que los médicos'. El Español, 5 August 2025.", "title": "Un enfermo de cáncer de páncreas de 35 años, muy rotundo: \"ChatGPT detectó mi tumor antes que los médicos\"", "url": "https://www.elespanol.com/ciencia/20250805/enfermo-cancer-pancreas-anos-rotundo-chatgpt-detecto-tumor-medicos/1003743876208_0.html", "date": "2025-08-05", "written_by": "independent", "quote": "Tras unas analíticas, la internista confirmó lo que ChatGPT había sugerido: efectivamente tenía un insulinoma." }, { "kind": "news report", "citation": "Bartolomé Saorin Rios / MARCA (2025). Gracias a ChatGPT descubre que tiene cáncer de páncreas: 'Ha marcado un antes y un después en nuestras vidas'. MARCA, 5 August 2025.", "title": "Gracias a ChatGPT descubre que tiene cáncer de páncreas: \"Ha marcado un antes y un después en nuestras vidas\"", "url": "https://www.marca.com/tecnologia/2025/08/05/gracias-chatgpt-descubre-cancer-pancreas-marcado-despues-vidas.html", "date": "2025-08-05", "written_by": "independent", "quote": "Este video está colgado en las redes de su pareja María Hernández-Alcalá y ya acumula más de 1 millón de visualizaciones." }, { "kind": "news report", "citation": "Candela Hornero / Telecinco (2025). ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico. Telecinco.es, 5 August 2025.", "title": "ChatGPT le ayuda a descubrir que tiene cáncer de páncreas tras años sin diagnóstico", "url": "https://www.telecinco.es/noticias/salud/20250805/chatgpt-ayuda-descubrir-cancer-pancreas-anos-sin-diagnostico_18_016301459.html", "date": "2025-08-05", "written_by": "independent", "quote": "ChatGPT detectó mi tumor antes que los médicos y hoy me operan del páncreas gracias a su diagnóstico" }, { "kind": "other", "citation": "María Hernández-Alcalá (@mariahalcala) (2025). Instagram reel carrying Dani's video and his written clarification. Instagram, 3 August 2025.", "title": "Esta historia ha marcado un antes y un después en nuestras vidas (Instagram reel DM508s_tAKz)", "url": "https://www.instagram.com/reel/DM508s_tAKz/", "date": "2025-08-03", "written_by": "beneficiary", "quote": "la internista del Puerta lo identificó sin nosotros decirle lo que creíamos que era" } ] } }, "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": "ChatGPT suggested an insulinoma after three undiagnosed years; a glucose sensor and ER blood tests confirmed it and surgery cured him.", "account": "Dani, 35, had three years of tingling, numbness, dizziness, double vision and a 15 kg weight gain. A neurologist and a primary-care doctor called them nothing important. He entered his symptoms, results and history into ChatGPT, which named an insulinoma as the likely cause. He bought a glucose sensor and recorded readings of 30 to 40 mg/dL. He went to an emergency department. An internist at Hospital Puerta de Hierro Majadahonda, Madrid, confirmed an insulinoma by blood tests. A month and a half later surgeons removed about two thirds of his pancreas and his symptoms went. His video was posted on Instagram on 3 August 2025. The dates of the query, visit and surgery were 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": "value_present", "value": { "kind": "text", "text": "ChatGPT suggested insulinoma as a possible explanation for the reported symptoms." }, "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": "Dani described using the most intelligent model available in ChatGPT. The account does not name that 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[].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 named; Dani said he used 'el modelo más inteligente de ChatGPT' (the most intelligent ChatGPT model)" } }, "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": "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 names an insulinoma that three years of specialist visits had missed; a Madrid internist confirms it and surgery cures the 35-year-old", "headline": "ChatGPT names an insulinoma doctors missed for three 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": [ "0ceafd256de27fe7eee2f880df02c43a273967c01252679c08c75e916609ae73" ], "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": "Dani, 35, had three years of tingling, numbness, dizziness, double vision and a 15 kg weight gain. A neurologist and a primary-care doctor called them nothing important. He entered his symptoms, results and history into ChatGPT, which named an insulinoma as the likely cause. He bought a glucose sensor and recorded readings of 30 to 40 mg/dL. He went to an emergency department. An internist at Hospital Puerta de Hierro Majadahonda, Madrid, confirmed an insulinoma by blood tests. A month and a half later surgeons removed about two thirds of his pancreas and his symptoms went. His video was posted on Instagram on 3 August 2025. The dates of the query, visit and surgery were not given.", "replication": { "produced_by": [ "A rare condition (Dani: “una enfermedad muy poco común (1 entre 1millon)”) with diffuse symptoms. A neurologist, after neck and brain magnetic resonance imaging (MRI) scans, and a primary-care doctor had dismissed them over three years.", "A user with “formación biosanitaria de muchos años” who entered the full symptom list, test results and clinical history into the chatbot. He could interpret its answer.", "A cheap self-test, a continuous glucose sensor, that could corroborate the hypothesis before the emergency visit. It read 30 to 40 mg/dL at night and between meals.", "An emergency internist at Puerta de Hierro who confirmed the insulinoma by blood tests, and a surgical team that removed two thirds of the pancreas." ], "must_hold": [ "The chatbot's hypothesis is treated as a reason to be tested, not as a diagnosis. Dani says he went to doctors before, during and after.", "The suspected condition has an objective marker that a patient or clinician can measure quickly.", "The user can state symptoms and prior results in enough detail for the model to rank a rare cause. Two doctors had the same information." ], "echoes": [ { "setting": "AIBD-0038: ChatGPT proposes ALMS1 for a 44-year-old nephrology patient (consumer chatbot supplies a rare diagnosis that clinicians then confirm).", "outcome": "unknown" }, { "setting": "AIBD-0060: ChatGPT suggests tethered cord syndrome for a child seen by 17 doctors.", "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": "Dani entered his symptoms, test results, and medical history into ChatGPT. He used a glucose sensor that recorded readings of 30 to 40 mg/dL, then sought emergency care. An internist confirmed the insulinoma, and a surgical team that treated him removed about two-thirds of his pancreas." } } }, "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-0133", "case_url": "records/AIBD-0133.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The NWM-based assisted-driving system judged the driver unresponsive, ran the emergency pull-over and placed the SOS call that woke him. The car was already under assisted driving. Its specific contribution was therefore taking the car out of the live lane and rousing the driver rather than keeping it in lane. The sequence is known only from the owner's video as relayed by independent outlets. NIO's founder confirmed the function's design. No telemetry, police report or third-party verification was published." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0133", "title": "NIO owner falls asleep at 110 km/h on a rainy highway; his car's NWM emergency pull-over stops it and an SOS call wakes him", "summary": "Driver asleep at 110 km/h on a rainy highway; NIO's NWM emergency pull-over stopped the car and an SOS call woke him, 1 June 2025.", "date_observed": "2025-06-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Around midnight on 1 June 2025, a NIO owner fell asleep driving at 110 km/h on a highway in rain in China. NIO's assisted driving was engaged. The NIO World Model (NWM) emergency autonomous pull-over function triggered, and an SOS call woke him. NIO had begun pushing it to more than 400,000 Banyan-platform cars on 30 May 2025. He posted a video on social media on 2 June. NIO founder William Li reposted it, describing the function (gradual deceleration, a lane change to the rightmost emergency lane, hazard lights, SOS intervention). He stressed that assisted driving is not autonomous driving. No crash or injury was reported. The vehicle model and highway are not named.", "category": "protective response", "unexpected": false, "purpose": "NIO's first NWM release, pushed to Banyan-platform cars from 30 May 2025, added an “emergency autonomous pull-over”. It works on a highway or urban expressway when the assisted-driving system detects an unresponsive driver. The car slows gradually, moves to the rightmost emergency lane, turns on its hazard lights and triggers an SOS call. CnEVPost notes that NIO cars previously could only decelerate and stop within the current lane. This event is that function doing what it was built to do.", "system": "NIO World Model (NWM) emergency autonomous pull-over function within NIO's assisted-driving system", "model_version": "First release of NIO World Model (NWM), pushed to more than 400,000 Banyan-platform cars from 30 May 2025; the vehicle model and software build are not reported", "developer": "NIO", "deployer": "NIO", "location": null, "domain": "Road vehicle crash avoidance", "segment": "Consumer driver-assistance software on a privately owned vehicle", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-05-30-3168" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "One identifiable driver, asleep at 110 km/h in rain on a highway, was brought to a stop and woken without a crash. The owner's account, as relayed by the outlets, calls it a rescue (“挽救”). The account does not establish that a crash was imminent." } ], "ai_role": { "status": "assessed", "statement": "The NWM-based assisted-driving system judged the driver unresponsive, ran the emergency pull-over and placed the SOS call that woke him. The car was already under assisted driving. Its specific contribution was therefore taking the car out of the live lane and rousing the driver rather than keeping it in lane. The sequence is known only from the owner's video as relayed by independent outlets. NIO's founder confirmed the function's design. No telemetry, police report or third-party verification was published." }, "evidence": [ { "kind": "news report", "citation": "快科技 / 驱动之家 (2025). 蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶. news.mydrivers.com.", "title": "蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶 [NIO owner falls asleep at 110 km/h on a rainy night highway, triggering emergency autonomous pull-over; William Li: assisted driving is not autonomous driving]", "url": "https://news.mydrivers.com/1/1051/1051320.htm", "date": "2025-06-02", "written_by": "independent", "quote": "他在6月1日凌晨12点开车睡着了，靠车辆自主靠边停车挽救" }, { "kind": "news report", "citation": "新浪科技 (闫妍), via 和讯网 (2025). 李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主. auto.hexun.com.", "title": "李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主 [William Li responds to NIO owner who fell asleep on the highway: assisted driving is not autonomous driving; automatic emergency pull-over triggered, SOS call woke the owner]", "url": "http://auto.hexun.com/2025-06-03/219371749.html", "date": "2025-06-03", "written_by": "independent", "quote": "触发了自动紧急靠边停车，并通过SOS电话方式唤醒了车主" }, { "kind": "news report", "citation": "易车讯, via 中金在线 (2025). 李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全. auto.cnfol.com.", "title": "李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全 [William Li responds to owner who fell asleep on the highway: assisted driving is not autonomous driving, take care on the drive back to work]", "url": "http://auto.cnfol.com/xincheshangshi/20250603/31358495.shtml", "date": "2025-06-03", "written_by": "independent", "quote": "在5月30日宣布，蔚来世界模型 NWM首个版本开启推送" }, { "kind": "news report", "citation": "中关村在线 (2025). 蔚来车主高速睡着，车辆自动靠边停车. news.zol.com.cn; republished by 新浪汽车 (k.sina.com.cn) on 3 June 2025.", "title": "蔚来车主高速睡着，车辆自动靠边停车 [NIO owner falls asleep on the highway, car pulls over automatically]", "url": "https://news.zol.com.cn/991/9916102.html", "date": "2025-06-02", "written_by": "independent", "quote": "以110公里/小时的速度行驶时，自己在高速公路上不慎睡着" }, { "kind": "news report", "citation": "Zhang, Phate (2025). [Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities. CnEVPost.", "title": "[Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities", "url": "https://cnevpost.com/2025/05/30/nio-rolls-out-first-version-nwm/", "date": "2025-05-30", "written_by": "independent", "quote": "With NWM, if the vehicle detects driver dysfunction, it will autonomously pull over to the roadside." }, { "kind": "news report", "citation": "CnEVPost (2026). Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience. CnEVPost, 28 January 2026.", "title": "Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience", "url": "https://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "date": "2026-01-28", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "The car was running the first NIO World Model (NWM) release with the “emergency autonomous pull-over” function, pushed to Banyan-platform cars from 30 May 2025.", "Assisted driving was engaged on a highway, the function's stated operating domain (highways and urban expressways).", "The assisted-driving system judged the driver unresponsive. He did not respond to the escalating alarms NIO describes. The pull-over and the SOS call therefore ran to completion." ], "must_hold": [ "Driver monitoring must detect an unresponsive driver. The road must offer a rightmost emergency lane to stop in.", "The SOS call needs working connectivity and a NIO emergency-response channel to reach the driver or a responder.", "NIO and its founder present the function as a safety net within assisted driving, not autonomous driving. The driver remains responsible for staying awake." ], "echoes": [] }, "paired_incident": null, "headline": "NIO's NWM emergency pull-over stops a sleeping driver's car", "metric": null, "comparator": null, "later": { "status": "scaled", "date": "2026-01-28", "note": "NIO rolled out a major NWM update in January 2026 to more than 460,000 Banyan-system vehicles with Cedar and Cedar S models to follow; the report does not discuss the pull-over function itself.", "quote": "The update will first become available to over 460,000 vehicles equipped with the Banyan system. Models featuring the Cedar and Cedar S systems will also receive the update shortly.", "url": "https://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/" }, "value": null, "developer_country": "China", "deployer_country": "China", "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/2025-05-30-3168", "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://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "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/Nio_Inc.; https://en.wikipedia.org/wiki/Nio_Inc.", "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-0133", "catalog_version": "1.0.0", "source_fingerprint": "13ec2ded56728532b6aa3c142cfe7bd1ae46dcb7c8841fd49a4063c25a533414", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0133-source-0001", "AIBD-0133-source-0002", "AIBD-0133-source-0003", "AIBD-0133-source-0004", "AIBD-0133-source-0005", "AIBD-0133-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": "NIO", "deployer": "NIO" } }, "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 NWM-based assisted-driving system judged the driver unresponsive, ran the emergency pull-over and placed the SOS call that woke him. The car was already under assisted driving. Its specific contribution was therefore taking the car out of the live lane and rousing the driver rather than keeping it in lane. The sequence is known only from the owner's video as relayed by independent outlets. NIO's founder confirmed the function's design. No telemetry, police report or third-party verification was 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": "protective response", "ai_role": { "status": "assessed", "statement": "The NWM-based assisted-driving system judged the driver unresponsive, ran the emergency pull-over and placed the SOS call that woke him. The car was already under assisted driving. 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"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": "2025-05-30-3168" } } }, "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": "2026-01-28", "note": "NIO rolled out a major NWM update in January 2026 to more than 460,000 Banyan-system vehicles with Cedar and Cedar S models to follow; the report does not discuss the pull-over function itself.", "quote": "The update will first become available to over 460,000 vehicles equipped with the Banyan system. Models featuring the Cedar and Cedar S systems will also receive the update shortly.", "url": "https://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/" } } }, "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-0133" } }, "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": "Around midnight on 1 June 2025, a NIO owner fell asleep driving at 110 km/h on a highway in rain in China. NIO's assisted driving was engaged. The NIO World Model (NWM) emergency autonomous pull-over function triggered, and an SOS call woke him. NIO had begun pushing it to more than 400,000 Banyan-platform cars on 30 May 2025. He posted a video on social media on 2 June. NIO founder William Li reposted it, describing the function (gradual deceleration, a lane change to the rightmost emergency lane, hazard lights, SOS intervention). He stressed that assisted driving is not autonomous driving. No crash or injury was reported. The vehicle model and highway are not named.", "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": 3, "reason": "One identifiable driver, asleep at 110 km/h in rain on a highway, was brought to a stop and woken without a crash. The owner's account, as relayed by the outlets, calls it a rescue (“挽救”). The account does not establish that a crash was imminent." } ] } }, "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": "NIO's first NWM release, pushed to Banyan-platform cars from 30 May 2025, added an “emergency autonomous pull-over”. It works on a highway or urban expressway when the assisted-driving system detects an unresponsive driver. The car slows gradually, moves to the rightmost emergency lane, turns on its hazard lights and triggers an SOS call. CnEVPost notes that NIO cars previously could only decelerate and stop within the current lane. This event is that function doing what it was built to do." } }, "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": "China", "deployer_country": "China" } }, "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": "NIO's first NWM release, pushed to Banyan-platform cars from 30 May 2025, added an “emergency autonomous pull-over”. It works on a highway or urban expressway when the assisted-driving system detects an unresponsive driver. The car slows gradually, moves to the rightmost emergency lane, turns on its hazard lights and triggers an SOS call. CnEVPost notes that NIO cars previously could only decelerate and stop within the current lane. This event is that function doing what it was built to do." } }, "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": "2025-06-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-05-30-3168", "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://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "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/Nio_Inc.; https://en.wikipedia.org/wiki/Nio_Inc.", "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 car was running the first NIO World Model (NWM) release with the “emergency autonomous pull-over” function, pushed to Banyan-platform cars from 30 May 2025.", "Assisted driving was engaged on a highway, the function's stated operating domain (highways and urban expressways).", "The assisted-driving system judged the driver unresponsive. He did not respond to the escalating alarms NIO describes. The pull-over and the SOS call therefore ran to completion." ], "must_hold": [ "Driver monitoring must detect an unresponsive driver. The road must offer a rightmost emergency lane to stop in.", "The SOS call needs working connectivity and a NIO emergency-response channel to reach the driver or a responder.", "NIO and its founder present the function as a safety net within assisted driving, not autonomous driving. The driver remains responsible for staying awake." ], "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/2025-05-30-3168", "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://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "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/Nio_Inc.; https://en.wikipedia.org/wiki/Nio_Inc.", "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": "Road vehicle crash avoidance", "segment": "Consumer driver-assistance software on a privately owned vehicle" } }, "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": "快科技 / 驱动之家 (2025). 蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶. news.mydrivers.com.", "title": "蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶 [NIO owner falls asleep at 110 km/h on a rainy night highway, triggering emergency autonomous pull-over; William Li: assisted driving is not autonomous driving]", "url": "https://news.mydrivers.com/1/1051/1051320.htm", "date": "2025-06-02", "written_by": "independent", "quote": "他在6月1日凌晨12点开车睡着了，靠车辆自主靠边停车挽救" }, { "kind": "news report", "citation": "新浪科技 (闫妍), via 和讯网 (2025). 李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主. auto.hexun.com.", "title": "李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主 [William Li responds to NIO owner who fell asleep on the highway: assisted driving is not autonomous driving; automatic emergency pull-over triggered, SOS call woke the owner]", "url": "http://auto.hexun.com/2025-06-03/219371749.html", "date": "2025-06-03", "written_by": "independent", "quote": "触发了自动紧急靠边停车，并通过SOS电话方式唤醒了车主" }, { "kind": "news report", "citation": "易车讯, via 中金在线 (2025). 李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全. auto.cnfol.com.", "title": "李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全 [William Li responds to owner who fell asleep on the highway: assisted driving is not autonomous driving, take care on the drive back to work]", "url": "http://auto.cnfol.com/xincheshangshi/20250603/31358495.shtml", "date": "2025-06-03", "written_by": "independent", "quote": "在5月30日宣布，蔚来世界模型 NWM首个版本开启推送" }, { "kind": "news report", "citation": "中关村在线 (2025). 蔚来车主高速睡着，车辆自动靠边停车. news.zol.com.cn; republished by 新浪汽车 (k.sina.com.cn) on 3 June 2025.", "title": "蔚来车主高速睡着，车辆自动靠边停车 [NIO owner falls asleep on the highway, car pulls over automatically]", "url": "https://news.zol.com.cn/991/9916102.html", "date": "2025-06-02", "written_by": "independent", "quote": "以110公里/小时的速度行驶时，自己在高速公路上不慎睡着" }, { "kind": "news report", "citation": "Zhang, Phate (2025). [Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities. CnEVPost.", "title": "[Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities", "url": "https://cnevpost.com/2025/05/30/nio-rolls-out-first-version-nwm/", "date": "2025-05-30", "written_by": "independent", "quote": "With NWM, if the vehicle detects driver dysfunction, it will autonomously pull over to the roadside." }, { "kind": "news report", "citation": "CnEVPost (2026). Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience. CnEVPost, 28 January 2026.", "title": "Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience", "url": "https://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "date": "2026-01-28", "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-0133-source-0001", "AIBD-0133-source-0002", "AIBD-0133-source-0003", "AIBD-0133-source-0004", "AIBD-0133-source-0005", "AIBD-0133-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": "快科技 / 驱动之家 (2025). 蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶. news.mydrivers.com.", "title": "蔚来车主雨夜高速110km/h时速睡着触发紧急自主靠边停车 李斌：辅助驾驶不是自动驾驶 [NIO owner falls asleep at 110 km/h on a rainy night highway, triggering emergency autonomous pull-over; William Li: assisted driving is not autonomous driving]", "url": "https://news.mydrivers.com/1/1051/1051320.htm", "date": "2025-06-02", "written_by": "independent", "quote": "他在6月1日凌晨12点开车睡着了，靠车辆自主靠边停车挽救" }, { "kind": "news report", "citation": "新浪科技 (闫妍), via 和讯网 (2025). 李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主. auto.hexun.com.", "title": "李斌回应蔚来车主高速睡着：辅助驾驶不是自动驾驶，返工路上注意安全！触发自动紧急靠边停车，SOS电话唤醒车主 [William Li responds to NIO owner who fell asleep on the highway: assisted driving is not autonomous driving; automatic emergency pull-over triggered, SOS call woke the owner]", "url": "http://auto.hexun.com/2025-06-03/219371749.html", "date": "2025-06-03", "written_by": "independent", "quote": "触发了自动紧急靠边停车，并通过SOS电话方式唤醒了车主" }, { "kind": "news report", "citation": "易车讯, via 中金在线 (2025). 李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全. auto.cnfol.com.", "title": "李斌回应车主高速睡着：辅助驾驶不是自动驾驶 返工注意安全 [William Li responds to owner who fell asleep on the highway: assisted driving is not autonomous driving, take care on the drive back to work]", "url": "http://auto.cnfol.com/xincheshangshi/20250603/31358495.shtml", "date": "2025-06-03", "written_by": "independent", "quote": "在5月30日宣布，蔚来世界模型 NWM首个版本开启推送" }, { "kind": "news report", "citation": "中关村在线 (2025). 蔚来车主高速睡着，车辆自动靠边停车. news.zol.com.cn; republished by 新浪汽车 (k.sina.com.cn) on 3 June 2025.", "title": "蔚来车主高速睡着，车辆自动靠边停车 [NIO owner falls asleep on the highway, car pulls over automatically]", "url": "https://news.zol.com.cn/991/9916102.html", "date": "2025-06-02", "written_by": "independent", "quote": "以110公里/小时的速度行驶时，自己在高速公路上不慎睡着" }, { "kind": "news report", "citation": "Zhang, Phate (2025). [Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities. CnEVPost.", "title": "[Video] Nio rolls out first version of NWM, bringing enhanced smart driving capabilities", "url": "https://cnevpost.com/2025/05/30/nio-rolls-out-first-version-nwm/", "date": "2025-05-30", "written_by": "independent", "quote": "With NWM, if the vehicle detects driver dysfunction, it will autonomously pull over to the roadside." }, { "kind": "news report", "citation": "CnEVPost (2026). Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience. CnEVPost, 28 January 2026.", "title": "Nio rolls out major update to NWM driver assist software in China, delivering more human-like driving experience", "url": "https://cnevpost.com/2026/01/28/nio-rolls-out-major-update-to-nwm-driver-assist-china/", "date": "2026-01-28", "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": "Driver asleep at 110 km/h on a rainy highway; NIO's NWM emergency pull-over stopped the car and an SOS call woke him, 1 June 2025.", "account": "Around midnight on 1 June 2025, a NIO owner fell asleep driving at 110 km/h on a highway in rain in China. NIO's assisted driving was engaged. The NIO World Model (NWM) emergency autonomous pull-over function triggered, and an SOS call woke him. NIO had begun pushing it to more than 400,000 Banyan-platform cars on 30 May 2025. He posted a video on social media on 2 June. NIO founder William Li reposted it, describing the function (gradual deceleration, a lane change to the rightmost emergency lane, hazard lights, SOS intervention). He stressed that assisted driving is not autonomous driving. No crash or injury was reported. The vehicle model and highway are not named." } }, "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 detected that the driver was unresponsive and initiated an emergency pull-over, according to the owner’s account." }, "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": "NIO released the first NIO World Model to more than 400,000 cars using its Banyan platform on 30 May 2025." }, "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": "First release of NIO World Model (NWM), pushed to more than 400,000 Banyan-platform cars from 30 May 2025; the vehicle model and software build are 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": "The first NIO World Model release, distributed from 30 May 2025. The installed build is not identified.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "NIO World Model (NWM) emergency autonomous pull-over function within NIO's assisted-driving 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": "NIO" } }, "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": "NIO owner falls asleep at 110 km/h on a rainy highway; his car's NWM emergency pull-over stops it and an SOS call wakes him", "headline": "NIO's NWM emergency pull-over stops a sleeping driver's car" } }, "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": [ "13ec2ded56728532b6aa3c142cfe7bd1ae46dcb7c8841fd49a4063c25a533414" ], "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": "Around midnight on 1 June 2025, a NIO owner fell asleep driving at 110 km/h on a highway in rain in China. NIO's assisted driving was engaged. The NIO World Model (NWM) emergency autonomous pull-over function triggered, and an SOS call woke him. NIO had begun pushing it to more than 400,000 Banyan-platform cars on 30 May 2025. He posted a video on social media on 2 June. NIO founder William Li reposted it, describing the function (gradual deceleration, a lane change to the rightmost emergency lane, hazard lights, SOS intervention). He stressed that assisted driving is not autonomous driving. No crash or injury was reported. The vehicle model and highway are not named.", "replication": { "produced_by": [ "The car was running the first NIO World Model (NWM) release with the “emergency autonomous pull-over” function, pushed to Banyan-platform cars from 30 May 2025.", "Assisted driving was engaged on a highway, the function's stated operating domain (highways and urban expressways).", "The assisted-driving system judged the driver unresponsive. He did not respond to the escalating alarms NIO describes. The pull-over and the SOS call therefore ran to completion." ], "must_hold": [ "Driver monitoring must detect an unresponsive driver. The road must offer a rightmost emergency lane to stop in.", "The SOS call needs working connectivity and a NIO emergency-response channel to reach the driver or a responder.", "NIO and its founder present the function as a safety net within assisted driving, not autonomous driving. The driver remains responsible for staying awake." ], "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": 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-0101", "case_url": "records/AIBD-0101.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "AI classification of thermal-camera feeds generated the alerts that triggered staff intervention and train slow-downs. The figures (alerts, stops, crossings, zero deaths) are the department's own statements as reproduced verbatim in news reports. No official document is among the sources. No per-event counterfactual is established." }, { "label": "Comparison", "value": "the same 7.05 km Madukkarai railway stretch before the cameras (2008 to 2022)", "url": "https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months" }, { "label": "Before", "value": "Eleven wild elephants were killed on railway tracks in Madukkarai stretch from 2008 to 2022" }, { "label": "After", "value": "no elephant deaths on the railway tracks in the last 22 months (as of 21 December 2025)" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0101", "title": "AI thermal-camera early warning stops trains for elephants at Madukkarai, Tamil Nadu", "summary": "TN Forest Dept AI cameras on a 7.05 km rail stretch: 7,116+ alerts, thousands of train stops, zero elephant deaths since Feb 2024.", "date_observed": "2024-02-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Tamil Nadu Forest Department installed 24 hybrid thermal cameras with AI detection on 12 towers. They cover 7.05 km of the Ettimadai–Walayar railway section in Madukkarai forest range, Coimbatore district. They have operated since 9 February 2024. A trial run already generated alerts by 7 January 2024. Alerts go to a control room at Puthupathi. Its staff warn station masters and loco pilots and drive elephants away from the tracks. By 7 July 2026 the department's Additional Chief Secretary reported over 7,116 alerts and 3,281 train slow-downs or stops (DT Next reports 3,821). It also gave about 9,481 safe elephant crossings and no elephant deaths on the stretch. Eleven elephants were killed there between 2008 and 2022.", "category": "protective response", "unexpected": false, "purpose": "Detect elephants near railway tracks with thermal cameras and AI, alert forest staff and railway crews, and prevent train-elephant collisions.", "system": "AI-based elephant early warning system (24 hybrid thermal cameras on 12 towers, Elephant Control and Command Center, Puthupathi)", "model_version": null, "developer": null, "deployer": "Tamil Nadu Forest Department (Department of Environment, Climate Change and Forests), with railway station masters at Walayar and Ettimadai and loco pilots", "location": "Madukkarai, Coimbatore district, India", "domain": "Conservation and biodiversity", "segment": "Wildlife protection / railway operations", "benefit_type": "Increased Product Safety", "taxonomy_note": "The collisions avoided are between trains and elephants, so the gain is shared with conservation.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "The reports count train stops and crossings, not injuries avoided by crews or passengers." }, { "group": "organizational", "depth": 2, "reason": "The Forest Department reports zero elephant deaths on a stretch that recorded eleven deaths from 2008 to 2022, including three in November 2021." }, { "group": "societal", "depth": 3, "reason": "Reported 9,481 safe crossings and no track deaths for wild elephants on a stretch that recorded eleven deaths from 2008 to 2022. The number of deaths that would otherwise have occurred remains unknown." } ], "ai_role": { "status": "assessed", "statement": "AI classification of thermal-camera feeds generated the alerts that triggered staff intervention and train slow-downs. The figures (alerts, stops, crossings, zero deaths) are the department's own statements as reproduced verbatim in news reports. No official document is among the sources. No per-event counterfactual is established." }, "evidence": [ { "kind": "news report", "citation": "The New Indian Express (2026). AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range.", "title": "AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range", "url": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "date": "2026-07-08", "written_by": "independent", "quote": "has generated more than 7,116 real-time alerts regarding animal movement, leading to 3,281 instances of loco pilots stopping or slowing down trains" }, { "kind": "news report", "citation": "The New Indian Express (2025). No elephant deaths on railway tracks in Madukkarai stretch in 22 months.", "title": "No elephant deaths on railway tracks in Madukkarai stretch in 22 months", "url": "https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "date": "2025-12-21", "written_by": "independent", "quote": "Two dozen 360-degree-wide hybrid thermal cameras have been functioning since February 9, 2024 for a 7.05-kilometre distance" }, { "kind": "news report", "citation": "India Today (2025). Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants.", "title": "Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants", "url": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12", "date": "2025-08-12", "written_by": "independent", "quote": "In Madukkarai, the AI-powered early warning system has ensured zero elephant deaths on rail tracks since Nov 2023." }, { "kind": "news report", "citation": "DT Next (2026). AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths.", "title": "AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths", "url": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths", "date": "2026-07-07", "written_by": "independent", "quote": "resulting in 3,821 instances where trains were slowed down or halted to allow elephants to cross tracks safely" }, { "kind": "news report", "citation": "The Hindu (2024). Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants.", "title": "Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants", "url": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece", "date": "2024-01-07", "written_by": "independent", "quote": "Their intervention based on the early warning has already prevented several elephants from walking along the tracks or crossing them before train movement." } ], "replication": { "produced_by": [ "Thermal cameras covering a known elephant crossing corridor", "24-hour staffed control room that relays alerts to station masters and loco pilots", "Field staff able to move elephants away from the track", "Railway agreement to slow or stop trains on alert" ], "must_hold": [ "Camera coverage of the full vulnerable section", "Low false-alert rate so crews keep responding", "Communication chain (intercom, walkie-talkie, WhatsApp) functioning at night", "Elephant movement continuing to cross at monitored points" ], "echoes": [ { "date": "2024-01-07", "setting": "Trial run, same stretch, alerts to Forest Department only", "model_version": null, "outcome": "recurred", "source": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece" }, { "date": "2025-08-12", "setting": "Department statement: 6,592 safe crossings, zero deaths since Nov 2023", "model_version": null, "outcome": "recurred", "source": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12" }, { "date": "2026-07-07", "setting": "Department statement at 2.5 years: 9,481 crossings, zero deaths", "model_version": null, "outcome": "recurred", "source": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths" } ] }, "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 wildlife-reserve cameras failed to detect intruders and four rhinos were killed; Madukkarai's thermal cameras are the same detection-to-alert design protecting elephants." }, "headline": "AI thermal cameras stop trains for elephants at Madukkarai", "metric": "about 9,481 safe elephant crossings and no elephant deaths on the stretch", "comparator": { "against": "the same 7.05 km Madukkarai railway stretch before the cameras (2008 to 2022)", "before": "Eleven wild elephants were killed on railway tracks in Madukkarai stretch from 2008 to 2022", "after": "no elephant deaths on the railway tracks in the last 22 months (as of 21 December 2025)", "basis": "measured", "quote": "Eleven wild elephants were killed on railway tracks in Madukkarai stretch from 2008 to 2022 with the last being reported in November 2021 when three elephants died.", "url": "https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months" }, "later": { "status": "persisted", "date": "2026-07-08", "note": "At two and a half years the department reported about 9,481 safe crossings and still no elephant deaths on the stretch, and had added AI-powered drones to extend surveillance beyond the fixed cameras.", "quote": "\"These are the unsung heroes whose constant vigilance, dedication, and hard work have facilitated around 9,481 safe elephant crossings, ensuring zero elephant deaths on railway tracks and demonstrating how technology, human vigilance, and teamwork can come together to protect wildlife,\" said Supriya Sahu.", "url": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range" }, "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": "2026-10-20", "change": "Replaced “Southern Railway” in deployer with the sources' station masters at Walayar and Ettimadai and loco pilots, restated the trial as already generating alerts by 7 January 2024 rather than beginning then, attributed the zero-deaths figure to the Forest Department alone and dropped “endangered” from the societal reason, because no cited source names Southern Railway, dates the trial's start, quotes the railway on deaths or calls the elephants endangered.", "source": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range; https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece; https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "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.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months; https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "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/Tamil_Nadu_Forest_Department", "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: 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-0101", "catalog_version": "1.0.0", "source_fingerprint": "457b98c55b5eb77a2c5d54faa3fb9712f6689ce678abccfa92e2bfaa11b4378e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0101-source-0001", "AIBD-0101-source-0002", "AIBD-0101-source-0003", "AIBD-0101-source-0004", "AIBD-0101-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": "Tamil Nadu Forest Department (Department of Environment, Climate Change and Forests), with railway station masters at Walayar and Ettimadai and loco pilots" } }, "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": "AI classification of thermal-camera feeds generated the alerts that triggered staff intervention and train slow-downs. The figures (alerts, stops, crossings, zero deaths) are the department's own statements as reproduced verbatim in news reports. No official document is among the sources. No per-event counterfactual is 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": "protective response", "ai_role": { "status": "assessed", "statement": "AI classification of thermal-camera feeds generated the alerts that triggered staff intervention and train slow-downs. The figures (alerts, stops, crossings, zero deaths) are the department's own statements as reproduced verbatim in news reports. No official document is among the sources. 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The number of deaths that would otherwise have occurred remains unknown." } ] } }, "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. 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The number of deaths that would otherwise have occurred remains unknown." } ] } }, "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": "The reports count train stops and crossings, not injuries avoided by crews or passengers." }, { "group": "organizational", "depth": 2, "reason": "The Forest Department reports zero elephant deaths on a stretch that recorded eleven deaths from 2008 to 2022, including three in November 2021." }, { "group": "societal", "depth": 3, "reason": "Reported 9,481 safe crossings and no track deaths for wild elephants on a stretch that recorded eleven deaths from 2008 to 2022. The number of deaths that would otherwise have occurred remains unknown." } ] } }, "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": "AI wildlife-reserve cameras failed to detect intruders and four rhinos were killed; Madukkarai's thermal cameras are the same detection-to-alert design protecting elephants.", "links": [ { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and 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"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" }, 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"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": "about 9,481 safe elephant crossings and no elephant deaths on the stretch", "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": { "paired_incident": { "database": "AI Incident Database", "identifier": "219", "title": "Poachers Evaded AI Cameras and Killed Four Rhinos", "url": "https://incidentdatabase.ai/cite/219", "group": "societal", "basis": "AI wildlife-reserve cameras failed to detect intruders and four rhinos were killed; Madukkarai's thermal cameras are the same detection-to-alert design protecting elephants." } } }, "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": "2026-07-08", "note": "At two and a half years the department reported about 9,481 safe crossings and still no elephant deaths on the stretch, and had added AI-powered drones to extend surveillance beyond the fixed cameras.", "quote": "\"These are the unsung heroes whose constant vigilance, dedication, and hard work have facilitated around 9,481 safe elephant crossings, ensuring zero elephant deaths on railway tracks and demonstrating how technology, human vigilance, and teamwork can come together to protect wildlife,\" said Supriya Sahu.", "url": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range" } } }, "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-0101" } }, "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": "The collisions avoided are between trains 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": "The Tamil Nadu Forest Department installed 24 hybrid thermal cameras with AI detection on 12 towers. They cover 7.05 km of the Ettimadai–Walayar railway section in Madukkarai forest range, Coimbatore district. They have operated since 9 February 2024. A trial run already generated alerts by 7 January 2024. Alerts go to a control room at Puthupathi. Its staff warn station masters and loco pilots and drive elephants away from the tracks. By 7 July 2026 the department's Additional Chief Secretary reported over 7,116 alerts and 3,281 train slow-downs or stops (DT Next reports 3,821). It also gave about 9,481 safe elephant crossings and no elephant deaths on the stretch. Eleven elephants were killed there between 2008 and 2022.", "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": "The reports count train stops and crossings, not injuries avoided by crews or passengers." }, { "group": "organizational", "depth": 2, "reason": "The Forest Department reports zero elephant deaths on a stretch that recorded eleven deaths from 2008 to 2022, including three in November 2021." }, { "group": "societal", "depth": 3, "reason": "Reported 9,481 safe crossings and no track deaths for wild elephants on a stretch that recorded eleven deaths from 2008 to 2022. The number of deaths that would otherwise have occurred remains unknown." } ] } }, "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 elephants near railway tracks with thermal cameras and AI, alert forest staff and railway crews, and prevent train-elephant collisions." } }, "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": "Madukkarai, Coimbatore district, 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": "Detect elephants near railway tracks with thermal cameras and AI, alert forest staff and railway crews, and prevent train-elephant collisions." } }, "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": "2024-02-09", "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 219 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Replaced “Southern Railway” in deployer with the sources' station masters at Walayar and Ettimadai and loco pilots, restated the trial as already generating alerts by 7 January 2024 rather than beginning then, attributed the zero-deaths figure to the Forest Department alone and dropped “endangered” from the societal reason, because no cited source names Southern Railway, dates the trial's start, quotes the railway on deaths or calls the elephants endangered.", "source": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range; https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece; https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "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.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months; https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "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/Tamil_Nadu_Forest_Department", "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": [ "Thermal cameras covering a known elephant crossing corridor", "24-hour staffed control room that relays alerts to station masters and loco pilots", "Field staff able to move elephants away from the track", "Railway agreement to slow or stop trains on alert" ], "must_hold": [ "Camera coverage of the full vulnerable section", "Low false-alert rate so crews keep responding", "Communication chain (intercom, walkie-talkie, WhatsApp) functioning at night", "Elephant movement continuing to cross at monitored points" ], "echoes": [ { "date": "2024-01-07", "setting": "Trial run, same stretch, alerts to Forest Department only", "outcome": "recurred", "source": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece" }, { "date": "2025-08-12", "setting": "Department statement: 6,592 safe crossings, zero deaths since Nov 2023", "outcome": "recurred", "source": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12" }, { "date": "2026-07-07", "setting": "Department statement at 2.5 years: 9,481 crossings, zero deaths", "outcome": "recurred", "source": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths" } ] } } }, "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 covering a known elephant crossing corridor", "24-hour staffed control room that relays alerts to station masters and loco pilots", "Field staff able to move elephants away from the track", "Railway agreement to slow or stop trains on alert" ], "must_hold": [ "Camera coverage of the full vulnerable section", "Low false-alert rate so crews keep responding", "Communication chain (intercom, walkie-talkie, WhatsApp) functioning at night", "Elephant movement continuing to cross at monitored points" ], "echoes": [ { "date": "2024-01-07", "setting": "Trial run, same stretch, alerts to Forest Department only", "outcome": "recurred", "source": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece" }, { "date": "2025-08-12", "setting": "Department statement: 6,592 safe crossings, zero deaths since Nov 2023", "outcome": "recurred", "source": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12" }, { "date": "2026-07-07", "setting": "Department statement at 2.5 years: 9,481 crossings, zero deaths", "outcome": "recurred", "source": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths" } ] } } }, "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 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/219", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Replaced “Southern Railway” in deployer with the sources' station masters at Walayar and Ettimadai and loco pilots, restated the trial as already generating alerts by 7 January 2024 rather than beginning then, attributed the zero-deaths figure to the Forest Department alone and dropped “endangered” from the societal reason, because no cited source names Southern Railway, dates the trial's start, quotes the railway on deaths or calls the elephants endangered.", "source": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range; https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece; https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "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.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months; https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "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/Tamil_Nadu_Forest_Department", "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": "Conservation and biodiversity", "segment": "Wildlife protection / railway 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": "The New Indian Express (2026). AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range.", "title": "AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range", "url": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "date": "2026-07-08", "written_by": "independent", "quote": "has generated more than 7,116 real-time alerts regarding animal movement, leading to 3,281 instances of loco pilots stopping or slowing down trains" }, { "kind": "news report", "citation": "The New Indian Express (2025). No elephant deaths on railway tracks in Madukkarai stretch in 22 months.", "title": "No elephant deaths on railway tracks in Madukkarai stretch in 22 months", "url": "https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "date": "2025-12-21", "written_by": "independent", "quote": "Two dozen 360-degree-wide hybrid thermal cameras have been functioning since February 9, 2024 for a 7.05-kilometre distance" }, { "kind": "news report", "citation": "India Today (2025). Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants.", "title": "Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants", "url": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12", "date": "2025-08-12", "written_by": "independent", "quote": "In Madukkarai, the AI-powered early warning system has ensured zero elephant deaths on rail tracks since Nov 2023." }, { "kind": "news report", "citation": "DT Next (2026). AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths.", "title": "AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths", "url": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths", "date": "2026-07-07", "written_by": "independent", "quote": "resulting in 3,821 instances where trains were slowed down or halted to allow elephants to cross tracks safely" }, { "kind": "news report", "citation": "The Hindu (2024). Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants.", "title": "Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants", "url": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece", "date": "2024-01-07", "written_by": "independent", "quote": "Their intervention based on the early warning has already prevented several elephants from walking along the tracks or crossing them before train movement." } ] } }, "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-0101-source-0001", "AIBD-0101-source-0002", "AIBD-0101-source-0003", "AIBD-0101-source-0004", "AIBD-0101-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 New Indian Express (2026). AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range.", "title": "AI camera system along railway tracks ends jumbo deaths in Madukkarai forest range", "url": "https://www.newindianexpress.com/states/tamil-nadu/2026/Jul/08/ai-camera-system-along-railway-tracks-ends-jumbo-deaths-in-madukkarai-forest-range", "date": "2026-07-08", "written_by": "independent", "quote": "has generated more than 7,116 real-time alerts regarding animal movement, leading to 3,281 instances of loco pilots stopping or slowing down trains" }, { "kind": "news report", "citation": "The New Indian Express (2025). No elephant deaths on railway tracks in Madukkarai stretch in 22 months.", "title": "No elephant deaths on railway tracks in Madukkarai stretch in 22 months", "url": "https://www.newindianexpress.com/states/tamil-nadu/2025/Dec/21/no-elephant-deaths-on-railway-tracks-in-madukkarai-stretch-in-22-months", "date": "2025-12-21", "written_by": "independent", "quote": "Two dozen 360-degree-wide hybrid thermal cameras have been functioning since February 9, 2024 for a 7.05-kilometre distance" }, { "kind": "news report", "citation": "India Today (2025). Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants.", "title": "Tamil Nadu Forest Department shares video showing how AI ensures safety of elephants", "url": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12", "date": "2025-08-12", "written_by": "independent", "quote": "In Madukkarai, the AI-powered early warning system has ensured zero elephant deaths on rail tracks since Nov 2023." }, { "kind": "news report", "citation": "DT Next (2026). AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths.", "title": "AI powered elephant alert system enables 9,481 safe crossings, records zero train track deaths", "url": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths", "date": "2026-07-07", "written_by": "independent", "quote": "resulting in 3,821 instances where trains were slowed down or halted to allow elephants to cross tracks safely" }, { "kind": "news report", "citation": "The Hindu (2024). Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants.", "title": "Trial run of AI-based early warning system near Coimbatore proves promising as field staff save elephants", "url": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece", "date": "2024-01-07", "written_by": "independent", "quote": "Their intervention based on the early warning has already prevented several elephants from walking along the tracks or crossing them before train movement." } ] } }, "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": "TN Forest Dept AI cameras on a 7.05 km rail stretch: 7,116+ alerts, thousands of train stops, zero elephant deaths since Feb 2024.", "account": "The Tamil Nadu Forest Department installed 24 hybrid thermal cameras with AI detection on 12 towers. They cover 7.05 km of the Ettimadai–Walayar railway section in Madukkarai forest range, Coimbatore district. They have operated since 9 February 2024. A trial run already generated alerts by 7 January 2024. Alerts go to a control room at Puthupathi. Its staff warn station masters and loco pilots and drive elephants away from the tracks. By 7 July 2026 the department's Additional Chief Secretary reported over 7,116 alerts and 3,281 train slow-downs or stops (DT Next reports 3,821). It also gave about 9,481 safe elephant crossings and no elephant deaths on the stretch. Eleven elephants were killed there between 2008 and 2022." } }, "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 elephant early warning system (24 hybrid thermal cameras on 12 towers, Elephant Control and Command Center, Puthupathi)" } }, "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 thermal-camera early warning stops trains for elephants at Madukkarai, Tamil Nadu", "headline": "AI thermal cameras stop trains for elephants at Madukkarai" } }, "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": [ "457b98c55b5eb77a2c5d54faa3fb9712f6689ce678abccfa92e2bfaa11b4378e" ], "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 Tamil Nadu Forest Department installed 24 hybrid thermal cameras with AI detection on 12 towers. They cover 7.05 km of the Ettimadai–Walayar railway section in Madukkarai forest range, Coimbatore district. They have operated since 9 February 2024. A trial run already generated alerts by 7 January 2024. Alerts go to a control room at Puthupathi. Its staff warn station masters and loco pilots and drive elephants away from the tracks. By 7 July 2026 the department's Additional Chief Secretary reported over 7,116 alerts and 3,281 train slow-downs or stops (DT Next reports 3,821). It also gave about 9,481 safe elephant crossings and no elephant deaths on the stretch. Eleven elephants were killed there between 2008 and 2022.", "replication": { "produced_by": [ "Thermal cameras covering a known elephant crossing corridor", "24-hour staffed control room that relays alerts to station masters and loco pilots", "Field staff able to move elephants away from the track", "Railway agreement to slow or stop trains on alert" ], "must_hold": [ "Camera coverage of the full vulnerable section", "Low false-alert rate so crews keep responding", "Communication chain (intercom, walkie-talkie, WhatsApp) functioning at night", "Elephant movement continuing to cross at monitored points" ], "echoes": [ { "date": "2024-01-07", "setting": "Trial run, same stretch, alerts to Forest Department only", "outcome": "recurred", "source": "https://www.thehindu.com/news/cities/Coimbatore/trial-run-of-ai-based-early-warning-system-near-coimbatore-proves-promising-as-field-staff-save-elephants/article67716099.ece" }, { "date": "2025-08-12", "setting": "Department statement: 6,592 safe crossings, zero deaths since Nov 2023", "outcome": "recurred", "source": "https://www.indiatoday.in/trending-news/story/tamil-nadu-forest-department-shares-video-showing-how-ai-ensures-safety-of-elephants-2770278-2025-08-12" }, { "date": "2026-07-07", "setting": "Department statement at 2.5 years: 9,481 crossings, zero deaths", "outcome": "recurred", "source": "https://www.dtnext.in/news/tamilnadu/ai-powered-elephant-alert-system-enables-9481-safe-crossings-records-zero-train-track-deaths" } ] } } }, "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-0122", "case_url": "records/AIBD-0122.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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. 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[].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 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.", "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. 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" }, "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" }, 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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-0119", "case_url": "records/AIBD-0119.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The sources attribute three things to the robot's AI. It fused MRI, DTI, CT and DSA into one 3D image showing fiber tracts and clot. It allowed a rehearsal so the skull opening and path avoided the tracts. It gave green-amber-red warnings during surgery when the instrument strayed from the planned path. Clot removal, hemostasis and the awake function tests were done by the surgical and anesthesia teams. No source compares this with conventional surgery for this patient. None says what in the system is machine learning rather than image processing, so the counterfactual is unproven. One sentence in two reports calls the patient Mr. Vinh; all otherwise call him Mr. Hải." }, { "label": "Comparison", "value": "conventional (traditional) brain surgery under general anesthesia", "url": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/" }, { "label": "Before", "value": "nếu mổ theo phương pháp truyền thống, nguy cơ di chứng cao (high risk of sequelae with the traditional method)" }, { "label": "After", "value": "khi được mổ lấy máu tụ tỉnh thức bằng robot AI, ông có thể song ca cùng bác sĩ ngay khi đang mổ (awake robot-assisted clot removal while talking and singing)" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0119", "title": "Awake robot-guided surgery removes a 4 cm brain hemorrhage from a 58-year-old man at Tam Anh General Hospital, Ho Chi Minh City", "summary": "Awake surgery guided by the Modus V Synaptive robot removed a 58-year-old stroke patient's 4 cm clot at Tam Anh Hospital; full recovery.", "date_observed": "2023-08-10", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 10 August 2023 a 58-year-old man, Mr. Hải, reached Tam Anh General Hospital at 14:00. He was drowsy and weak over 24 hours after a hemorrhagic stroke. Magnetic resonance imaging (MRI) showed a clot up to 4 cm pressing on motor and language areas. Surgery began at 17:00 under local anesthesia. First the Modus V Synaptive robot's AI fused MRI, DTI, computed tomography (CT) and DSA scans into one 3D view of fiber tracts and clot. There neurosurgeon Chu Tấn Sĩ rehearsed the approach. In surgery its traffic-light warnings policed the path while the awake patient talked and moved on request. About 40 ml of clot came out in just over 30 minutes. CT later showed none left and he went home on day six.", "category": "protective response", "unexpected": false, "purpose": "Tam Anh launched the Modus V Synaptive robot in May 2023 for brain tumor, hemorrhagic stroke and other neurosurgery. It shows fiber tracts on fused images, allows 3D rehearsal and warns when the instrument leaves the planned path. This use was that purpose.", "system": "Modus V Synaptive robotic digital microscope with Neuro-Navigation AI (image fusion, fiber-tract display, surgical simulation, trajectory warnings)", "model_version": "Modus V™", "developer": "Synaptive Medical Inc. (Toronto)", "deployer": "Tam Anh General Hospital, Ho Chi Minh City", "location": "Ho Chi Minh City, Vietnam", "domain": "Healthcare (neurosurgery)", "segment": "Haemorrhagic stroke surgery", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2023-08-14-9862" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "The surgeons said that without urgent surgery he faced hemiplegia, speech loss, blurred vision or death. After the guided awake operation CT showed no residual clot. His movement, vision and cognition were normal and he went home on day six. The life-saving element belongs to the operation as a whole. The robot's own contribution was preserving function." } ], "ai_role": { "status": "assessed", "statement": "The sources attribute three things to the robot's AI. It fused MRI, DTI, CT and DSA into one 3D image showing fiber tracts and clot. It allowed a rehearsal so the skull opening and path avoided the tracts. It gave green-amber-red warnings during surgery when the instrument strayed from the planned path. Clot removal, hemostasis and the awake function tests were done by the surgical and anesthesia teams. No source compares this with conventional surgery for this patient. None says what in the system is machine learning rather than image processing, so the counterfactual is unproven. One sentence in two reports calls the patient Mr. Vinh; all otherwise call him Mr. Hải." }, "evidence": [ { "kind": "news report", "citation": "VietNamNet (2023). Người bệnh hát trong lúc được robot AI mổ não. VietNamNet, Sức khỏe.", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://vietnamnet.vn/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-2177047.html", "date": "2023-08-14", "written_by": "independent", "quote": "Nếu không mổ khẩn cấp, người bệnh đối mặt liệt nửa người, nói khó, nhìn mờ, thậm chí tử vong." }, { "kind": "news report", "citation": "Ngân D. / Báo Đầu Tư (2023). Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não. Báo Đầu Tư.", "title": "Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não", "url": "https://baodautu.vn/tin-moi-ve-y-te-ngay-148-nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-d196299.html", "date": "2023-08-14", "written_by": "independent", "quote": "Ông Hải (58 tuổi) bị đột quỵ xuất huyết não đã hơn 24 giờ, được mổ lấy máu tụ tỉnh thức bằng robot AI" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Người bệnh hát trong lúc được robot AI mổ não. Hospital case news (deployer's account).", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/", "date": "2023-08-18", "written_by": "deployer", "quote": "kết quả chụp CT 768 lát cắt cho thấy không còn máu tụ trong não, vận động, thị giác và nhận thức của người bệnh hoàn toàn bình thường" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM. Hospital news (system launch).", "title": "Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM", "url": "https://tamanhhospital.vn/tin-tuc/ra-mat-robot-mo-nao-duy-nhat-viet-nam-tai-bvdk-tam-anh-tphcm/", "date": "2023-05-29", "written_by": "deployer", "quote": "Robot có thể giúp bác sĩ thấy rõ các bó sợi thần kinh, các mô não lành xung quanh khối u, vùng tổn thương não" }, { "kind": "press release", "citation": "Synaptive Medical (2020). Synaptive Medical Announces Product Update to Flagship Surgical Technology, Modus V. Company press release, Toronto.", "title": "Synaptive Medical Announces Product Update to Flagship Surgical Technology, Modus V", "url": "https://www.synaptivemedical.com/news/synaptive-medical-announces-product-update-to-flagship-surgical-technology-modus-v-tm-automated-robotic-microscope", "date": "2020-01-15", "written_by": "makers", "quote": "a fully automated robotic digital microscope designed to assist surgeons in the operating room" } ], "replication": { "produced_by": [ "A large (4 cm) hemorrhagic-stroke clot compressing motor and language areas in a patient still able to cooperate with awake surgery", "A hospital that owns the Modus V Synaptive robot with navigation software and a team trained to run it", "Pre-operative fusion of MRI, DTI, CT and DSA and a simulated approach that avoids fiber tracts", "Local anesthesia and intensive-care anesthesiology able to keep the patient awake and stable for function testing" ], "must_hold": [ "Surgery within hours of arrival. The surgeon notes about two million brain cells are lost per minute after a stroke", "The surgeon follows the rehearsed trajectory and heeds the robot's warnings", "Awake testing during clot removal so any tract injury is noticed immediately" ], "echoes": [] }, "paired_incident": null, "headline": "Modus V Synaptive guides awake removal of a 4 cm brain clot", "metric": "About 40 ml of clot came out in just over 30 minutes", "comparator": { "against": "conventional (traditional) brain surgery under general anesthesia", "before": "nếu mổ theo phương pháp truyền thống, nguy cơ di chứng cao (high risk of sequelae with the traditional method)", "after": "khi được mổ lấy máu tụ tỉnh thức bằng robot AI, ông có thể song ca cùng bác sĩ ngay khi đang mổ (awake robot-assisted clot removal while talking and singing)", "basis": "assumed", "quote": "Ông Hải (58 tuổi) bị đột quỵ xuất huyết não đã hơn 24 giờ, dần lơ mơ yếu liệt, nếu mổ theo phương pháp truyền thống, nguy cơ di chứng cao nhưng khi được mổ lấy máu tụ tỉnh thức bằng robot AI, ông có thể song ca cùng bác sĩ ngay khi đang mổ.", "url": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/" }, "later": { "status": "unknown", "date": null, "note": "No report on the patient after discharge on day six was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "Canada", "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 the cited sources.", "source": "https://oecd.ai/en/incidents/2023-08-14-9862", "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 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, the model version from the cited sources.", "source": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/; https://www.synaptivemedical.com/news/synaptive-medical-announces-product-update-to-flagship-surgical-technology-modus-v-tm-automated-robotic-microscope", "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.synaptivemedical.com/contact-us/; https://tamanhhospital.vn/", "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 makers' press release 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-0119", "catalog_version": "1.0.0", "source_fingerprint": "e603a114bf59fa1fabdce8892a052cad025e112d8e70282017cf0e55fdaae32f", "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" } ], "source_ids": [ "AIBD-0119-source-0001", "AIBD-0119-source-0002", "AIBD-0119-source-0003", "AIBD-0119-source-0004", "AIBD-0119-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": "Synaptive Medical Inc. (Toronto)", "deployer": "Tam Anh General Hospital, Ho Chi Minh City" } }, "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 sources attribute three things to the robot's AI. It fused MRI, DTI, CT and DSA into one 3D image showing fiber tracts and clot. It allowed a rehearsal so the skull opening and path avoided the tracts. It gave green-amber-red warnings during surgery when the instrument strayed from the planned path. Clot removal, hemostasis and the awake function tests were done by the surgical and anesthesia teams. No source compares this with conventional surgery for this patient. None says what in the system is machine learning rather than image processing, so the counterfactual is unproven. One sentence in two reports calls the patient Mr. Vinh; all otherwise call him Mr. Hải." } } }, "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 sources attribute three things to the robot's AI. It fused MRI, DTI, CT and DSA into one 3D image showing fiber tracts and clot. It allowed a rehearsal so the skull opening and path avoided the tracts. It gave green-amber-red warnings during surgery when the instrument strayed from the planned path. Clot removal, hemostasis and the awake function tests were done by the surgical and anesthesia teams. No source compares this with conventional surgery for this patient. None says what in the system is machine learning rather than image processing, so the counterfactual is unproven. One sentence in two reports calls the patient Mr. Vinh; all otherwise call him Mr. Hải." } } }, "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 surgeons said that without urgent surgery he faced hemiplegia, speech loss, blurred vision or death. After the guided awake operation CT showed no residual clot. His movement, vision and cognition were normal and he went home on day six. The life-saving element belongs to the operation as a whole. The robot's own contribution was preserving function." } ] } }, "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, the makers' press release 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": "The surgeons said that without urgent surgery he faced hemiplegia, speech loss, blurred vision or death. After the guided awake operation CT showed no residual clot. His movement, vision and cognition were normal and he went home on day six. The life-saving element belongs to the operation as a whole. The robot's own contribution was preserving function." } ] } }, "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": 3, "reason": "The surgeons said that without urgent surgery he faced hemiplegia, speech loss, blurred vision or death. After the guided awake operation CT showed no residual clot. His movement, vision and cognition were normal and he went home on day six. The life-saving element belongs to the operation as a whole. The robot's own contribution was preserving function." } ] } }, "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": "About 40 ml of clot came out in just over 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": "2023-08-14-9862" } } }, "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 report on the patient after discharge on day six 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-0119" } }, "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" }, 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Người bệnh hát trong lúc được robot AI mổ não. VietNamNet, Sức khỏe.", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://vietnamnet.vn/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-2177047.html", "date": "2023-08-14", "written_by": "independent", "quote": "Nếu không mổ khẩn cấp, người bệnh đối mặt liệt nửa người, nói khó, nhìn mờ, thậm chí tử vong." }, { "kind": "news report", "citation": "Ngân D. / Báo Đầu Tư (2023). Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não. Báo Đầu Tư.", "title": "Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não", "url": "https://baodautu.vn/tin-moi-ve-y-te-ngay-148-nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-d196299.html", "date": "2023-08-14", "written_by": "independent", "quote": "Ông Hải (58 tuổi) bị đột quỵ xuất huyết não đã hơn 24 giờ, được mổ lấy máu tụ tỉnh thức bằng robot AI" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Người bệnh hát trong lúc được robot AI mổ não. Hospital case news (deployer's account).", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/", "date": "2023-08-18", "written_by": "deployer", "quote": "kết quả chụp CT 768 lát cắt cho thấy không còn máu tụ trong não, vận động, thị giác và nhận thức của người bệnh hoàn toàn bình thường" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM. Hospital news (system launch).", "title": "Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM", "url": "https://tamanhhospital.vn/tin-tuc/ra-mat-robot-mo-nao-duy-nhat-viet-nam-tai-bvdk-tam-anh-tphcm/", "date": "2023-05-29", "written_by": "deployer", "quote": "Robot có thể giúp bác sĩ thấy rõ các bó sợi thần kinh, các mô não lành xung quanh khối u, vùng tổn thương não" }, { "kind": "press release", "citation": "Synaptive Medical (2020). Synaptive Medical Announces Product Update to Flagship Surgical Technology, Modus V. 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Người bệnh hát trong lúc được robot AI mổ não. VietNamNet, Sức khỏe.", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://vietnamnet.vn/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-2177047.html", "date": "2023-08-14", "written_by": "independent", "quote": "Nếu không mổ khẩn cấp, người bệnh đối mặt liệt nửa người, nói khó, nhìn mờ, thậm chí tử vong." }, { "kind": "news report", "citation": "Ngân D. / Báo Đầu Tư (2023). Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não. Báo Đầu Tư.", "title": "Tin mới về y tế ngày 14/8: Người bệnh hát trong lúc được robot AI mổ não", "url": "https://baodautu.vn/tin-moi-ve-y-te-ngay-148-nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao-d196299.html", "date": "2023-08-14", "written_by": "independent", "quote": "Ông Hải (58 tuổi) bị đột quỵ xuất huyết não đã hơn 24 giờ, được mổ lấy máu tụ tỉnh thức bằng robot AI" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Người bệnh hát trong lúc được robot AI mổ não. Hospital case news (deployer's account).", "title": "Người bệnh hát trong lúc được robot AI mổ não", "url": "https://tamanhhospital.vn/tin-tuc/nguoi-benh-hat-trong-luc-duoc-robot-ai-mo-nao/", "date": "2023-08-18", "written_by": "deployer", "quote": "kết quả chụp CT 768 lát cắt cho thấy không còn máu tụ trong não, vận động, thị giác và nhận thức của người bệnh hoàn toàn bình thường" }, { "kind": "press release", "citation": "Tam Anh General Hospital (2023). Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM. Hospital news (system launch).", "title": "Ra mắt robot mổ não duy nhất Việt Nam tại BVĐK Tâm Anh TP.HCM", "url": "https://tamanhhospital.vn/tin-tuc/ra-mat-robot-mo-nao-duy-nhat-viet-nam-tai-bvdk-tam-anh-tphcm/", "date": "2023-05-29", "written_by": "deployer", "quote": "Robot có thể giúp bác sĩ thấy rõ các bó sợi thần kinh, các mô não lành xung quanh khối u, vùng tổn thương não" }, { "kind": "press release", "citation": "Synaptive Medical (2020). Synaptive Medical Announces Product Update to Flagship Surgical Technology, Modus V. Company press release, Toronto.", "title": "Synaptive Medical Announces Product Update to Flagship Surgical Technology, Modus V", "url": "https://www.synaptivemedical.com/news/synaptive-medical-announces-product-update-to-flagship-surgical-technology-modus-v-tm-automated-robotic-microscope", "date": "2020-01-15", "written_by": "makers", "quote": "a fully automated robotic digital microscope designed to assist surgeons in the operating room" } ] } }, "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": "Awake surgery guided by the Modus V Synaptive robot removed a 58-year-old stroke patient's 4 cm clot at Tam Anh Hospital; full recovery.", "account": "On 10 August 2023 a 58-year-old man, Mr. Hải, reached Tam Anh General Hospital at 14:00. He was drowsy and weak over 24 hours after a hemorrhagic stroke. Magnetic resonance imaging (MRI) showed a clot up to 4 cm pressing on motor and language areas. Surgery began at 17:00 under local anesthesia. First the Modus V Synaptive robot's AI fused MRI, DTI, computed tomography (CT) and DSA scans into one 3D view of fiber tracts and clot. There neurosurgeon Chu Tấn Sĩ rehearsed the approach. In surgery its traffic-light warnings policed the path while the awake patient talked and moved on request. About 40 ml of clot came out in just over 30 minutes. CT later showed none left and he went home on day six." } }, "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": "value_present", "value": { "kind": "text", "text": "The deployment used the Modus V system." }, "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": "Modus V™" } }, "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": "Modus V Synaptive robotic digital microscope with Neuro-Navigation AI (image fusion, fiber-tract display, surgical simulation, trajectory warnings)" } }, "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": "Synaptive Medical Inc. 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He was drowsy and weak over 24 hours after a hemorrhagic stroke. Magnetic resonance imaging (MRI) showed a clot up to 4 cm pressing on motor and language areas. Surgery began at 17:00 under local anesthesia. First the Modus V Synaptive robot's AI fused MRI, DTI, computed tomography (CT) and DSA scans into one 3D view of fiber tracts and clot. There neurosurgeon Chu Tấn Sĩ rehearsed the approach. In surgery its traffic-light warnings policed the path while the awake patient talked and moved on request. About 40 ml of clot came out in just over 30 minutes. CT later showed none left and he went home on day six.", "replication": { "produced_by": [ "A large (4 cm) hemorrhagic-stroke clot compressing motor and language areas in a patient still able to cooperate with awake surgery", "A hospital that owns the Modus V Synaptive robot with navigation software and a team trained to run it", "Pre-operative fusion of MRI, DTI, CT and DSA and a simulated approach that avoids fiber tracts", "Local anesthesia and intensive-care anesthesiology able to keep the patient awake and stable for function testing" ], "must_hold": [ "Surgery within hours of arrival. The surgeon notes about two million brain cells are lost per minute after a stroke", "The surgeon follows the rehearsed trajectory and heeds the robot's warnings", "Awake testing during clot removal so any tract injury is noticed immediately" ], "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-0115", "case_url": "records/AIBD-0115.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The model read the exercise-ECG signals and report features. It returned a 72% probability of severe coronary narrowing, above the moderate-risk verdict of the manual reading and risk score. On that basis the cardiologist ordered angiography, which showed the 99% stenosis, and he stented it. The counterfactual rests on the treating cardiologist's statement, carried by three outlets and the hospital's release. He said the manual reading would have led to drug treatment and follow-up only. The hospital that says so also built the model. The dates of the reading and procedure are not reported. The published model's specificity of 0.60 means such flags are often false positives." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0115", "title": "China Medical University Hospital's exercise-ECG AI catching a 99% coronary stenosis that manual reading had rated moderate, Taichung, 2022", "summary": "CMUH's exercise-ECG AI put severe-blockage odds at 72% where manual reading said follow-up; angiography found a 99% LAD stenosis, stented.", "date_observed": "2022-12-28", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A 75-year-old man surnamed Chen, with controlled hypertension, woke one day with chest tightness and palpitations first taken for reflux. Liberty Times puts the onset in 2021. At China Medical University Hospital (CMUH), Taichung, his exercise electrocardiogram (ECG) was read manually with a risk score. That gave about 50% blockage, moderate risk, follow-up only. The hospital's own Exercise ECG AI-Assisted Interpretation System, in use since May 2022, put the probability of severe (over 70%) narrowing at 72%. So cardiologist 吳宏彬 ordered angiography, found a 99% stenosis of the left anterior descending artery and stented it at once. He went home, chest tightness resolved. The hospital presented it on 28 December 2022. Test and procedure dates are not reported.", "category": "harm prevented", "unexpected": false, "purpose": "The hospital's AI Center built the system to judge from an exercise ECG whether a patient's coronary arteries are severely narrowed (over 70%). Manual reading of a dozen tracings is slow and, the hospital says, only 40–50% accurate. Flagging this patient's severe stenosis was exactly that purpose.", "system": "運動心電圖AI輔助判讀系統 (Exercise ECG AI-Assisted Interpretation System), China Medical University Hospital", "model_version": "Not reported. The hospital describes a deep-learning model that takes exercise-ECG signals at three time points plus 10 measured and 2 derived features (such as history and maximum workload), trained on nearly 1,000 exercise ECGs matched to catheterization data, accuracy about 80%, sensitivity 88%, in clinical use since May 2022; the authors' 2025 paper describes a convolutional recurrent network with three ECG slices and 12 features, AUC 0.83, sensitivity 0.89, specificity 0.60.", "developer": "China Medical University Hospital (中國醫藥大學附設醫院人工智慧中心), director 許凱程", "deployer": "China Medical University Hospital, Division of Cardiology (心臟血管系), Taichung", "location": "Taichung, Taiwan", "domain": "Healthcare: cardiology diagnostics (exercise stress ECG)", "segment": "Hospital cardiology; in-house clinical AI", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2022-12-28-624f" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A man with a 99% stenosis of the left anterior descending artery had it stented after the AI flagged a 72% probability of severe narrowing. The manual exercise-ECG reading had rated it moderate and left it for drug treatment and follow-up. The cardiologist put his chance of an acute infarction at 8 in 10. He said the stent removed the sudden-death risk. He left hospital, symptoms resolved. The hospital's headlines call it life-saving, but the harm was prospective and the account is the developer-hospital's own, so life-saving is not established." } ], "ai_role": { "status": "assessed", "statement": "The model read the exercise-ECG signals and report features. It returned a 72% probability of severe coronary narrowing, above the moderate-risk verdict of the manual reading and risk score. On that basis the cardiologist ordered angiography, which showed the 99% stenosis, and he stented it. The counterfactual rests on the treating cardiologist's statement, carried by three outlets and the hospital's release. He said the manual reading would have led to drug treatment and follow-up only. The hospital that says so also built the model. The dates of the reading and procedure are not reported. The published model's specificity of 0.60 means such flags are often false positives." }, "evidence": [ { "kind": "news report", "citation": "蔡淑媛 (2022). 胸悶、心悸以為胃食道逆流 竟慢性心肌梗塞99％ [Chest tightness and palpitations taken for reflux turn out to be chronic myocardial infarction, 99% blocked]. 自由健康網 (Liberty Times Health), Taichung, 28 December 2022; print edition 29 December 2022 as 以為胃食道逆流 AI判讀竟是慢性心肌梗塞, https://health.ltn.com.tw/article/paper/1559692.", "title": "胸悶、心悸以為胃食道逆流 竟慢性心肌梗塞99％", "url": "https://health.ltn.com.tw/article/breakingnews/4168516", "date": "2022-12-28", "written_by": "independent", "quote": "同步AI輔助判讀卻發現有嚴重心肌梗塞風險" }, { "kind": "news report", "citation": "寇世菁, 中廣新聞網 (BCC News) (2022). 75歲男起床胸悶心悸 中國附醫運動心電圖AI輔助判讀系統及時救命 [75-year-old man wakes with chest tightness and palpitations; CMUH exercise-ECG AI-assisted interpretation system saves his life in time]. Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "75歲男起床胸悶心悸 中國附醫運動心電圖AI輔助判讀系統及時救命", "url": "https://tw.news.yahoo.com/75%E6%AD%B2%E7%94%B7%E8%B5%B7%E5%BA%8A%E8%83%B8%E6%82%B6%E5%BF%83%E6%82%B8-%E4%B8%AD%E5%9C%8B%E9%99%84%E9%86%AB%E9%81%8B%E5%8B%95%E5%BF%83%E9%9B%BB%E5%9C%96ai%E8%BC%94%E5%8A%A9%E5%88%A4%E8%AE%80%E7%B3%BB%E7%B5%B1%E5%8F%8A%E6%99%82%E6%95%91%E5%91%BD-083734634.html", "date": "2022-12-28", "written_by": "independent", "quote": "運動心電圖檢查後，傳統人工判讀，認為追蹤即可" }, { "kind": "news report", "citation": "張雅淳, 周刊王CTWANT (2022). 男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死 [Man thought it was reflux; AI judged '72% vessel blockage' in time and he escaped sudden death]. Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死", "url": "https://tw.news.yahoo.com/%E7%94%B7%E5%AD%90%E4%BB%A5%E7%82%BA%E8%83%83%E9%A3%9F%E9%81%93%E9%80%86%E6%B5%81-ai%E5%8F%8A%E6%99%82%E5%88%A4%E6%96%B7-%E8%A1%80%E7%AE%A1%E5%A0%B5%E5%A1%9E72-%E9%80%83%E9%81%8E%E7%8C%9D%E6%AD%BB-083151068.html", "date": "2022-12-28", "written_by": "independent", "quote": "AI報告顯示：陳伯伯的冠狀動脈血管嚴重阻塞機率已高達72%" }, { "kind": "press release", "citation": "China Medical University Hospital, Public Relations Office 公關室 (2022). 75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」及時介入治療倖免於難 [75-year-old wakes with chest tightness and palpitations, coronary artery 99% blocked; exercise-ECG AI-assisted interpretation system intervenes in time]. CMUH news no. 8228, 29 December 2022.", "title": "75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」 及時介入治療倖免於難", "url": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8228", "date": "2022-12-29", "written_by": "makers", "quote": "結果顯示，陳伯伯左前降支狹窄竟已高達99%" }, { "kind": "peer-reviewed study", "citation": "Liang, H.-Y., Hsu, K.-C., Chien, S.-Y., Yeh, C.-Y., Sun, T.-H., Liu, M.-H., & Ng, K. K. (2025). Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease. Frontiers in Artificial Intelligence, 8, 1496109. PMID 40166362; PMC11955648.", "title": "Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease", "url": "https://doi.org/10.3389/frai.2025.1496109", "date": "2025-03-17", "written_by": "makers", "quote": "We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months." } ], "replication": { "produced_by": [ "A patient with atypical symptoms has an exercise ECG at a hospital that runs the AI model alongside the manual reading", "The model's probability of severe (over 70%) stenosis exceeds what the manual reading and risk score suggest. The cardiologist acts on it by ordering angiography", "Angiography confirms a severe stenosis that can be treated at once by stenting" ], "must_hold": [ "The model is run in addition to, not instead of, the clinician's reading. The hospital calls it assisted interpretation and a clinician made every decision", "The clinician is willing to send a patient to invasive angiography on an AI probability score. With the published model's specificity of 0.60, some such patients will have no severe lesion", "The patient is seen at a hospital that has the system. The sources describe it only at CMUH, in use from May 2022" ], "echoes": [] }, "paired_incident": null, "headline": "CMUH's exercise-ECG AI catches a 99% LAD stenosis rated moderate", "metric": "angiography found a 99% LAD stenosis, stented", "comparator": null, "later": { "status": "persisted", "date": "2025-03-17", "note": "The hospital's cardiology group published the algorithm in Frontiers in Artificial Intelligence in March 2025 with external validation on its own patients after February 2022 and at Asia University Hospital.", "quote": "Our study provides an efficient and accurate tool to identify patients with significant CAD by the AI-enhanced ExECG algorithm, which achieved an AUC of 0.83, a sensitivity of 0.89, and a specificity of 0.60, within 1 min.", "url": "https://doi.org/10.3389/frai.2025.1496109" }, "value": null, "developer_country": "Taiwan", "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/2022-12-28-624f", "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 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://doi.org/10.3389/frai.2025.1496109", "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://zh.wikipedia.org/wiki/%E4%B8%AD%E5%9C%8B%E9%86%AB%E8%97%A5%E5%A4%A7%E5%AD%B8%E9%99%84%E8%A8%AD%E9%86%AB%E9%99%A2; https://en.wikipedia.org/wiki/China_Medical_University_(Taiwan)", "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 China Medical University Hospital.", "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 makers' press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0115", "catalog_version": "1.0.0", "source_fingerprint": "ada0f6e4bf20d8d0f0d30c3ff523e1b613ca3f8d97c3269834cf564e62df77c8", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0115-source-0001", "AIBD-0115-source-0002", "AIBD-0115-source-0003", "AIBD-0115-source-0004", "AIBD-0115-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": "China Medical University Hospital (中國醫藥大學附設醫院人工智慧中心), director 許凱程", "deployer": "China Medical University Hospital, Division of Cardiology (心臟血管系), Taichung" } }, "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 read the exercise-ECG signals and report features. It returned a 72% probability of severe coronary narrowing, above the moderate-risk verdict of the manual reading and risk score. On that basis the cardiologist ordered angiography, which showed the 99% stenosis, and he stented it. The counterfactual rests on the treating cardiologist's statement, carried by three outlets and the hospital's release. He said the manual reading would have led to drug treatment and follow-up only. The hospital that says so also built the model. The dates of the reading and procedure are not reported. The published model's specificity of 0.60 means such flags are often false positives." } } }, "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 read the exercise-ECG signals and report features. It returned a 72% probability of severe coronary narrowing, above the moderate-risk verdict of the manual reading and risk score. On that basis the cardiologist ordered angiography, which showed the 99% stenosis, and he stented it. The counterfactual rests on the treating cardiologist's statement, carried by three outlets and the hospital's release. He said the manual reading would have led to drug treatment and follow-up only. The hospital that says so also built the model. The dates of the reading and procedure are not reported. The published model's specificity of 0.60 means such flags are often false positives." } } }, "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 man with a 99% stenosis of the left anterior descending artery had it stented after the AI flagged a 72% probability of severe narrowing. The manual exercise-ECG reading had rated it moderate and left it for drug treatment and follow-up. The cardiologist put his chance of an acute infarction at 8 in 10. He said the stent removed the sudden-death risk. He left hospital, symptoms resolved. The hospital's headlines call it life-saving, but the harm was prospective and the account is the developer-hospital's own, so life-saving 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: three independent news reports, the makers' peer-reviewed study 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": 3, "reason": "A man with a 99% stenosis of the left anterior descending artery had it stented after the AI flagged a 72% probability of severe narrowing. The manual exercise-ECG reading had rated it moderate and left it for drug treatment and follow-up. The cardiologist put his chance of an acute infarction at 8 in 10. He said the stent removed the sudden-death risk. He left hospital, symptoms resolved. The hospital's headlines call it life-saving, but the harm was prospective and the account is the developer-hospital's own, so life-saving 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": "personal", "depth": 3, "reason": "A man with a 99% stenosis of the left anterior descending artery had it stented after the AI flagged a 72% probability of severe narrowing. The manual exercise-ECG reading had rated it moderate and left it for drug treatment and follow-up. The cardiologist put his chance of an acute infarction at 8 in 10. He said the stent removed the sudden-death risk. He left hospital, symptoms resolved. The hospital's headlines call it life-saving, but the harm was prospective and the account is the developer-hospital's own, so life-saving 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", 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Liberty Times puts the onset in 2021. At China Medical University Hospital (CMUH), Taichung, his exercise electrocardiogram (ECG) was read manually with a risk score. That gave about 50% blockage, moderate risk, follow-up only. The hospital's own Exercise ECG AI-Assisted Interpretation System, in use since May 2022, put the probability of severe (over 70%) narrowing at 72%. So cardiologist 吳宏彬 ordered angiography, found a 99% stenosis of the left anterior descending artery and stented it at once. He went home, chest tightness resolved. The hospital presented it on 28 December 2022. Test and procedure dates are not reported.", "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": 3, "reason": "A man with a 99% stenosis of the left anterior descending artery had it stented after the AI flagged a 72% probability of severe narrowing. The manual exercise-ECG reading had rated it moderate and left it for drug treatment and follow-up. The cardiologist put his chance of an acute infarction at 8 in 10. He said the stent removed the sudden-death risk. He left hospital, symptoms resolved. The hospital's headlines call it life-saving, but the harm was prospective and the account is the developer-hospital's own, so life-saving 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": "The hospital's AI Center built the system to judge from an exercise ECG whether a patient's coronary arteries are severely narrowed (over 70%). Manual reading of a dozen tracings is slow and, the hospital says, only 40–50% accurate. Flagging this patient's severe stenosis was exactly 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": "Taichung, Taiwan", "developer_country": "Taiwan", "deployer_country": "Taiwan" } }, "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 hospital's AI Center built the system to judge from an exercise ECG whether a patient's coronary arteries are severely narrowed (over 70%). Manual reading of a dozen tracings is slow and, the hospital says, only 40–50% accurate. 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The cardiologist acts on it by ordering angiography", "Angiography confirms a severe stenosis that can be treated at once by stenting" ], "must_hold": [ "The model is run in addition to, not instead of, the clinician's reading. The hospital calls it assisted interpretation and a clinician made every decision", "The clinician is willing to send a patient to invasive angiography on an AI probability score. With the published model's specificity of 0.60, some such patients will have no severe lesion", "The patient is seen at a hospital that has the system. 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Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "75歲男起床胸悶心悸 中國附醫運動心電圖AI輔助判讀系統及時救命", "url": "https://tw.news.yahoo.com/75%E6%AD%B2%E7%94%B7%E8%B5%B7%E5%BA%8A%E8%83%B8%E6%82%B6%E5%BF%83%E6%82%B8-%E4%B8%AD%E5%9C%8B%E9%99%84%E9%86%AB%E9%81%8B%E5%8B%95%E5%BF%83%E9%9B%BB%E5%9C%96ai%E8%BC%94%E5%8A%A9%E5%88%A4%E8%AE%80%E7%B3%BB%E7%B5%B1%E5%8F%8A%E6%99%82%E6%95%91%E5%91%BD-083734634.html", "date": "2022-12-28", "written_by": "independent", "quote": "運動心電圖檢查後，傳統人工判讀，認為追蹤即可" }, { "kind": "news report", "citation": "張雅淳, 周刊王CTWANT (2022). 男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死 [Man thought it was reflux; AI judged '72% vessel blockage' in time and he escaped sudden death]. Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死", "url": "https://tw.news.yahoo.com/%E7%94%B7%E5%AD%90%E4%BB%A5%E7%82%BA%E8%83%83%E9%A3%9F%E9%81%93%E9%80%86%E6%B5%81-ai%E5%8F%8A%E6%99%82%E5%88%A4%E6%96%B7-%E8%A1%80%E7%AE%A1%E5%A0%B5%E5%A1%9E72-%E9%80%83%E9%81%8E%E7%8C%9D%E6%AD%BB-083151068.html", "date": "2022-12-28", "written_by": "independent", "quote": "AI報告顯示：陳伯伯的冠狀動脈血管嚴重阻塞機率已高達72%" }, { "kind": "press release", "citation": "China Medical University Hospital, Public Relations Office 公關室 (2022). 75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」及時介入治療倖免於難 [75-year-old wakes with chest tightness and palpitations, coronary artery 99% blocked; exercise-ECG AI-assisted interpretation system intervenes in time]. CMUH news no. 8228, 29 December 2022.", "title": "75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」 及時介入治療倖免於難", "url": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8228", "date": "2022-12-29", "written_by": "makers", "quote": "結果顯示，陳伯伯左前降支狹窄竟已高達99%" }, { "kind": "peer-reviewed study", "citation": "Liang, H.-Y., Hsu, K.-C., Chien, S.-Y., Yeh, C.-Y., Sun, T.-H., Liu, M.-H., & Ng, K. K. (2025). Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease. Frontiers in Artificial Intelligence, 8, 1496109. PMID 40166362; PMC11955648.", "title": "Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease", "url": "https://doi.org/10.3389/frai.2025.1496109", "date": "2025-03-17", "written_by": "makers", "quote": "We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months." } ] } }, "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-0115-source-0001", "AIBD-0115-source-0002", "AIBD-0115-source-0003", "AIBD-0115-source-0004", "AIBD-0115-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": "蔡淑媛 (2022). 胸悶、心悸以為胃食道逆流 竟慢性心肌梗塞99％ [Chest tightness and palpitations taken for reflux turn out to be chronic myocardial infarction, 99% blocked]. 自由健康網 (Liberty Times Health), Taichung, 28 December 2022; print edition 29 December 2022 as 以為胃食道逆流 AI判讀竟是慢性心肌梗塞, https://health.ltn.com.tw/article/paper/1559692.", "title": "胸悶、心悸以為胃食道逆流 竟慢性心肌梗塞99％", "url": "https://health.ltn.com.tw/article/breakingnews/4168516", "date": "2022-12-28", "written_by": "independent", "quote": "同步AI輔助判讀卻發現有嚴重心肌梗塞風險" }, { "kind": "news report", "citation": "寇世菁, 中廣新聞網 (BCC News) (2022). 75歲男起床胸悶心悸 中國附醫運動心電圖AI輔助判讀系統及時救命 [75-year-old man wakes with chest tightness and palpitations; CMUH exercise-ECG AI-assisted interpretation system saves his life in time]. Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "75歲男起床胸悶心悸 中國附醫運動心電圖AI輔助判讀系統及時救命", "url": "https://tw.news.yahoo.com/75%E6%AD%B2%E7%94%B7%E8%B5%B7%E5%BA%8A%E8%83%B8%E6%82%B6%E5%BF%83%E6%82%B8-%E4%B8%AD%E5%9C%8B%E9%99%84%E9%86%AB%E9%81%8B%E5%8B%95%E5%BF%83%E9%9B%BB%E5%9C%96ai%E8%BC%94%E5%8A%A9%E5%88%A4%E8%AE%80%E7%B3%BB%E7%B5%B1%E5%8F%8A%E6%99%82%E6%95%91%E5%91%BD-083734634.html", "date": "2022-12-28", "written_by": "independent", "quote": "運動心電圖檢查後，傳統人工判讀，認為追蹤即可" }, { "kind": "news report", "citation": "張雅淳, 周刊王CTWANT (2022). 男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死 [Man thought it was reflux; AI judged '72% vessel blockage' in time and he escaped sudden death]. Syndicated on Yahoo奇摩新聞, 28 December 2022.", "title": "男子以為胃食道逆流 AI及時判斷「血管堵塞72%」逃過猝死", "url": "https://tw.news.yahoo.com/%E7%94%B7%E5%AD%90%E4%BB%A5%E7%82%BA%E8%83%83%E9%A3%9F%E9%81%93%E9%80%86%E6%B5%81-ai%E5%8F%8A%E6%99%82%E5%88%A4%E6%96%B7-%E8%A1%80%E7%AE%A1%E5%A0%B5%E5%A1%9E72-%E9%80%83%E9%81%8E%E7%8C%9D%E6%AD%BB-083151068.html", "date": "2022-12-28", "written_by": "independent", "quote": "AI報告顯示：陳伯伯的冠狀動脈血管嚴重阻塞機率已高達72%" }, { "kind": "press release", "citation": "China Medical University Hospital, Public Relations Office 公關室 (2022). 75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」及時介入治療倖免於難 [75-year-old wakes with chest tightness and palpitations, coronary artery 99% blocked; exercise-ECG AI-assisted interpretation system intervenes in time]. CMUH news no. 8228, 29 December 2022.", "title": "75歲阿伯起床胸悶心悸，冠動脈阻塞高達99％！「運動心電圖AI輔助判讀系統」 及時介入治療倖免於難", "url": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8228", "date": "2022-12-29", "written_by": "makers", "quote": "結果顯示，陳伯伯左前降支狹窄竟已高達99%" }, { "kind": "peer-reviewed study", "citation": "Liang, H.-Y., Hsu, K.-C., Chien, S.-Y., Yeh, C.-Y., Sun, T.-H., Liu, M.-H., & Ng, K. K. (2025). Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease. Frontiers in Artificial Intelligence, 8, 1496109. PMID 40166362; PMC11955648.", "title": "Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease", "url": "https://doi.org/10.3389/frai.2025.1496109", "date": "2025-03-17", "written_by": "makers", "quote": "We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months." } ] } }, "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": "CMUH's exercise-ECG AI put severe-blockage odds at 72% where manual reading said follow-up; angiography found a 99% LAD stenosis, stented.", "account": "A 75-year-old man surnamed Chen, with controlled hypertension, woke one day with chest tightness and palpitations first taken for reflux. Liberty Times puts the onset in 2021. At China Medical University Hospital (CMUH), Taichung, his exercise electrocardiogram (ECG) was read manually with a risk score. That gave about 50% blockage, moderate risk, follow-up only. The hospital's own Exercise ECG AI-Assisted Interpretation System, in use since May 2022, put the probability of severe (over 70%) narrowing at 72%. So cardiologist 吳宏彬 ordered angiography, found a 99% stenosis of the left anterior descending artery and stented it at once. He went home, chest tightness resolved. The hospital presented it on 28 December 2022. Test and procedure dates 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": "value_present", "value": { "kind": "text", "text": "The model estimated the probability of coronary narrowing from exercise ECGs and clinical features." }, "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 model combined exercise ECG recordings from three time points with 12 measured or derived clinical features." }, "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 analyzed exercise ECG signals and clinical features to assess coronary artery disease." }, "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": "A deep-learning model that processes exercise ECG recordings and clinical features." }, "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": "Not reported. The hospital describes a deep-learning model that takes exercise-ECG signals at three time points plus 10 measured and 2 derived features (such as history and maximum workload), trained on nearly 1,000 exercise ECGs matched to catheterization data, accuracy about 80%, sensitivity 88%, in clinical use since May 2022; the authors' 2025 paper describes a convolutional recurrent network with three ECG slices and 12 features, AUC 0.83, sensitivity 0.89, specificity 0.60." } }, "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": "運動心電圖AI輔助判讀系統 (Exercise ECG AI-Assisted Interpretation System), China Medical University Hospital" } }, "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": "China Medical University Hospital (中國醫藥大學附設醫院人工智慧中心), director 許凱程" } }, "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": "China Medical University Hospital's exercise-ECG AI catching a 99% coronary stenosis that manual reading had rated moderate, Taichung, 2022", "headline": "CMUH's exercise-ECG AI catches a 99% LAD stenosis rated moderate" } }, "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": [ "ada0f6e4bf20d8d0f0d30c3ff523e1b613ca3f8d97c3269834cf564e62df77c8" ], "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 75-year-old man surnamed Chen, with controlled hypertension, woke one day with chest tightness and palpitations first taken for reflux. Liberty Times puts the onset in 2021. At China Medical University Hospital (CMUH), Taichung, his exercise electrocardiogram (ECG) was read manually with a risk score. That gave about 50% blockage, moderate risk, follow-up only. The hospital's own Exercise ECG AI-Assisted Interpretation System, in use since May 2022, put the probability of severe (over 70%) narrowing at 72%. So cardiologist 吳宏彬 ordered angiography, found a 99% stenosis of the left anterior descending artery and stented it at once. He went home, chest tightness resolved. The hospital presented it on 28 December 2022. Test and procedure dates are not reported.", "replication": { "produced_by": [ "A patient with atypical symptoms has an exercise ECG at a hospital that runs the AI model alongside the manual reading", "The model's probability of severe (over 70%) stenosis exceeds what the manual reading and risk score suggest. The cardiologist acts on it by ordering angiography", "Angiography confirms a severe stenosis that can be treated at once by stenting" ], "must_hold": [ "The model is run in addition to, not instead of, the clinician's reading. The hospital calls it assisted interpretation and a clinician made every decision", "The clinician is willing to send a patient to invasive angiography on an AI probability score. With the published model's specificity of 0.60, some such patients will have no severe lesion", "The patient is seen at a hospital that has the system. The sources describe it only at CMUH, in use from May 2022" ], "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": 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-0110", "case_url": "records/AIBD-0110.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The watch's fall-detection classifier read the collision's impact as a hard fall. With the wearer unresponsive, it placed the automatic ambulance call through the paired phone and sent the location texts and calls to his girlfriend. SCDF paramedics treated and conveyed him. The counterfactual is unproven. A passer-by later picked up his phone and spoke to the girlfriend. The family at first assumed that person had called the ambulance, and Fitri was regaining consciousness as paramedics arrived." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0110", "title": "Apple Watch Fall Detection calling an ambulance for a motorcyclist left unconscious by a hit-and-run in Singapore", "summary": "Apple Watch Fall Detection called an ambulance and alerted his girlfriend after a hit-and-run left a Singapore motorcyclist unconscious.", "date_observed": "2021-09-25", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On the evening of 25 September 2021, Muhammad Fitri, 24, was riding home when a gray van struck his motorcycle. That was at the junction of Ang Mo Kio Avenue 6 and Ang Mo Kio Street 31, Singapore. The driver fled; Fitri lost consciousness on a near-empty road. His Apple Watch's Fall Detection registered the impact. Via the paired phone it automatically called an ambulance and texted and phoned his emergency contact, his girlfriend. The Singapore Civil Defence Force said it was alerted around 8:20pm. Fitri was regaining consciousness as paramedics arrived. He was conveyed, conscious, to Khoo Teck Puat Hospital with abrasions, a neck collar and a suspected bleed at the back of the head. Police confirmed a hit-and-run.", "category": "protective response", "unexpected": false, "purpose": "Fall Detection is built to recognize a hard fall. If the wearer stays immobile for about a minute, it calls emergency services and messages emergency contacts with the location. That is what it did. The trigger was a motorcycle collision rather than a fall from standing. Apple's documentation notes only that high-impact activity may register as a fall.", "system": "Apple Watch Fall Detection", "model_version": "Not reported; Fall Detection requires Apple Watch Series 4 or later (Mothership, citing Apple)", "developer": "Apple", "deployer": null, "location": "Ang Mo Kio, Singapore", "domain": "Emergency response / road traffic injury", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2021-09-29-a99a" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "An unconscious hit-and-run victim on a quiet road at night had an ambulance summoned within what Wanbao calls the golden hour. He had abrasions, a neck injury needing a collar and a suspected bleed at the back of the head. His girlfriend was told where he was. The family and the Wanbao headline call it life-saving. No clinician or official says so and a passer-by did reach him, so life-saving is not established." } ], "ai_role": { "status": "assessed", "statement": "The watch's fall-detection classifier read the collision's impact as a hard fall. With the wearer unresponsive, it placed the automatic ambulance call through the paired phone and sent the location texts and calls to his girlfriend. SCDF paramedics treated and conveyed him. The counterfactual is unproven. A passer-by later picked up his phone and spoke to the girlfriend. The family at first assumed that person had called the ambulance, and Fitri was regaining consciousness as paramedics arrived." }, "evidence": [ { "kind": "news report", "citation": "Lianhe Wanbao, Chen Yujia 陈昱嘉 (2021). 准新郎遭撞后逃昏迷 智能表救一命 [Groom-to-be knocked unconscious in hit-and-run; smart watch saves his life]. zaobao.com.sg, 28 September 2021 (print: Lianhe Wanbao, 28 September 2021).", "title": "准新郎遭撞后逃昏迷 智能表救一命", "url": "https://www.zaobao.com.sg/news/singapore/story20210928-1198175", "date": "2021-09-28", "written_by": "independent", "quote": "菲特里的智能手表检测到不寻常的剧烈震动后立刻启动了救援功能，链接手表的手机也自动召救护车到现场" }, { "kind": "news report", "citation": "Zheng, Z. (2021). Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio. Mothership.sg, 29 September 2021.", "title": "Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio", "url": "https://mothership.sg/2021/09/motorcyclist-apple-watch-saves-life-ang-mo-kio/", "date": "2021-09-29", "written_by": "independent", "quote": "the Singapore Civil Defence Force (SCDF) said that they were alerted to the accident at around 8:20pm. Fitri was conveyed to Khoo Teck Puat Hospital." }, { "kind": "press release", "citation": "Apple (2026). Use Fall Detection with Apple Watch. Apple Support article 108896, last modified 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ], "replication": { "produced_by": [ "Fall Detection is enabled on a worn Apple Watch (Series 4 or later) with a connected paired iPhone or cellular service", "A collision throws the wearer down with an impact the classifier reads as a hard fall. The wearer stays immobile for about a minute without dismissing the alert.", "The ambulance service and the emergency contact can act on the automatic call and the shared location" ], "must_hold": [ "The watch and phone keep working and connected after the impact (here the watch screen cracked but still functioned)", "Nobody else present is able to call for help, otherwise the watch's call adds little", "An ambulance can reach the location within the window in which treatment matters" ], "echoes": [] }, "paired_incident": null, "headline": "Apple Watch calls an ambulance for an unconscious motorcyclist", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "No later report on Fitri's recovery or the hit-and-run case was found in the cited sources.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2021-09-29-a99a", "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": "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, 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 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-0110", "catalog_version": "1.0.0", "source_fingerprint": "eda5b9b706d6048044c13a42c8e838a81ea8d558c429266c9ba66817f3c217fd", "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" } ], "source_ids": [ "AIBD-0110-source-0001", "AIBD-0110-source-0002", "AIBD-0110-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" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 watch's fall-detection classifier read the collision's impact as a hard fall. With the wearer unresponsive, it placed the automatic ambulance call through the paired phone and sent the location texts and calls to his girlfriend. SCDF paramedics treated and conveyed him. The counterfactual is unproven. A passer-by later picked up his phone and spoke to the girlfriend. The family at first assumed that person had called the ambulance, and Fitri was regaining consciousness as paramedics arrived." } } }, "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 watch's fall-detection classifier read the collision's impact as a hard fall. With the wearer unresponsive, it placed the automatic ambulance call through the paired phone and sent the location texts and calls to his girlfriend. SCDF paramedics treated and conveyed him. The counterfactual is unproven. A passer-by later picked up his phone and spoke to the girlfriend. The family at first assumed that person had called the ambulance, and Fitri was regaining consciousness as paramedics arrived." } } }, "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": "An unconscious hit-and-run victim on a quiet road at night had an ambulance summoned within what Wanbao calls the golden hour. He had abrasions, a neck injury needing a collar and a suspected bleed at the back of the head. 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}, "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": "2021-09-29-a99a" } } }, "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 Fitri's recovery or the hit-and-run case 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": 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"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 evening of 25 September 2021, Muhammad Fitri, 24, was riding home when a gray van struck his motorcycle. That was at the junction of Ang Mo Kio Avenue 6 and Ang Mo Kio Street 31, Singapore. The driver fled; Fitri lost consciousness on a near-empty road. His Apple Watch's Fall Detection registered the impact. Via the paired phone it automatically called an ambulance and texted and phoned his emergency contact, his girlfriend. The Singapore Civil Defence Force said it was alerted around 8:20pm. Fitri was regaining consciousness as paramedics arrived. He was conveyed, conscious, to Khoo Teck Puat Hospital with abrasions, a neck collar and a suspected bleed at the back of the head. Police confirmed a hit-and-run.", "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": 3, "reason": "An unconscious hit-and-run victim on a quiet road at night had an ambulance summoned within what Wanbao calls the golden hour. He had abrasions, a neck injury needing a collar and a suspected bleed at the back of the head. His girlfriend was told where he was. The family and the Wanbao headline call it life-saving. No clinician or official says so and a passer-by did reach him, so life-saving 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": "Fall Detection is built to recognize a hard fall. If the wearer stays immobile for about a minute, it calls emergency services and messages emergency contacts with the location. That is what it did. The trigger was a motorcycle collision rather than a fall from standing. Apple's documentation notes only that high-impact activity may register as a fall." } }, "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": "Ang Mo Kio, Singapore", "developer_country": "United States" } }, "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": "Fall Detection is built to recognize a hard fall. If the wearer stays immobile for about a minute, it calls emergency services and messages emergency contacts with the location. That is what it did. The trigger was a motorcycle collision rather than a fall from standing. Apple's documentation notes only that high-impact activity may register as a fall." } }, "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": "2021-09-25", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2021-09-29-a99a", "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": "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, 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": [ "Fall Detection is enabled on a worn Apple Watch (Series 4 or later) with a connected paired iPhone or cellular service", "A collision throws the wearer down with an impact the classifier reads as a hard fall. The wearer stays immobile for about a minute without dismissing the alert.", "The ambulance service and the emergency contact can act on the automatic call and the shared location" ], "must_hold": [ "The watch and phone keep working and connected after the impact (here the watch screen cracked but still functioned)", "Nobody else present is able to call for help, otherwise the watch's call adds little", "An ambulance can reach the location within the window in which treatment matters" ], "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/2021-09-29-a99a", "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": "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, 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 response / road traffic injury", "segment": "Consumer wearables" } }, "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": "Lianhe Wanbao, Chen Yujia 陈昱嘉 (2021). 准新郎遭撞后逃昏迷 智能表救一命 [Groom-to-be knocked unconscious in hit-and-run; smart watch saves his life]. zaobao.com.sg, 28 September 2021 (print: Lianhe Wanbao, 28 September 2021).", "title": "准新郎遭撞后逃昏迷 智能表救一命", "url": "https://www.zaobao.com.sg/news/singapore/story20210928-1198175", "date": "2021-09-28", "written_by": "independent", "quote": "菲特里的智能手表检测到不寻常的剧烈震动后立刻启动了救援功能，链接手表的手机也自动召救护车到现场" }, { "kind": "news report", "citation": "Zheng, Z. (2021). Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio. Mothership.sg, 29 September 2021.", "title": "Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio", "url": "https://mothership.sg/2021/09/motorcyclist-apple-watch-saves-life-ang-mo-kio/", "date": "2021-09-29", "written_by": "independent", "quote": "the Singapore Civil Defence Force (SCDF) said that they were alerted to the accident at around 8:20pm. Fitri was conveyed to Khoo Teck Puat Hospital." }, { "kind": "press release", "citation": "Apple (2026). Use Fall Detection with Apple Watch. Apple Support article 108896, last modified 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ] } }, "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-0110-source-0001", "AIBD-0110-source-0002", "AIBD-0110-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": "Lianhe Wanbao, Chen Yujia 陈昱嘉 (2021). 准新郎遭撞后逃昏迷 智能表救一命 [Groom-to-be knocked unconscious in hit-and-run; smart watch saves his life]. zaobao.com.sg, 28 September 2021 (print: Lianhe Wanbao, 28 September 2021).", "title": "准新郎遭撞后逃昏迷 智能表救一命", "url": "https://www.zaobao.com.sg/news/singapore/story20210928-1198175", "date": "2021-09-28", "written_by": "independent", "quote": "菲特里的智能手表检测到不寻常的剧烈震动后立刻启动了救援功能，链接手表的手机也自动召救护车到现场" }, { "kind": "news report", "citation": "Zheng, Z. (2021). Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio. Mothership.sg, 29 September 2021.", "title": "Motorcyclist's Apple watch alerts girlfriend & calls for ambulance after hit-&-run accident at Ang Mo Kio", "url": "https://mothership.sg/2021/09/motorcyclist-apple-watch-saves-life-ang-mo-kio/", "date": "2021-09-29", "written_by": "independent", "quote": "the Singapore Civil Defence Force (SCDF) said that they were alerted to the accident at around 8:20pm. Fitri was conveyed to Khoo Teck Puat Hospital." }, { "kind": "press release", "citation": "Apple (2026). Use Fall Detection with Apple Watch. Apple Support article 108896, last modified 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ] } }, "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": "Apple Watch Fall Detection called an ambulance and alerted his girlfriend after a hit-and-run left a Singapore motorcyclist unconscious.", "account": "On the evening of 25 September 2021, Muhammad Fitri, 24, was riding home when a gray van struck his motorcycle. That was at the junction of Ang Mo Kio Avenue 6 and Ang Mo Kio Street 31, Singapore. The driver fled; Fitri lost consciousness on a near-empty road. His Apple Watch's Fall Detection registered the impact. Via the paired phone it automatically called an ambulance and texted and phoned his emergency contact, his girlfriend. The Singapore Civil Defence Force said it was alerted around 8:20pm. Fitri was regaining consciousness as paramedics arrived. He was conveyed, conscious, to Khoo Teck Puat Hospital with abrasions, a neck collar and a suspected bleed at the back of the head. Police confirmed a hit-and-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": "The watch detected a hard fall and called emergency services when the wearer did not respond." }, "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 reported feature was Apple Watch Fall Detection. The record does not identify the watch generation." }, "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; Fall Detection requires Apple Watch Series 4 or later (Mothership, citing Apple)" } }, "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 Watch Fall 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": "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": "Apple Watch Fall Detection calling an ambulance for a motorcyclist left unconscious by a hit-and-run in Singapore", "headline": "Apple Watch calls an ambulance for an unconscious motorcyclist" } }, "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": [ "eda5b9b706d6048044c13a42c8e838a81ea8d558c429266c9ba66817f3c217fd" ], "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 25 September 2021, Muhammad Fitri, 24, was riding home when a gray van struck his motorcycle. That was at the junction of Ang Mo Kio Avenue 6 and Ang Mo Kio Street 31, Singapore. The driver fled; Fitri lost consciousness on a near-empty road. His Apple Watch's Fall Detection registered the impact. Via the paired phone it automatically called an ambulance and texted and phoned his emergency contact, his girlfriend. The Singapore Civil Defence Force said it was alerted around 8:20pm. Fitri was regaining consciousness as paramedics arrived. He was conveyed, conscious, to Khoo Teck Puat Hospital with abrasions, a neck collar and a suspected bleed at the back of the head. Police confirmed a hit-and-run.", "replication": { "produced_by": [ "Fall Detection is enabled on a worn Apple Watch (Series 4 or later) with a connected paired iPhone or cellular service", "A collision throws the wearer down with an impact the classifier reads as a hard fall. The wearer stays immobile for about a minute without dismissing the alert.", "The ambulance service and the emergency contact can act on the automatic call and the shared location" ], "must_hold": [ "The watch and phone keep working and connected after the impact (here the watch screen cracked but still functioned)", "Nobody else present is able to call for help, otherwise the watch's call adds little", "An ambulance can reach the location within the window in which treatment matters" ], "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": 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-0193", "case_url": "records/AIBD-0193.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "Two AI uses are documented: algorithms that double-check submitted photos (Times) and AI that filters unusable records out of FAO's daily intake (FAO). A Penn State deep-learning soil-moisture model is mentioned by the maker's university. The outcome figures belong to the entire control operation and no source isolates the AI's contribution. FAO is both co-developer and deployer, so its feature is maker-authored. The Times report is independent with named sources (Cressman, Ferrand, Hughes, Jeptoo, Owuor, Craig)." }, { "label": "Comparison", "value": "no locust control operations in East Africa (FAO's estimate of the losses that the control campaign averted)", "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en" }, { "label": "Before", "value": "loss of 4 million tonnes of cereal and 800 million litres of milk production, USD 1.56 billion in cereal and milk losses" }, { "label": "After", "value": "those losses prevented and the food security of 36.6 million people protected" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0193", "title": "eLocust3m and AI data cleaning feed FAO's East Africa locust control, which may have averted USD 1.56 billion in losses", "summary": "PlantVillage's app, built with FAO in under a month in 2020, gathered 240,000+ records; AI removed unusable data; FAO credits the tech-enabled operation with averting USD 1.56 billion in losses.", "date_observed": "2021-05-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In early 2020 desert locust swarms swept the Horn of Africa. FAO's senior locust forecaster Keith Cressman asked Penn State's PlantVillage to adapt its app. PlantVillage had built FAO's fall armyworm app. eLocust3M was released on Google Play on 20 February 2020. Anyone with a smartphone could record georeferenced locust sightings; algorithms checked photos. Penn State also extended a deep-learning soil-moisture model to the affected region. FAO reported receiving up to 2,500 records a day, nearly 25 percent unusable or incorrect. It asked PlantVillage to use artificial intelligence to identify and remove the unwanted data before sharing locations with ground and air control teams. The New York Times reported over 240,000 records collected in a year by PlantVillage scouts, government personnel and citizens. The wider operation also used Garmin satellite devices, EarthRanger (Vulcan) and 28 aircraft coordinated with 51 Degrees. FAO's 6 May 2021 feature says East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. They protected 36.6 million people's food security and avoided USD 1.56 billion in losses, it says. The Times quoted FAO's estimate of USD 1.5 billion averted since February 2020. It quoted FAO's Cyril Ferrand saying swarms were prevented from spilling into the Sahel.", "category": "harm prevented", "unexpected": false, "purpose": "Collect georeferenced locust sightings from anyone with a smartphone. Automatically check photos and remove unusable records so that FAO can direct survey and control teams.", "system": "eLocust3m (PlantVillage) with AI data validation feeding FAO's Desert Locust Information Service", "model_version": "eLocust3M", "developer": "PlantVillage, Pennsylvania State University, with FAO", "deployer": "FAO Desert Locust Information Service and national locust control operations in Kenya, Ethiopia and Somalia", "location": null, "domain": "Agriculture / pest early warning", "segment": "Desert locust control", "benefit_type": "Reduced Risk", "taxonomy_note": "The AI cleaned one data stream in a wider operation, and the averted-loss figure is FAO's estimate for the whole effort.", "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 3, "reason": "FAO states that East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. FAO says they protected 36.6 million people's food security. The Times quotes FAO's USD 1.5 billion averted since February 2020. These are estimates for the whole operation; AI data handling was one part." }, { "group": "organizational", "depth": 2, "reason": "FAO received up to 2,500 records a day with nearly 25 percent unusable. PlantVillage's AI removed the unwanted data. Over 240,000 records were collected in a year from scouts and citizens who could not have used the discontinued eLocust3 tablets." }, { "group": "personal", "depth": null, "reason": "No individually documented farmer outcome in the sources." } ], "ai_role": { "status": "assessed", "statement": "Two AI uses are documented: algorithms that double-check submitted photos (Times) and AI that filters unusable records out of FAO's daily intake (FAO). A Penn State deep-learning soil-moisture model is mentioned by the maker's university. The outcome figures belong to the entire control operation and no source isolates the AI's contribution. FAO is both co-developer and deployer, so its feature is maker-authored. The Times report is independent with named sources (Cressman, Ferrand, Hughes, Jeptoo, Owuor, Craig)." }, "evidence": [ { "kind": "press release", "citation": "FAO. A high-tech response is helping countries win the battle against Desert Locusts. FAO feature story, 6 May 2021.", "title": "A high-tech response is helping countries win the battle against Desert Locusts", "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en", "date": "2021-05-06", "written_by": "makers", "quote": "nearly 25 percent of the data is unusable or incorrect and FAO again turned to PlantVIllage to harness artificial intelligence to rapidly identify and remove the unwanted data." }, { "kind": "news report", "citation": "Nuwer R. As Locusts Swarmed East Africa, This Tech Helped Squash Them. The New York Times, 8 April 2021.", "title": "As Locusts Swarmed East Africa, This Tech Helped Squash Them", "url": "https://www.nytimes.com/2021/04/08/science/locust-swarms-africa.html", "date": "2021-04-08", "written_by": "independent", "quote": "Since February 2020, the F.A.O. estimates that this effort in East Africa has averted the loss of agricultural products with a commercial value of $1.5 billion" }, { "kind": "press release", "citation": "LaJeunesse S. Penn State responds: App aids UN efforts to control Africa's locust infestation. Penn State News, 21 February 2020.", "title": "Penn State responds: App aids UN efforts to control Africa's locust infestation", "url": "https://www.psu.edu/news/research/story/penn-state-responds-app-aids-un-efforts-control-africas-locust-infestation", "date": "2020-02-21", "written_by": "makers", "quote": "a very basic version, eLocust3M, was rapidly developed by Penn State and released for use on mobile phones through Google Play Store on Feb. 20." }, { "kind": "press release", "citation": "FAO. FAO Desert Locust campaign: East Africa upsurge suppressed, Kenya free from Desert Locust. Interview with Cyril Ferrand and Carla Mucavi, FAO newsroom, 15 June 2022.", "title": "FAO Desert Locust campaign: East Africa upsurge suppressed, Kenya free from Desert Locust", "url": "https://www.fao.org/newsroom/detail/fao-desert-locust-campaign-east-africa-upsurge-supressed-kenya-free-from-desert-locust/en", "date": "2022-06-15", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "An upsurge with swarms across Kenya, Ethiopia and Somalia in 2020 and no trained locust trackers in those countries", "An existing FAO-PlantVillage app blueprint (fall armyworm) adaptable in under a month", "Scouts recruited and trained by PlantVillage and governments", "Aerial control assets (28 aircraft) coordinated through EarthRanger with 51 Degrees" ], "must_hold": [ "Smartphone coverage among scouts, with Garmin satellite devices where there is no network", "FAO's Desert Locust Information Service acting on records daily", "Control teams and aircraft available to act on reported locations" ], "echoes": [ { "date": "2021-01-13", "setting": "Kenya, Uganda, Ethiopia, Somalia: Kuzi machine-learning locust forecasting with SMS alerts by Selina Wamucii", "model_version": "Kuzi", "outcome": "unknown", "source": "TRT World, 13 January 2021" }, { "date": "2020-07-20", "setting": "East Africa: icipe MaxEnt prediction of breeding regions", "model_version": "MaxEnt (Kimathi et al.)", "outcome": "unknown", "source": "Kimathi et al. 2020, Scientific Reports, doi:10.1038/s41598-020-68895-2" } ] }, "paired_incident": null, "headline": "Locust control fed by eLocust3m may have averted USD 1.56 billion", "metric": "protected the food security of 36.6 million people and avoided USD 1.56 billion in losses", "comparator": { "against": "no locust control operations in East Africa (FAO's estimate of the losses that the control campaign averted)", "before": "loss of 4 million tonnes of cereal and 800 million litres of milk production, USD 1.56 billion in cereal and milk losses", "after": "those losses prevented and the food security of 36.6 million people protected", "basis": "assumed", "quote": "In East Africa locust control operations have prevented the loss of 4 million tonnes of cereal and 800 million litres of milk production, while protecting the food security of 36.6 million people and avoiding USD 1.56 billion in cereal and milk losses.", "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en" }, "later": { "status": "persisted", "date": "2022-06-15", "note": "In June 2022 FAO declared the East Africa upsurge over and said it had trained 3,800 people across the region to use applications such as eLocust3 to report locusts.", "quote": "We have trained 3 800 people in the entire region on how to use innovative applications such as eLocust3 to recognize and report on Desert Locust.", "url": "https://www.fao.org/newsroom/detail/fao-desert-locust-campaign-east-africa-upsurge-supressed-kenya-free-from-desert-locust/en" }, "value": { "amount": "USD 1.56 billion", "what": "cereal and milk losses avoided by the locust control operations in East Africa that the app and AI data cleaning fed", "who": "East African farmers and herders (the food security of 36.6 million people)", "estimate": true, "quote": "In East Africa locust control operations have prevented the loss of 4 million tonnes of cereal and 800 million litres of milk production, while protecting the food security of 36.6 million people and avoiding USD 1.56 billion in cereal and milk losses.", "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en" }, "developer_country": "United States", "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": "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 model version, the value as stated from the cited sources.", "source": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en; https://www.fao.org/newsroom/detail/fao-desert-locust-campaign-east-africa-upsurge-supressed-kenya-free-from-desert-locust/en; https://www.psu.edu/news/research/story/penn-state-responds-app-aids-un-efforts-control-africas-locust-infestation; https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en", "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/Pennsylvania_State_University; https://www.fao.org/contact-us/en", "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": "2026-10-25", "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: one independent news report 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-0193", "catalog_version": "1.0.0", "source_fingerprint": "7a5092b62a3325fadb137449f080787adc9da3bf2ff54dec74ee80646e5f1f07", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0193-source-0001", "AIBD-0193-source-0002", "AIBD-0193-source-0003", "AIBD-0193-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": [ "AIBD-0193-legacy-value", "DIV-0006" ], "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-0193-legacy-value", "DIV-0006" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "PlantVillage, Pennsylvania State University, with FAO", "deployer": "FAO Desert Locust Information Service and national locust control operations in Kenya, Ethiopia and Somalia" } }, "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": "Two AI uses are documented: algorithms that double-check submitted photos (Times) and AI that filters unusable records out of FAO's daily intake (FAO). A Penn State deep-learning soil-moisture model is mentioned by the maker's university. The outcome figures belong to the entire control operation and no source isolates the AI's contribution. FAO is both co-developer and deployer, so its feature is maker-authored. The Times report is independent with named sources (Cressman, Ferrand, Hughes, Jeptoo, Owuor, Craig)." } } }, "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": "Two AI uses are documented: algorithms that double-check submitted photos (Times) and AI that filters unusable records out of FAO's daily intake (FAO). A Penn State deep-learning soil-moisture model is mentioned by the maker's university. The outcome figures belong to the entire control operation and no source isolates the AI's contribution. FAO is both co-developer and deployer, so its feature is maker-authored. The Times report is independent with named sources (Cressman, Ferrand, Hughes, Jeptoo, Owuor, Craig)." } } }, "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": "FAO states that East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. FAO says they protected 36.6 million people's food security. The Times quotes FAO's USD 1.5 billion averted since February 2020. These are estimates for the whole operation; AI data handling was one part." }, { "group": "organizational", "depth": 2, "reason": "FAO received up to 2,500 records a day with nearly 25 percent unusable. PlantVillage's AI removed the unwanted data. Over 240,000 records were collected in a year from scouts and citizens who could not have used the discontinued eLocust3 tablets." }, { "group": "personal", "reason": "No individually documented farmer 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[].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 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": "societal", "depth": 3, "reason": "FAO states that East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. FAO says they protected 36.6 million people's food security. The Times quotes FAO's USD 1.5 billion averted since February 2020. These are estimates for the whole operation; AI data handling was one part." }, { "group": "organizational", "depth": 2, "reason": "FAO received up to 2,500 records a day with nearly 25 percent unusable. PlantVillage's AI removed the unwanted data. Over 240,000 records were collected in a year from scouts and citizens who could not have used the discontinued eLocust3 tablets." }, { "group": "personal", "reason": "No individually documented farmer 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", "display": { "answer": "FAO states that the campaign prevented the loss of 4 million metric tons of cereal and 800 million liters of milk. PlantVillage helped FAO process up to 2,500 reports a day, about a quarter of which were initially unusable. The campaign estimates cover many interventions, so they cannot be assigned to AI alone." } }, "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": "FAO states that East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. FAO says they protected 36.6 million people's food security. The Times quotes FAO's USD 1.5 billion averted since February 2020. These are estimates for the whole operation; AI data handling was one part." }, { "group": "organizational", "depth": 2, "reason": "FAO received up to 2,500 records a day with nearly 25 percent unusable. PlantVillage's AI removed the unwanted data. 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The number attributable to AI is not established." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "The AI cleaned one data stream in a wider operation, and the averted-loss figure is FAO's estimate for the whole effort." } }, "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 early 2020 desert locust swarms swept the Horn of Africa. FAO's senior locust forecaster Keith Cressman asked Penn State's PlantVillage to adapt its app. PlantVillage had built FAO's fall armyworm app. eLocust3M was released on Google Play on 20 February 2020. Anyone with a smartphone could record georeferenced locust sightings; algorithms checked photos. Penn State also extended a deep-learning soil-moisture model to the affected region. FAO reported receiving up to 2,500 records a day, nearly 25 percent unusable or incorrect. It asked PlantVillage to use artificial intelligence to identify and remove the unwanted data before sharing locations with ground and air control teams. The New York Times reported over 240,000 records collected in a year by PlantVillage scouts, government personnel and citizens. The wider operation also used Garmin satellite devices, EarthRanger (Vulcan) and 28 aircraft coordinated with 51 Degrees. FAO's 6 May 2021 feature says East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. They protected 36.6 million people's food security and avoided USD 1.56 billion in losses, it says. The Times quoted FAO's estimate of USD 1.5 billion averted since February 2020. It quoted FAO's Cyril Ferrand saying swarms were prevented from spilling into the Sahel.", "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": "FAO estimates that the East African control campaign protected food supplies for 36.6 million people. AI helped process locust reports within that wider operation." } }, "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": "FAO states that East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. FAO says they protected 36.6 million people's food security. The Times quotes FAO's USD 1.5 billion averted since February 2020. 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FAO staff must review reports daily, and control teams and aircraft must be available to act on them." } }, "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 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, the value as stated from the cited sources.", "source": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en; https://www.fao.org/newsroom/detail/fao-desert-locust-campaign-east-africa-upsurge-supressed-kenya-free-from-desert-locust/en; https://www.psu.edu/news/research/story/penn-state-responds-app-aids-un-efforts-control-africas-locust-infestation; https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en", "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/Pennsylvania_State_University; https://www.fao.org/contact-us/en", "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": "2026-10-25", "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": "Agriculture / pest early warning", "segment": "Desert locust control" } }, "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": "FAO. 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Interview with Cyril Ferrand and Carla Mucavi, FAO newsroom, 15 June 2022.", "title": "FAO Desert Locust campaign: East Africa upsurge suppressed, Kenya free from Desert Locust", "url": "https://www.fao.org/newsroom/detail/fao-desert-locust-campaign-east-africa-upsurge-supressed-kenya-free-from-desert-locust/en", "date": "2022-06-15", "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-0193-source-0001", "AIBD-0193-source-0002", "AIBD-0193-source-0003", "AIBD-0193-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": "FAO. 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Penn State News, 21 February 2020.", "title": "Penn State responds: App aids UN efforts to control Africa's locust infestation", "url": "https://www.psu.edu/news/research/story/penn-state-responds-app-aids-un-efforts-control-africas-locust-infestation", "date": "2020-02-21", "written_by": "makers", "quote": "a very basic version, eLocust3M, was rapidly developed by Penn State and released for use on mobile phones through Google Play Store on Feb. 20." }, { "kind": "press release", "citation": "FAO. FAO Desert Locust campaign: East Africa upsurge suppressed, Kenya free from Desert Locust. 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FAO's senior locust forecaster Keith Cressman asked Penn State's PlantVillage to adapt its app. PlantVillage had built FAO's fall armyworm app. eLocust3M was released on Google Play on 20 February 2020. Anyone with a smartphone could record georeferenced locust sightings; algorithms checked photos. Penn State also extended a deep-learning soil-moisture model to the affected region. FAO reported receiving up to 2,500 records a day, nearly 25 percent unusable or incorrect. It asked PlantVillage to use artificial intelligence to identify and remove the unwanted data before sharing locations with ground and air control teams. The New York Times reported over 240,000 records collected in a year by PlantVillage scouts, government personnel and citizens. The wider operation also used Garmin satellite devices, EarthRanger (Vulcan) and 28 aircraft coordinated with 51 Degrees. FAO's 6 May 2021 feature says East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. They protected 36.6 million people's food security and avoided USD 1.56 billion in losses, it says. The Times quoted FAO's estimate of USD 1.5 billion averted since February 2020. It quoted FAO's Cyril Ferrand saying swarms were prevented from spilling into the Sahel." } }, "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 checked submitted photographs and filtered unusable reports before FAO analysis." }, "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 eLocust3M." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. 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FAO's senior locust forecaster Keith Cressman asked Penn State's PlantVillage to adapt its app. PlantVillage had built FAO's fall armyworm app. eLocust3M was released on Google Play on 20 February 2020. Anyone with a smartphone could record georeferenced locust sightings; algorithms checked photos. Penn State also extended a deep-learning soil-moisture model to the affected region. FAO reported receiving up to 2,500 records a day, nearly 25 percent unusable or incorrect. It asked PlantVillage to use artificial intelligence to identify and remove the unwanted data before sharing locations with ground and air control teams. The New York Times reported over 240,000 records collected in a year by PlantVillage scouts, government personnel and citizens. The wider operation also used Garmin satellite devices, EarthRanger (Vulcan) and 28 aircraft coordinated with 51 Degrees. FAO's 6 May 2021 feature says East African control operations prevented the loss of 4 million tonnes of cereal and 800 million liters of milk. They protected 36.6 million people's food security and avoided USD 1.56 billion in losses, it says. The Times quoted FAO's estimate of USD 1.5 billion averted since February 2020. It quoted FAO's Cyril Ferrand saying swarms were prevented from spilling into the Sahel.", "replication": { "produced_by": [ "An upsurge with swarms across Kenya, Ethiopia and Somalia in 2020 and no trained locust trackers in those countries", "An existing FAO-PlantVillage app blueprint (fall armyworm) adaptable in under a month", "Scouts recruited and trained by PlantVillage and governments", "Aerial control assets (28 aircraft) coordinated through EarthRanger with 51 Degrees" ], "must_hold": [ "Smartphone coverage among scouts, with Garmin satellite devices where there is no network", "FAO's Desert Locust Information Service acting on records daily", "Control teams and aircraft available to act on reported locations" ], "echoes": [ { "date": "2021-01-13", "setting": "Kenya, Uganda, Ethiopia, Somalia: Kuzi machine-learning locust forecasting with SMS alerts by Selina Wamucii", "model_version": "Kuzi", "outcome": "unknown", "source": "TRT World, 13 January 2021" }, { "date": "2020-07-20", "setting": "East Africa: icipe MaxEnt prediction of breeding regions", "model_version": "MaxEnt (Kimathi et al.)", "outcome": "unknown", "source": "Kimathi et al. 2020, Scientific Reports, doi:10.1038/s41598-020-68895-2" } ] } } }, "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": "Scouts and residents sent locust sightings through eLocust3m. PlantVillage’s software removed unusable data. FAO used the reports to direct control teams and aircraft." } } }, "summary": { "total": 91, "addressed": 91, "known": 42, "reviewed": 0, "record_mapped": 42, "not_assessed": 49, "complete": false, "by_state": { "value_present": 42, "not_reported": 0, "unresolved": 0, "not_assessed": 49, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0108", "case_url": "records/AIBD-0108.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "By Laurence Sanderson's account his car was on Autopilot and the emergency braking stopped it so the trunk hit the bonnet rather than the cabin. Josh Whitelock said he was not sure whether his loan car's automatic brakes were enabled and credited its other safety features. No Tesla data, police finding or engineering assessment of whether either system braked, or whether the drivers would have stopped in time themselves, is reported. Elon Musk's reply, “Glad you're all ok”, acknowledges the outcome, not the mechanism." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0108", "title": "Two Tesla Model X cars stopped as a 400-year-old oak fell across the A31 at Sturminster Marshall during Storm Dennis; eight occupants walked away, and automatic emergency braking may have saved them", "summary": "Two Model X cars stopped as an oak fell on the A31 in Storm Dennis; eight occupants walked away, both drivers credit the emergency brakes.", "date_observed": "2020-02-15", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On the evening of Saturday 15 February 2020, during Storm Dennis, a 400-year-old oak fell across the A31 at Sturminster Marshall, Dorset. It hit two Tesla Model X cars going in opposite directions. Laurence Sanderson of Brentwood, Essex, was driving wife Anna and three children to a half-term holiday. He told the Mirror the car was on Autopilot, the emergency braking kicked in and the trunk landed on the bonnet. The other car, a Tesla loan vehicle driven by Josh Whitelock of Wimborne with Kitty McConnell and her mother Julia, also stopped suddenly. Whitelock told the Bournemouth Echo he was not sure its automatic brakes were enabled. Anna Sanderson had two black eyes and bruising. Nobody was seriously hurt.", "category": "protective response", "unexpected": false, "purpose": "Tesla's Autopilot hardware and software provide driver assistance and active safety features including Automatic Emergency Braking. It detects obstacles the car may hit and applies the brakes. Stopping short of a falling tree is that intended use as the drivers describe it.", "system": "Tesla Autopilot driver-assistance suite with Automatic Emergency Braking, on two Model X cars", "model_version": "Not reported; both cars were Tesla Model X, one a Tesla loan car, and no Autopilot hardware or software version is given", "developer": "Tesla", "deployer": null, "location": "Sturminster Marshall, United Kingdom", "domain": "Road vehicle crash avoidance", "segment": "Automotive", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2020-02-18-956e" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A 400-year-old oak struck two cars at the bonnet. Eight people walked away with, at worst, two black eyes, a bump on the head and bruising. Both drivers and the Mirror call it life-saving. But that rests on the drivers' accounts, no telemetry or official assessment is reported, and the second driver was unsure the brakes were enabled." } ], "ai_role": { "status": "unassessed", "statement": "By Laurence Sanderson's account his car was on Autopilot and the emergency braking stopped it so the trunk hit the bonnet rather than the cabin. Josh Whitelock said he was not sure whether his loan car's automatic brakes were enabled and credited its other safety features. No Tesla data, police finding or engineering assessment of whether either system braked, or whether the drivers would have stopped in time themselves, is reported. Elon Musk's reply, “Glad you're all ok”, acknowledges the outcome, not the mechanism." }, "evidence": [ { "kind": "news report", "citation": "Rudd A (2020). Tesla Autopilot saves family's lives after tree crushes car in Storm Dennis. Daily Mirror, 17 February 2020 (exclusive).", "title": "Tesla Autopilot saves family's lives after tree crushes car in Storm Dennis", "url": "https://www.mirror.co.uk/news/uk-news/tesla-autopilot-saves-familys-life-21512255", "date": "2020-02-17", "written_by": "independent", "quote": "We were driving along and out of the corner of my eye I saw the tree coming down. The car was on Autopilot and the emergency braking kicked in." }, { "kind": "news report", "citation": "Bournemouth Echo (2020). 'Miracle' as falling tree narrowly misses two Teslas - because the cars have automatic emergency brakes. Bournemouth Echo, 18 February 2020.", "title": "'Miracle' as falling tree misses Tesla cars because of brakes", "url": "https://www.bournemouthecho.co.uk/news/18240206.miracle-falling-tree-narrowly-misses-killing-families-two-tesla-model-xs---cars-automatic-emergency-brakes/", "date": "2020-02-18", "written_by": "independent", "quote": "the incident on Saturday evening, which happened on the A31 near Vines Farm Shop east of the Roundhouse roundabout at Sturminster Marshall" }, { "kind": "news report", "citation": "Bournemouth Echo (2020). 'My life flashed before my eyes': Driver of second Tesla crushed by falling tree describes lucky escape. Bournemouth Echo, 19 February 2020.", "title": "Tesla driver's lucky escape after falling tree hits cars during Storm Dennis", "url": "https://www.bournemouthecho.co.uk/news/18245292.tesla-drivers-lucky-escape-falling-tree-hits-cars-storm-dennis/", "date": "2020-02-19", "written_by": "independent", "quote": "said he isn't sure whether the automatic brakes were enabled at the time, but said the car's other safety features enabled him and his two passengers to walk away unscathed" }, { "kind": "news report", "citation": "Gratton J (2020). Families escape after Tesla Model X's automatic brakes stopped a falling tree from crushing them during Storm Dennis. The Sun, 17 February 2020.", "title": "Families escape after Tesla Model X's automatic brakes stopped a falling tree from crushing them during Storm Dennis", "url": "https://www.thesun.co.uk/motors/10979882/families-miracle-escape-teslas-automatic-brakes-stopped-tree/", "date": "2020-02-17", "written_by": "independent", "quote": "The A31 was blocked in both directions due to the massive tree fall." }, { "kind": "other", "citation": "Sanderson L (@sterlingandlaw) (2020). Tweet to @elonmusk and @Tesla with two photographs of the crushed car, 15 February 2020, 23:14 UTC; text retrieved through the fxtwitter API.", "title": "Tweet by Laurence Sanderson, 15 February 2020", "url": "https://twitter.com/sterlingandlaw/status/1228819929832525826", "date": "2020-02-15", "written_by": "beneficiary", "quote": "The tree below fell on our car today. Autopilot kicked in and saved us. The car on the other side is a model x as well. 9 lives saved." }, { "kind": "news report", "citation": "Rudd A (2020). Elon Musk 'glad everyone is ok' after Tesla Autopilot saves 8 lives in Storm Dennis. Daily Mirror, 19 February 2020.", "title": "Elon Musk 'glad everyone is ok' after Tesla Autopilot saves 8 lives in Storm Dennis", "url": "https://www.mirror.co.uk/news/uk-news/elon-musk-glad-everyone-ok-21527013", "date": "2020-02-19", "written_by": "independent", "quote": "Elon replied with a heart emoji and said: ''Glad you're all ok.''" }, { "kind": "news report", "citation": "La Vanguardia (2020). El Autopilot de Tesla salva la vida a 8 personas de la tormenta Dennis. La Vanguardia, 19 February 2020, citing the Mirror; one of the four Spanish-language sources cited by the OECD Monitor.", "title": "El Autopilot de Tesla salva la vida a 8 personas de la tormenta Dennis", "url": "https://www.lavanguardia.com/motor/actualidad/20200219/473657698342/el-autopilot-de-tesla-salva-la-vida-a-8-personas-tormenta-dennis-reino-unido-arbol-elon-musk.html", "date": "2020-02-19", "written_by": "independent", "quote": "Los dos vehículos, que circulaban con el piloto automático, activaron los frenos de emergencia justo en el momento en que un roble de 400 años caía" }, { "kind": "news report", "citation": "RAC (2020). Tesla Model X's automatic brakes just saved two families from a falling tree during Storm Dennis. RAC Drive news, undated on the page; Google News lists 19 February 2020.", "title": "Tesla Model X's automatic brakes just saved two families from a falling tree during Storm Dennis", "url": "https://www.rac.co.uk/drive/news/autonomous-vehicles-news/tesla-model-xs-automatic-brakes-just-saved-two-families-from-a-falling-tree/", "date": null, "written_by": "independent", "quote": "an oak tree came crashing down onto their bonnet rather than their roof, thanks to the smart safety feature" } ], "replication": { "produced_by": [ "Automatic Emergency Braking active on a Tesla Model X whose cameras and radar register an obstacle entering its path", "A hazard that arrives in front of the car, here a tree falling across the carriageway, rather than onto the roof from above", "A stop short of the point where the trunk lands, leaving the bonnet rather than the cabin to take the impact" ], "must_hold": [ "The system has time to detect and brake, which Sanderson put at about a second", "The vehicle's structure withstands the part of the impact that still lands. Whitelock credited the car's other safety features", "Occupants are belted. The Whitelock passengers were sore from the seatbelts" ], "echoes": [] }, "paired_incident": null, "headline": "Two Tesla Model X cars stop as an oak falls; eight walk away", "metric": "eight occupants walked away", "comparator": null, "later": { "status": "unknown", "date": null, "note": "Later coverage on 19 February 2020 only added Elon Musk's reply that he was glad everyone was OK; both cars were written off and no further development was reported.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2020-02-18-956e", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Tesla,_Inc.", "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: seven independent news reports and the beneficiary's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0108", "catalog_version": "1.0.0", "source_fingerprint": "711695c7ca3fbdc1da9ec1e4e39bb39f6e8dcfa022a9f5500d8c002d80cf18d3", "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" } ], "source_ids": [ "AIBD-0108-source-0001", "AIBD-0108-source-0002", "AIBD-0108-source-0003", "AIBD-0108-source-0004", "AIBD-0108-source-0005", "AIBD-0108-source-0006", "AIBD-0108-source-0007", "AIBD-0108-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": "Tesla" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 Laurence Sanderson's account his car was on Autopilot and the emergency braking stopped it so the trunk hit the bonnet rather than the cabin. Josh Whitelock said he was not sure whether his loan car's automatic brakes were enabled and credited its other safety features. No Tesla data, police finding or engineering assessment of whether either system braked, or whether the drivers would have stopped in time themselves, is reported. Elon Musk's reply, “Glad you're all ok”, acknowledges the outcome, not the mechanism." } } }, "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 400-year-old oak struck two cars at the bonnet. Eight people walked away with, at worst, two black eyes, a bump on the head and bruising. Both drivers and the Mirror call it life-saving. But that rests on the drivers' accounts, no telemetry or official assessment is reported, and the second driver was unsure the brakes were enabled." } ] } }, "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: seven independent news reports and the beneficiary's other. 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But that rests on the drivers' accounts, no telemetry or official assessment is reported, and the second driver was unsure the brakes were enabled." } ] } }, "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": 3, "reason": "A 400-year-old oak struck two cars at the bonnet. Eight people walked away with, at worst, two black eyes, a bump on the head and bruising. Both drivers and the Mirror call it life-saving. 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"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": "eight occupants 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It hit two Tesla Model X cars going in opposite directions. Laurence Sanderson of Brentwood, Essex, was driving wife Anna and three children to a half-term holiday. He told the Mirror the car was on Autopilot, the emergency braking kicked in and the trunk landed on the bonnet. The other car, a Tesla loan vehicle driven by Josh Whitelock of Wimborne with Kitty McConnell and her mother Julia, also stopped suddenly. Whitelock told the Bournemouth Echo he was not sure its automatic brakes were enabled. Anna Sanderson had two black eyes and bruising. 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The car on the other side is a model x as well. 9 lives saved." }, { "kind": "news report", "citation": "Rudd A (2020). Elon Musk 'glad everyone is ok' after Tesla Autopilot saves 8 lives in Storm Dennis. Daily Mirror, 19 February 2020.", "title": "Elon Musk 'glad everyone is ok' after Tesla Autopilot saves 8 lives in Storm Dennis", "url": "https://www.mirror.co.uk/news/uk-news/elon-musk-glad-everyone-ok-21527013", "date": "2020-02-19", "written_by": "independent", "quote": "Elon replied with a heart emoji and said: ''Glad you're all ok.''" }, { "kind": "news report", "citation": "La Vanguardia (2020). El Autopilot de Tesla salva la vida a 8 personas de la tormenta Dennis. 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It hit two Tesla Model X cars going in opposite directions. Laurence Sanderson of Brentwood, Essex, was driving wife Anna and three children to a half-term holiday. He told the Mirror the car was on Autopilot, the emergency braking kicked in and the trunk landed on the bonnet. The other car, a Tesla loan vehicle driven by Josh Whitelock of Wimborne with Kitty McConnell and her mother Julia, also stopped suddenly. Whitelock told the Bournemouth Echo he was not sure its automatic brakes were enabled. Anna Sanderson had two black eyes and bruising. Nobody was seriously hurt." } }, "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 driver reported that the system applied emergency braking." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. 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It hit two Tesla Model X cars going in opposite directions. Laurence Sanderson of Brentwood, Essex, was driving wife Anna and three children to a half-term holiday. He told the Mirror the car was on Autopilot, the emergency braking kicked in and the trunk landed on the bonnet. The other car, a Tesla loan vehicle driven by Josh Whitelock of Wimborne with Kitty McConnell and her mother Julia, also stopped suddenly. Whitelock told the Bournemouth Echo he was not sure its automatic brakes were enabled. Anna Sanderson had two black eyes and bruising. Nobody was seriously hurt.", "replication": { "produced_by": [ "Automatic Emergency Braking active on a Tesla Model X whose cameras and radar register an obstacle entering its path", "A hazard that arrives in front of the car, here a tree falling across the carriageway, rather than onto the roof from above", "A stop short of the point where the trunk lands, leaving the bonnet rather than the cabin to take the impact" ], "must_hold": [ "The system has time to detect and brake, which Sanderson put at about a second", "The vehicle's structure withstands the part of the impact that still lands. Whitelock credited the car's other safety features", "Occupants are belted. The Whitelock passengers were sore from the seatbelts" ], "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-0107", "case_url": "records/AIBD-0107.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The app's classifier graded a phone photograph of the mole high risk and told him to see a doctor at once. By his account that prompt, not the color change, is what sent him to his GP. He said he only goes to the doctor when absolutely desperate. The referral, excision, biopsy and staging were done by the GP, the dermatologist and pathology, none of whom the Daily Mail quotes. The article does not report the app's score, its version, or whether the 48-hour dermatologist advice it mentions was used." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0107", "title": "SkinVision app rated a changing mole on Jason Sheridan's arm high risk; a dermatologist found stage 1A melanoma and it was removed", "summary": "SkinVision rated a 31-year-old's changing arm mole high risk; stage 1A melanoma was found and removed, and he is cancer-free.", "date_observed": "2020-01-17", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Jason Sheridan, a 31-year-old office worker from Liverpool living in Vauxhall, South London, noticed a new 1cm mole on his left arm in March 2018. He did not see a doctor. Twelve months later it turned pink and he checked it with SkinVision, an AI app that grades photographed moles low, medium or high risk. It returned a high-risk rating and urged him to see a doctor. His GP referred him to a dermatologist and two weeks later the mole was removed in a 40-minute operation. The biopsy showed stage 1A melanoma, a further biopsy found no spread, and he is cancer-free. The Daily Mail reported the case on 17 January 2020 without dates for the app check or surgery.", "category": "harm prevented", "unexpected": false, "purpose": "SkinVision is built to grade a phone photograph of a mole low, medium or high risk. It tells users with a high-risk result to see a doctor, with dermatologist advice sent within 48 hours. This benefit is that intended use.", "system": "SkinVision skin-check app (AI analysis of smartphone photographs of moles, graded low, medium or high risk)", "model_version": null, "developer": "SkinVision", "deployer": null, "location": "London, United Kingdom", "domain": "Healthcare - dermatology / melanoma detection", "segment": "Consumer mobile health app", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2020-01-18-8e93" }, "ratings": [ { "group": "personal", "depth": 3, "reason": "A biopsy-confirmed stage 1A melanoma was excised, further tests found no spread and he is reported cancer-free. He calls it life-saving, but stage 1A is early disease and no clinician is quoted on prognosis. How long he would otherwise have waited is his own estimate." } ], "ai_role": { "status": "assessed", "statement": "The app's classifier graded a phone photograph of the mole high risk and told him to see a doctor at once. By his account that prompt, not the color change, is what sent him to his GP. He said he only goes to the doctor when absolutely desperate. The referral, excision, biopsy and staging were done by the GP, the dermatologist and pathology, none of whom the Daily Mail quotes. The article does not report the app's score, its version, or whether the 48-hour dermatologist advice it mentions was used." }, "evidence": [ { "kind": "news report", "citation": "Howard H (2020). Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm. Daily Mail, 17 January 2020.", "title": "Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm", "url": "https://www.dailymail.co.uk/health/article-7898507/Man-mole-removed-finding-skin-cancer-app-told-visit-doctor.html", "date": "2020-01-17", "written_by": "independent", "quote": "After downloading SkinVision, the app immediately came back with a high-risk rating, and I was urged to go see a doctor as soon as possible." }, { "kind": "news report", "citation": "Al-Dostour (2020). سكان فيزيون. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة [Skin Vision: technology detects cancer in an innovative way]. dostor.org, 18 January 2020, crediting the Daily Mail; one of the five Arabic-language sources cited by the OECD Monitor.", "title": "سكان فيزيون.. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة", "url": "https://www.dostor.org/2974509", "date": "2020-01-18", "written_by": "independent", "quote": "وأفادت صحيفة \"ديلي ميل\" البريطانية، فإن الشاب جيسون شيريدان، (31 عاما) قد لاحظ وجود الشامة على ذراعه الأيسر لأول مرة في عام 2018" }, { "kind": "press release", "citation": "SkinVision (2025). SkinVision | Skin Cancer Detection App. Company website, fetched 20 October 2026; the page served to US visitors gives no legal entity or address.", "title": "SkinVision | Skin Cancer Detection App", "url": "https://www.skinvision.com/", "date": null, "written_by": "makers", "quote": "SkinVision was founded with a mission to improve access to skin health information and support individuals in becoming more aware of their skin over time." } ], "replication": { "produced_by": [ "A smartphone photograph of a changing mole submitted to SkinVision's risk classifier", "A high-risk rating with an instruction to see a doctor, acted on by the user", "A GP referral and an excision with pathology that confirms and stages the lesion" ], "must_hold": [ "The lesion is one the classifier can score from a phone photograph. The article reports no score or app version", "The user follows the app's advice rather than treating the rating as noise", "Clinical follow-up is available quickly enough for early-stage removal. Here the operation came two weeks after referral" ], "echoes": [] }, "paired_incident": null, "headline": "SkinVision flags a mole as high risk; stage 1A melanoma removed", "metric": "stage 1A melanoma was found and removed, and he is cancer-free", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No later report on this case was found in the cited sources; SkinVision's site now says the app is not yet available in the United States pending regulatory clearance.", "quote": null, "url": null }, "value": null, "developer_country": "Netherlands", "deployer_country": null, "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/2020-01-18-8e93", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:31494983&resultType=core&format=json", "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 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-0107", "catalog_version": "1.0.0", "source_fingerprint": "695ed3c0cef61a50eb97a4a79d29d0f1f61c2fb57087826e4ed8213ec7b02e69", "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" } ], "source_ids": [ "AIBD-0107-source-0001", "AIBD-0107-source-0002", "AIBD-0107-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": "SkinVision" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 app's classifier graded a phone photograph of the mole high risk and told him to see a doctor at once. By his account that prompt, not the color change, is what sent him to his GP. He said he only goes to the doctor when absolutely desperate. The referral, excision, biopsy and staging were done by the GP, the dermatologist and pathology, none of whom the Daily Mail quotes. The article does not report the app's score, its version, or whether the 48-hour dermatologist advice it mentions was used." } } }, "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 app's classifier graded a phone photograph of the mole high risk and told him to see a doctor at once. By his account that prompt, not the color change, is what sent him to his GP. He said he only goes to the doctor when absolutely desperate. The referral, excision, biopsy and staging were done by the GP, the dermatologist and pathology, none of whom the Daily Mail quotes. The article does not report the app's score, its version, or whether the 48-hour dermatologist advice it mentions was used." } } }, "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 biopsy-confirmed stage 1A melanoma was excised, further tests found no spread and he is reported cancer-free. He calls it life-saving, but stage 1A is early disease and no clinician is quoted on prognosis. How long he would otherwise have waited is his own estimate." } ] } }, "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 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": 3, "reason": "A biopsy-confirmed stage 1A melanoma was excised, further tests found no spread and he is reported cancer-free. He calls it life-saving, but stage 1A is early disease and no clinician is quoted on prognosis. How long he would otherwise have waited is his own estimate." } ] } }, "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": 3, "reason": "A biopsy-confirmed stage 1A melanoma was excised, further tests found no spread and he is reported cancer-free. He calls it life-saving, but stage 1A is early disease and no clinician is quoted on prognosis. How long he would otherwise have waited is his own estimate." } ] } }, "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": "stage 1A melanoma was found and removed, and he is cancer-free", "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": "2020-01-18-8e93" } } }, "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 this case was found in the cited sources; SkinVision's site now says the app is not yet available in the United States pending regulatory clearance." } } }, "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-0107" } }, "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" } }, "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": "Jason Sheridan, a 31-year-old office worker from Liverpool living in Vauxhall, South London, noticed a new 1cm mole on his left arm in March 2018. He did not see a doctor. Twelve months later it turned pink and he checked it with SkinVision, an AI app that grades photographed moles low, medium or high risk. It returned a high-risk rating and urged him to see a doctor. His GP referred him to a dermatologist and two weeks later the mole was removed in a 40-minute operation. The biopsy showed stage 1A melanoma, a further biopsy found no spread, and he is cancer-free. The Daily Mail reported the case on 17 January 2020 without dates for the app check or surgery.", "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": 3, "reason": "A biopsy-confirmed stage 1A melanoma was excised, further tests found no spread and he is reported cancer-free. He calls it life-saving, but stage 1A is early disease and no clinician is quoted on prognosis. How long he would otherwise have waited is his own estimate." } ] } }, "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": "SkinVision is built to grade a phone photograph of a mole low, medium or high risk. It tells users with a high-risk result to see a doctor, with dermatologist advice sent within 48 hours. This benefit 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": "London, United Kingdom", "developer_country": "Netherlands" } }, "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": "SkinVision is built to grade a phone photograph of a mole low, medium or high risk. It tells users with a high-risk result to see a doctor, with dermatologist advice sent within 48 hours. This benefit 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": "2020-01-17", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2020-01-18-8e93", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:31494983&resultType=core&format=json", "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 smartphone photograph of a changing mole submitted to SkinVision's risk classifier", "A high-risk rating with an instruction to see a doctor, acted on by the user", "A GP referral and an excision with pathology that confirms and stages the lesion" ], "must_hold": [ "The lesion is one the classifier can score from a phone photograph. The article reports no score or app version", "The user follows the app's advice rather than treating the rating as noise", "Clinical follow-up is available quickly enough for early-stage removal. Here the operation came two weeks after referral" ], "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/2020-01-18-8e93", "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": "Country of the developer added from the organization's own site or a registry.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:31494983&resultType=core&format=json", "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": "Healthcare - dermatology / melanoma detection", "segment": "Consumer mobile health app" } }, "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": "Howard H (2020). Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm. Daily Mail, 17 January 2020.", "title": "Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm", "url": "https://www.dailymail.co.uk/health/article-7898507/Man-mole-removed-finding-skin-cancer-app-told-visit-doctor.html", "date": "2020-01-17", "written_by": "independent", "quote": "After downloading SkinVision, the app immediately came back with a high-risk rating, and I was urged to go see a doctor as soon as possible." }, { "kind": "news report", "citation": "Al-Dostour (2020). سكان فيزيون. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة [Skin Vision: technology detects cancer in an innovative way]. dostor.org, 18 January 2020, crediting the Daily Mail; one of the five Arabic-language sources cited by the OECD Monitor.", "title": "سكان فيزيون.. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة", "url": "https://www.dostor.org/2974509", "date": "2020-01-18", "written_by": "independent", "quote": "وأفادت صحيفة \"ديلي ميل\" البريطانية، فإن الشاب جيسون شيريدان، (31 عاما) قد لاحظ وجود الشامة على ذراعه الأيسر لأول مرة في عام 2018" }, { "kind": "press release", "citation": "SkinVision (2025). SkinVision | Skin Cancer Detection App. Company website, fetched 20 October 2026; the page served to US visitors gives no legal entity or address.", "title": "SkinVision | Skin Cancer Detection App", "url": "https://www.skinvision.com/", "written_by": "makers", "quote": "SkinVision was founded with a mission to improve access to skin health information and support individuals in becoming more aware of their skin over time." } ] } }, "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-0107-source-0001", "AIBD-0107-source-0002", "AIBD-0107-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": "Howard H (2020). Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm. Daily Mail, 17 January 2020.", "title": "Saved by an app: Office worker, 31, is diagnosed with a deadly form of skin cancer after his mobile phone warned him about a tiny mole on his arm", "url": "https://www.dailymail.co.uk/health/article-7898507/Man-mole-removed-finding-skin-cancer-app-told-visit-doctor.html", "date": "2020-01-17", "written_by": "independent", "quote": "After downloading SkinVision, the app immediately came back with a high-risk rating, and I was urged to go see a doctor as soon as possible." }, { "kind": "news report", "citation": "Al-Dostour (2020). سكان فيزيون. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة [Skin Vision: technology detects cancer in an innovative way]. dostor.org, 18 January 2020, crediting the Daily Mail; one of the five Arabic-language sources cited by the OECD Monitor.", "title": "سكان فيزيون.. التكنولوجيا تكشف عن السرطان بطريقة مبتكرة", "url": "https://www.dostor.org/2974509", "date": "2020-01-18", "written_by": "independent", "quote": "وأفادت صحيفة \"ديلي ميل\" البريطانية، فإن الشاب جيسون شيريدان، (31 عاما) قد لاحظ وجود الشامة على ذراعه الأيسر لأول مرة في عام 2018" }, { "kind": "press release", "citation": "SkinVision (2025). SkinVision | Skin Cancer Detection App. Company website, fetched 20 October 2026; the page served to US visitors gives no legal entity or address.", "title": "SkinVision | Skin Cancer Detection App", "url": "https://www.skinvision.com/", "written_by": "makers", "quote": "SkinVision was founded with a mission to improve access to skin health information and support individuals in becoming more aware of their skin over time." } ] } }, "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": "SkinVision rated a 31-year-old's changing arm mole high risk; stage 1A melanoma was found and removed, and he is cancer-free.", "account": "Jason Sheridan, a 31-year-old office worker from Liverpool living in Vauxhall, South London, noticed a new 1cm mole on his left arm in March 2018. He did not see a doctor. Twelve months later it turned pink and he checked it with SkinVision, an AI app that grades photographed moles low, medium or high risk. It returned a high-risk rating and urged him to see a doctor. His GP referred him to a dermatologist and two weeks later the mole was removed in a 40-minute operation. The biopsy showed stage 1A melanoma, a further biopsy found no spread, and he is cancer-free. The Daily Mail reported the case on 17 January 2020 without dates for the app check or surgery." } }, "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": "SkinVision skin-check app (AI analysis of smartphone photographs of moles, graded low, medium or high risk)" } }, "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": "SkinVision" } }, "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": "SkinVision app rated a changing mole on Jason Sheridan's arm high risk; a dermatologist found stage 1A melanoma and it was removed", "headline": "SkinVision flags a mole as high risk; stage 1A melanoma removed" } }, "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": [ "695ed3c0cef61a50eb97a4a79d29d0f1f61c2fb57087826e4ed8213ec7b02e69" ], "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": "Jason Sheridan, a 31-year-old office worker from Liverpool living in Vauxhall, South London, noticed a new 1cm mole on his left arm in March 2018. He did not see a doctor. Twelve months later it turned pink and he checked it with SkinVision, an AI app that grades photographed moles low, medium or high risk. It returned a high-risk rating and urged him to see a doctor. His GP referred him to a dermatologist and two weeks later the mole was removed in a 40-minute operation. The biopsy showed stage 1A melanoma, a further biopsy found no spread, and he is cancer-free. The Daily Mail reported the case on 17 January 2020 without dates for the app check or surgery.", "replication": { "produced_by": [ "A smartphone photograph of a changing mole submitted to SkinVision's risk classifier", "A high-risk rating with an instruction to see a doctor, acted on by the user", "A GP referral and an excision with pathology that confirms and stages the lesion" ], "must_hold": [ "The lesion is one the classifier can score from a phone photograph. The article reports no score or app version", "The user follows the app's advice rather than treating the rating as noise", "Clinical follow-up is available quickly enough for early-stage removal. Here the operation came two weeks after referral" ], "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-0304", "case_url": "records/AIBD-0304.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The watch's fall classifier triggered the 911 call and the location message to his family. Apple's support page says the watch calls automatically only if it detects a hard fall and then immobility for about a minute. The hiker was conscious and spoke to the dispatcher once connected. All sources are news reports from October 2019. The response-count claim is attributed to Middletown police by NBC News." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0304", "title": "Apple Watch Fall Detection calls 911 after hiker James Prudenciano, 28, falls from a ledge in Hartshorne Woods Park, New Jersey; three agencies respond", "summary": "On 14 October 2019 a lost hiker fell about 30 ft in Hartshorne Woods Park, NJ; his new Apple Watch's Fall Detection called 911 and texted his family; Middletown police say three agencies responded.", "date_observed": "2019-10-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "James Prudenciano, 28, and his hiking companion (named by the Associated Press as Paige Paruso) got lost in Hartshorne Woods Park, Monmouth County, New Jersey. It was 14 October 2019, as darkness fell. They slid down a steep slope onto a ledge. Police said they fell about 30 feet (NBC News, 23 October 2019). He had bought the watch two days earlier and turned on Fall Detection while exploring its settings. He told NBC New York he heard “911, what's your emergency” from the watch. The watch also texted his mother and sister with his location, and his mother drove to the park. “Three agencies responded, according to Middletown police”, and a civilian boater brought the pair to safety. NBC News reports a fractured back and leg and foot injuries. ABC7 New York reports a fracture to his right hand. The Associated Press says undisclosed injuries. No police report or dispatch record is among the sources.", "category": "protective response", "unexpected": false, "purpose": "Fall Detection is built to detect hard falls and, after about a minute of immobility, call emergency services and message emergency contacts with a location. This call was that purpose.", "system": "Apple Watch Fall Detection", "model_version": null, "developer": "Apple", "deployer": null, "location": "Hartshorne Woods Park, Middletown Township, New Jersey, United States", "domain": "Search and rescue", "segment": "Consumer wearables", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "An injured hiker stranded on a ledge at night was located and brought out the same night. Injuries are reported inconsistently (fractured back per NBC News, fractured hand per ABC7). Whether he could have called 911 himself is not addressed." }, { "group": "organizational", "depth": 1, "reason": "Middletown police say three agencies responded to the automated call. No statement on search time saved is among the sources." } ], "ai_role": { "status": "assessed", "statement": "The watch's fall classifier triggered the 911 call and the location message to his family. Apple's support page says the watch calls automatically only if it detects a hard fall and then immobility for about a minute. The hiker was conscious and spoke to the dispatcher once connected. All sources are news reports from October 2019. The response-count claim is attributed to Middletown police by NBC News." }, "evidence": [ { "kind": "news report", "citation": "Kaur H / NBC News (2019). Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff. 23 October 2019.", "title": "Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff", "url": "https://www.nbcnews.com/news/us-news/apple-watch-s-hard-fall-feature-automatically-calls-911-hiker-n1070471", "date": "2019-10-23", "written_by": "independent", "quote": "Three agencies responded, according to Middletown police. The hikers were brought to safety by a civilian boater who offered to help." }, { "kind": "news report", "citation": "Yates T / WABC-TV (2019). Apple Watch calls 911 after man falls down New Jersey cliff. ABC7 New York, 23 October 2019.", "title": "Apple Watch calls 911 after man falls down New Jersey cliff", "url": "https://abc7ny.com/post/apple-watch-calls-911-after-man-falls-down-nj-cliff/5638758/", "date": "2019-10-23", "written_by": "independent", "quote": "I just remember hearing, '911, what's your emergency?' and my Apple Watch started talking to me" }, { "kind": "news report", "citation": "Associated Press (2019). Apple Watch calls 911 after 2 hikers fall off NJ cliff. Republished by EMS1, 25 October 2019.", "title": "Apple Watch calls 911 after 2 hikers fall off NJ cliff", "url": "https://www.ems1.com/technology/articles/apple-watch-calls-911-after-2-hikers-fall-off-nj-cliff-gPSzpfkNa0ox7vn9/", "date": "2019-10-25", "written_by": "independent", "quote": "Prudenciano had bought the watch two days earlier and chose to activate the “fall detection” feature" }, { "kind": "press release", "citation": "Apple Support. Use Fall Detection with Apple Watch (article 108896), published 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ], "replication": { "produced_by": [ "An Apple Watch Series 4 or later with Fall Detection switched on (off by default for wearers under 55)", "A hard fall followed by immobility, or a wearer who confirms the call", "Cellular or Wi-Fi connectivity from the watch or a nearby iPhone" ], "must_hold": [ "The fall classifier recognizes the impact as a hard fall", "Emergency services can act on the transmitted coordinates" ], "echoes": [ { "date": "2025-10-31", "setting": "Tenmile Range near Breckenridge, Colorado: photographer Phong Le fell down a scree slope; his Apple Watch Ultra 3 detected the fall and began the SOS countdown, he confirmed the call, and Summit County Rescue Group ran a day-long rescue (Outside, 1–2 December 2025)", "model_version": "Apple Watch Ultra 3", "outcome": "recurred", "source": "https://www.outsideonline.com/outdoor-adventure/hiking-and-backpacking/apple-watch-sos-call/" }, { "date": "2026-07-28", "setting": "Cincinnati, Ohio: Mohammad Islam collapsed outside his home while alone; the watch called 911 and paramedics arrived; University of Cincinnati doctors found blood clots in his lungs (9to5Mac citing WKRC, 28 July 2026; event “several weeks” earlier)", "model_version": null, "outcome": "recurred", "source": "https://9to5mac.com/2026/07/28/man-credits-apple-watch-with-saving-his-life-after-collapsing-at-home/" }, { "date": "2024-03-01", "setting": "New York City: Eric Zollinger, 49, fainted in his bathroom after a bicycle crash; the watch struck the tub, called 911 and messaged his contacts with his location; he canceled the call and was checked at a hospital (New York Post, 17 April 2024; event “last month”, first day used)", "model_version": null, "outcome": "recurred", "source": "https://nypost.com/2024/04/17/tech/bicyclists-life-saved-by-smartwatch-that-called-911-after-crash/" } ] }, "paired_incident": null, "headline": "Apple Watch Fall Detection calls 911 for hiker James Prudenciano", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "No report later than October 2019 about Prudenciano was found; the later Apple Watch fall-detection rescues concern other people.", "quote": null, "url": null }, "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": "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, 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: three 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-0304", "catalog_version": "1.0.0", "source_fingerprint": "fff19628222890d6a5028a1df5dd721e966c0b0226da6ff3e01d2cd2647429d9", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0304-source-0001", "AIBD-0304-source-0002", "AIBD-0304-source-0003", "AIBD-0304-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": "Apple" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 watch's fall classifier triggered the 911 call and the location message to his family. 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"value_present", "value": { "kind": "record_fields", "fields": { "account": "James Prudenciano, 28, and his hiking companion (named by the Associated Press as Paige Paruso) got lost in Hartshorne Woods Park, Monmouth County, New Jersey. It was 14 October 2019, as darkness fell. They slid down a steep slope onto a ledge. Police said they fell about 30 feet (NBC News, 23 October 2019). He had bought the watch two days earlier and turned on Fall Detection while exploring its settings. He told NBC New York he heard “911, what's your emergency” from the watch. The watch also texted his mother and sister with his location, and his mother drove to the park. “Three agencies responded, according to Middletown police”, and a civilian boater brought the pair to safety. NBC News reports a fractured back and leg and foot injuries. ABC7 New York reports a fracture to his right hand. The Associated Press says undisclosed injuries. No police report or dispatch record is among the sources.", "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": 3, "reason": "An injured hiker stranded on a ledge at night was located and brought out the same night. Injuries are reported inconsistently (fractured back per NBC News, fractured hand per ABC7). Whether he could have called 911 himself is not addressed." }, { "group": "organizational", "depth": 1, "reason": "Middletown police say three agencies responded to the automated call. No statement on search time saved is among 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": "Fall Detection is built to detect hard falls and, after about a minute of immobility, call emergency services and message emergency contacts with a location. This call 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": "Hartshorne Woods Park, Middletown Township, New Jersey, United States", "developer_country": "United States" } }, "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": "Fall Detection is built to detect hard falls and, after about a minute of immobility, call emergency services and message emergency contacts with a location. This call 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": "2019-10-14", "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": "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, 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": [ "An Apple Watch Series 4 or later with Fall Detection switched on (off by default for wearers under 55)", "A hard fall followed by immobility, or a wearer who confirms the call", "Cellular or Wi-Fi connectivity from the watch or a nearby iPhone" ], "must_hold": [ "The fall classifier recognizes the impact as a hard fall", "Emergency services can act on the transmitted coordinates" ], "echoes": [ { "date": "2025-10-31", "setting": "Tenmile Range near Breckenridge, Colorado: photographer Phong Le fell down a scree slope; his Apple Watch Ultra 3 detected the fall and began the SOS countdown, he confirmed the call, and Summit County Rescue Group ran a day-long rescue (Outside, 1–2 December 2025)", "model_version": "Apple Watch Ultra 3", "outcome": "recurred", "source": "https://www.outsideonline.com/outdoor-adventure/hiking-and-backpacking/apple-watch-sos-call/" }, { "date": "2026-07-28", "setting": "Cincinnati, Ohio: Mohammad Islam collapsed outside his home while alone; the watch called 911 and paramedics arrived; University of Cincinnati doctors found blood clots in his lungs (9to5Mac citing WKRC, 28 July 2026; event “several weeks” earlier)", "outcome": "recurred", "source": "https://9to5mac.com/2026/07/28/man-credits-apple-watch-with-saving-his-life-after-collapsing-at-home/" }, { "date": "2024-03-01", "setting": "New York City: Eric Zollinger, 49, fainted in his bathroom after a bicycle crash; the watch struck the tub, called 911 and messaged his contacts with his location; he canceled the call and was checked at a hospital (New York Post, 17 April 2024; event “last month”, first day used)", "outcome": "recurred", "source": "https://nypost.com/2024/04/17/tech/bicyclists-life-saved-by-smartwatch-that-called-911-after-crash/" } ] } } }, "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 Apple Watch Series 4 or later with Fall Detection switched on (off by default for wearers under 55)", "A hard fall followed by immobility, or a wearer who confirms the call", "Cellular or Wi-Fi connectivity from the watch or a nearby iPhone" ], "must_hold": [ "The fall classifier recognizes the impact as a hard fall", "Emergency services can act on the transmitted coordinates" ], "echoes": [ { "date": "2025-10-31", "setting": "Tenmile Range near Breckenridge, Colorado: photographer Phong Le fell down a scree slope; his Apple Watch Ultra 3 detected the fall and began the SOS countdown, he confirmed the call, and Summit County Rescue Group ran a day-long rescue (Outside, 1–2 December 2025)", "model_version": "Apple Watch Ultra 3", "outcome": "recurred", "source": "https://www.outsideonline.com/outdoor-adventure/hiking-and-backpacking/apple-watch-sos-call/" }, { "date": "2026-07-28", "setting": "Cincinnati, Ohio: Mohammad Islam collapsed outside his home while alone; the watch called 911 and paramedics arrived; University of Cincinnati doctors found blood clots in his lungs (9to5Mac citing WKRC, 28 July 2026; event “several weeks” earlier)", "outcome": "recurred", "source": "https://9to5mac.com/2026/07/28/man-credits-apple-watch-with-saving-his-life-after-collapsing-at-home/" }, { "date": "2024-03-01", "setting": "New York City: Eric Zollinger, 49, fainted in his bathroom after a bicycle crash; the watch struck the tub, called 911 and messaged his contacts with his location; he canceled the call and was checked at a hospital (New York Post, 17 April 2024; event “last month”, first day used)", "outcome": "recurred", "source": "https://nypost.com/2024/04/17/tech/bicyclists-life-saved-by-smartwatch-that-called-911-after-crash/" } ] } } }, "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 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": "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, 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": "Search and rescue", "segment": "Consumer wearables" } }, "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": "Kaur H / NBC News (2019). Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff. 23 October 2019.", "title": "Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff", "url": "https://www.nbcnews.com/news/us-news/apple-watch-s-hard-fall-feature-automatically-calls-911-hiker-n1070471", "date": "2019-10-23", "written_by": "independent", "quote": "Three agencies responded, according to Middletown police. The hikers were brought to safety by a civilian boater who offered to help." }, { "kind": "news report", "citation": "Yates T / WABC-TV (2019). Apple Watch calls 911 after man falls down New Jersey cliff. ABC7 New York, 23 October 2019.", "title": "Apple Watch calls 911 after man falls down New Jersey cliff", "url": "https://abc7ny.com/post/apple-watch-calls-911-after-man-falls-down-nj-cliff/5638758/", "date": "2019-10-23", "written_by": "independent", "quote": "I just remember hearing, '911, what's your emergency?' and my Apple Watch started talking to me" }, { "kind": "news report", "citation": "Associated Press (2019). Apple Watch calls 911 after 2 hikers fall off NJ cliff. Republished by EMS1, 25 October 2019.", "title": "Apple Watch calls 911 after 2 hikers fall off NJ cliff", "url": "https://www.ems1.com/technology/articles/apple-watch-calls-911-after-2-hikers-fall-off-nj-cliff-gPSzpfkNa0ox7vn9/", "date": "2019-10-25", "written_by": "independent", "quote": "Prudenciano had bought the watch two days earlier and chose to activate the “fall detection” feature" }, { "kind": "press release", "citation": "Apple Support. Use Fall Detection with Apple Watch (article 108896), published 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ] } }, "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-0304-source-0001", "AIBD-0304-source-0002", "AIBD-0304-source-0003", "AIBD-0304-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": "Kaur H / NBC News (2019). Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff. 23 October 2019.", "title": "Apple Watch's 'Hard Fall' feature automatically calls 911 for hiker stranded on cliff", "url": "https://www.nbcnews.com/news/us-news/apple-watch-s-hard-fall-feature-automatically-calls-911-hiker-n1070471", "date": "2019-10-23", "written_by": "independent", "quote": "Three agencies responded, according to Middletown police. The hikers were brought to safety by a civilian boater who offered to help." }, { "kind": "news report", "citation": "Yates T / WABC-TV (2019). Apple Watch calls 911 after man falls down New Jersey cliff. ABC7 New York, 23 October 2019.", "title": "Apple Watch calls 911 after man falls down New Jersey cliff", "url": "https://abc7ny.com/post/apple-watch-calls-911-after-man-falls-down-nj-cliff/5638758/", "date": "2019-10-23", "written_by": "independent", "quote": "I just remember hearing, '911, what's your emergency?' and my Apple Watch started talking to me" }, { "kind": "news report", "citation": "Associated Press (2019). Apple Watch calls 911 after 2 hikers fall off NJ cliff. Republished by EMS1, 25 October 2019.", "title": "Apple Watch calls 911 after 2 hikers fall off NJ cliff", "url": "https://www.ems1.com/technology/articles/apple-watch-calls-911-after-2-hikers-fall-off-nj-cliff-gPSzpfkNa0ox7vn9/", "date": "2019-10-25", "written_by": "independent", "quote": "Prudenciano had bought the watch two days earlier and chose to activate the “fall detection” feature" }, { "kind": "press release", "citation": "Apple Support. Use Fall Detection with Apple Watch (article 108896), published 6 May 2026.", "title": "Use Fall Detection with Apple Watch", "url": "https://support.apple.com/en-us/108896", "date": "2026-05-06", "written_by": "makers", "quote": "If your watch detects that you've been immobile for about a minute, it will make the call automatically." } ] } }, "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 14 October 2019 a lost hiker fell about 30 ft in Hartshorne Woods Park, NJ; his new Apple Watch's Fall Detection called 911 and texted his family; Middletown police say three agencies responded.", "account": "James Prudenciano, 28, and his hiking companion (named by the Associated Press as Paige Paruso) got lost in Hartshorne Woods Park, Monmouth County, New Jersey. It was 14 October 2019, as darkness fell. They slid down a steep slope onto a ledge. Police said they fell about 30 feet (NBC News, 23 October 2019). He had bought the watch two days earlier and turned on Fall Detection while exploring its settings. He told NBC New York he heard “911, what's your emergency” from the watch. The watch also texted his mother and sister with his location, and his mother drove to the park. “Three agencies responded, according to Middletown police”, and a civilian boater brought the pair to safety. NBC News reports a fractured back and leg and foot injuries. ABC7 New York reports a fracture to his right hand. The Associated Press says undisclosed injuries. No police report or dispatch record 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": "Apple Watch Fall 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": "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": "Apple Watch Fall Detection calls 911 after hiker James Prudenciano, 28, falls from a ledge in Hartshorne Woods Park, New Jersey; three agencies respond", "headline": "Apple Watch Fall Detection calls 911 for hiker James Prudenciano" } }, "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": [ "fff19628222890d6a5028a1df5dd721e966c0b0226da6ff3e01d2cd2647429d9" ], "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": "James Prudenciano, 28, and his hiking companion (named by the Associated Press as Paige Paruso) got lost in Hartshorne Woods Park, Monmouth County, New Jersey. It was 14 October 2019, as darkness fell. They slid down a steep slope onto a ledge. Police said they fell about 30 feet (NBC News, 23 October 2019). He had bought the watch two days earlier and turned on Fall Detection while exploring its settings. He told NBC New York he heard “911, what's your emergency” from the watch. The watch also texted his mother and sister with his location, and his mother drove to the park. “Three agencies responded, according to Middletown police”, and a civilian boater brought the pair to safety. NBC News reports a fractured back and leg and foot injuries. ABC7 New York reports a fracture to his right hand. The Associated Press says undisclosed injuries. No police report or dispatch record is among the sources.", "replication": { "produced_by": [ "An Apple Watch Series 4 or later with Fall Detection switched on (off by default for wearers under 55)", "A hard fall followed by immobility, or a wearer who confirms the call", "Cellular or Wi-Fi connectivity from the watch or a nearby iPhone" ], "must_hold": [ "The fall classifier recognizes the impact as a hard fall", "Emergency services can act on the transmitted coordinates" ], "echoes": [ { "date": "2025-10-31", "setting": "Tenmile Range near Breckenridge, Colorado: photographer Phong Le fell down a scree slope; his Apple Watch Ultra 3 detected the fall and began the SOS countdown, he confirmed the call, and Summit County Rescue Group ran a day-long rescue (Outside, 1–2 December 2025)", "model_version": "Apple Watch Ultra 3", "outcome": "recurred", "source": "https://www.outsideonline.com/outdoor-adventure/hiking-and-backpacking/apple-watch-sos-call/" }, { "date": "2026-07-28", "setting": "Cincinnati, Ohio: Mohammad Islam collapsed outside his home while alone; the watch called 911 and paramedics arrived; University of Cincinnati doctors found blood clots in his lungs (9to5Mac citing WKRC, 28 July 2026; event “several weeks” earlier)", "outcome": "recurred", "source": "https://9to5mac.com/2026/07/28/man-credits-apple-watch-with-saving-his-life-after-collapsing-at-home/" }, { "date": "2024-03-01", "setting": "New York City: Eric Zollinger, 49, fainted in his bathroom after a bicycle crash; the watch struck the tub, called 911 and messaged his contacts with his location; he canceled the call and was checked at a hospital (New York Post, 17 April 2024; event “last month”, first day used)", "outcome": "recurred", "source": "https://nypost.com/2024/04/17/tech/bicyclists-life-saved-by-smartwatch-that-called-911-after-crash/" } ] } } }, "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-0377", "case_url": "records/AIBD-0377.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "What the system did is established only at the level of the company. Waymo says it detected a terms-of-service violation involving a firearm, pulled the driverless car over and alerted emergency services. The Independent says it is not clear how the firearm was detected. Xataka says Waymo has not explained whether detection was automatic or human. The Monitor's statement that the vehicle's AI detected the gun is not supported by any cited source. The driverless car's ability to stop on command is the only system function the accounts describe." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0377", "title": "A Waymo robotaxi is pulled over in San Francisco's Richmond District after the company detects a firearm violation; police find a loaded AR-style “ghost gun” and book two juveniles", "summary": "LA Times (12 September 2026): before 4 a.m. on 3 Sep Waymo pulled a robotaxi over in the Richmond District after detecting a firearm violation; SFPD found a loaded ghost gun and booked two minors.", "date_observed": "2026-09-03", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Shortly before 4 a.m. on Thursday 3 September 2026, Waymo pulled one of its driverless cars over in San Francisco's Richmond District. It had detected “a violation of our terms of service involving a firearm”. Waymo alerted emergency services and “cooperated fully” with police, a Waymo spokesperson told the Los Angeles Times (12 September). The San Francisco Police Department said officers made a “high-risk vehicle stop” and detained the two juvenile passengers. They found a loaded AR-style assault rifle, suspected marijuana and mace spray, and the two were taken to juvenile hall. The Independent, citing SFGATE, quotes SFPD spokesperson Allison Maxie calling the weapon an “AR-styled assault rifle also known as a ghost gun”. It says the boy and girl were booked into the Juvenile Justice Center on firearm-possession charges. The California Post puts the stop at 40th Avenue and Cabrillo Street. No source says how the firearm was detected; The Independent and Xataka both say this is unclear.", "category": "protective response", "unexpected": false, "purpose": "The Waymo Driver is built to provide driverless rides. The service's rider rules prohibit weapons, and Xataka says interior cameras record rides so that the company can check compliance and respond to emergencies. Pulling over and calling police on a firearm violation is that safeguard working, not a driving function.", "system": "Waymo driverless ride-hail vehicle (Waymo Driver) with the company's in-ride monitoring and terms-of-service enforcement", "model_version": null, "developer": "Waymo LLC", "deployer": "Waymo LLC", "location": "San Francisco, United States", "domain": "Mobility / rider safety", "segment": "Robotaxi in-ride monitoring", "benefit_type": "Reduced Violence", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-09-10-4b01" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "SFPD took a loaded AR-style ghost gun, suspected marijuana and mace from two juveniles. It booked both on firearm-possession charges after the company's alert, per the Los Angeles Times and The Independent." }, { "group": "societal", "depth": 1, "reason": "A loaded unserialized rifle was taken out of circulation, and two minors were charged. No report shows a harm that was about to occur, so the public benefit is the seizure itself." } ], "ai_role": { "status": "assessed", "statement": "What the system did is established only at the level of the company. Waymo says it detected a terms-of-service violation involving a firearm, pulled the driverless car over and alerted emergency services. The Independent says it is not clear how the firearm was detected. Xataka says Waymo has not explained whether detection was automatic or human. The Monitor's statement that the vehicle's AI detected the gun is not supported by any cited source. The driverless car's ability to stop on command is the only system function the accounts describe." }, "evidence": [ { "kind": "news report", "citation": "Kwok I (2026). Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say. Los Angeles Times, 12 September 2026, syndicated by The Spokesman-Review.", "title": "Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say", "url": "https://www.spokesman.com/stories/2026/sep/12/waymo-pulls-over-calls-police-on-juvenile-riders-w/", "date": "2026-09-12", "written_by": "independent", "quote": "The company pulled the car over after detecting a \"violation of our terms of service involving a firearm,\" the spokesperson said." }, { "kind": "news report", "citation": "The Independent (2026). Waymo pulls over robotaxi after detecting riders were carrying assault rifle. The Independent, 12 September 2026.", "title": "Waymo pulls over robotaxi after detecting riders were carrying assault rifle", "url": "https://www.independent.co.uk/news/world/americas/crime/waymo-assault-rifle-passengers-b3049133.html", "date": "2026-09-12", "written_by": "independent", "quote": "It's not clear exactly how the Waymo detected the firearm, especially one described as a \"ghost gun\" by the SFPD." }, { "kind": "news report", "citation": "Peña M (2026). Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'. The California Post, via Yahoo News, 13 September 2026.", "title": "Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'", "url": "https://www.yahoo.com/news/us/articles/waymo-calls-cops-teen-riders-001306046.html", "date": "2026-09-13", "written_by": "independent", "quote": "Both juveniles were arrested on charges related to illegal firearm possession. They were taken into custody and booked into San Francisco's Juvenile Justice Center." }, { "kind": "news report", "citation": "Marquez J (2026). Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía. Xataka, 14 September 2026.", "title": "Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía", "url": "https://www.xataka.com/movilidad/subirse-armado-a-robotaxi-puede-acabar-mal-dos-menores-comprobaron-cuando-aparecio-policia", "date": "2026-09-14", "written_by": "independent", "quote": "no sabemos si hubo detección automática, revisión humana o algún otro mecanismo que desencadenara la alerta" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco. OECD.AI, 10 September 2026.", "title": "Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco", "url": "https://oecd.ai/en/incidents/2026-09-10-4b01", "date": "2026-09-10", "written_by": "independent", "quote": "Waymo's autonomous vehicle AI detected a firearm violation involving two minors in San Francisco, pulled over, and alerted police." } ], "replication": { "produced_by": [ "Interior cameras recording the ride, which Waymo says it may review to check rule compliance and respond to emergencies (Xataka)", "A company process that pulls the car over and calls emergency services on a firearm violation", "Police able to reach the stopped car quickly before 4 a.m." ], "must_hold": [ "The weapon is visible or otherwise detectable inside the cabin", "Waymo staff or systems act on the detection in time", "Riders stay in the car until police arrive. The Independent notes the doors were not locked in the July San Mateo case" ], "echoes": [] }, "paired_incident": null, "headline": "Waymo pulls a car over; police find a loaded ghost gun with two minors", "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/2026-09-10-4b01", "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/Waymo; https://en.wikipedia.org/wiki/Waymo", "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 a registry entry. 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Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say. Los Angeles Times, 12 September 2026, syndicated by The Spokesman-Review.", "title": "Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say", "url": "https://www.spokesman.com/stories/2026/sep/12/waymo-pulls-over-calls-police-on-juvenile-riders-w/", "date": "2026-09-12", "written_by": "independent", "quote": "The company pulled the car over after detecting a \"violation of our terms of service involving a firearm,\" the spokesperson said." }, { "kind": "news report", "citation": "The Independent (2026). Waymo pulls over robotaxi after detecting riders were carrying assault rifle. The Independent, 12 September 2026.", "title": "Waymo pulls over robotaxi after detecting riders were carrying assault rifle", "url": "https://www.independent.co.uk/news/world/americas/crime/waymo-assault-rifle-passengers-b3049133.html", "date": "2026-09-12", "written_by": "independent", "quote": "It's not clear exactly how the Waymo detected the firearm, especially one described as a \"ghost gun\" by the SFPD." }, { "kind": "news report", "citation": "Peña M (2026). Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'. The California Post, via Yahoo News, 13 September 2026.", "title": "Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'", "url": "https://www.yahoo.com/news/us/articles/waymo-calls-cops-teen-riders-001306046.html", "date": "2026-09-13", "written_by": "independent", "quote": "Both juveniles were arrested on charges related to illegal firearm possession. They were taken into custody and booked into San Francisco's Juvenile Justice Center." }, { "kind": "news report", "citation": "Marquez J (2026). Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía. Xataka, 14 September 2026.", "title": "Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía", "url": "https://www.xataka.com/movilidad/subirse-armado-a-robotaxi-puede-acabar-mal-dos-menores-comprobaron-cuando-aparecio-policia", "date": "2026-09-14", "written_by": "independent", "quote": "no sabemos si hubo detección automática, revisión humana o algún otro mecanismo que desencadenara la alerta" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco. OECD.AI, 10 September 2026.", "title": "Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco", "url": "https://oecd.ai/en/incidents/2026-09-10-4b01", "date": "2026-09-10", "written_by": "independent", "quote": "Waymo's autonomous vehicle AI detected a firearm violation involving two minors in San Francisco, pulled over, and alerted 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-0377-source-0001", "AIBD-0377-source-0002", "AIBD-0377-source-0003", "AIBD-0377-source-0004", "AIBD-0377-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": "Kwok I (2026). Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say. Los Angeles Times, 12 September 2026, syndicated by The Spokesman-Review.", "title": "Waymo pulls over, calls police on juvenile riders who had 'ghost gun,' authorities say", "url": "https://www.spokesman.com/stories/2026/sep/12/waymo-pulls-over-calls-police-on-juvenile-riders-w/", "date": "2026-09-12", "written_by": "independent", "quote": "The company pulled the car over after detecting a \"violation of our terms of service involving a firearm,\" the spokesperson said." }, { "kind": "news report", "citation": "The Independent (2026). Waymo pulls over robotaxi after detecting riders were carrying assault rifle. The Independent, 12 September 2026.", "title": "Waymo pulls over robotaxi after detecting riders were carrying assault rifle", "url": "https://www.independent.co.uk/news/world/americas/crime/waymo-assault-rifle-passengers-b3049133.html", "date": "2026-09-12", "written_by": "independent", "quote": "It's not clear exactly how the Waymo detected the firearm, especially one described as a \"ghost gun\" by the SFPD." }, { "kind": "news report", "citation": "Peña M (2026). Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'. The California Post, via Yahoo News, 13 September 2026.", "title": "Waymo calls cops on teen riders after detecting loaded AR-style 'ghost gun'", "url": "https://www.yahoo.com/news/us/articles/waymo-calls-cops-teen-riders-001306046.html", "date": "2026-09-13", "written_by": "independent", "quote": "Both juveniles were arrested on charges related to illegal firearm possession. They were taken into custody and booked into San Francisco's Juvenile Justice Center." }, { "kind": "news report", "citation": "Marquez J (2026). Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía. Xataka, 14 September 2026.", "title": "Subirse armado a un robotaxi puede acabar mal: dos menores lo comprobaron cuando apareció la policía", "url": "https://www.xataka.com/movilidad/subirse-armado-a-robotaxi-puede-acabar-mal-dos-menores-comprobaron-cuando-aparecio-policia", "date": "2026-09-14", "written_by": "independent", "quote": "no sabemos si hubo detección automática, revisión humana o algún otro mecanismo que desencadenara la alerta" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor (2026). Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco. OECD.AI, 10 September 2026.", "title": "Waymo Robotaxi AI Detects Firearm, Alerts Police Leading to Juvenile Arrests in San Francisco", "url": "https://oecd.ai/en/incidents/2026-09-10-4b01", "date": "2026-09-10", "written_by": "independent", "quote": "Waymo's autonomous vehicle AI detected a firearm violation involving two minors in San Francisco, pulled over, and alerted 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": "LA Times (12 September 2026): before 4 a.m. on 3 Sep Waymo pulled a robotaxi over in the Richmond District after detecting a firearm violation; SFPD found a loaded ghost gun and booked two minors.", "account": "Shortly before 4 a.m. on Thursday 3 September 2026, Waymo pulled one of its driverless cars over in San Francisco's Richmond District. It had detected “a violation of our terms of service involving a firearm”. Waymo alerted emergency services and “cooperated fully” with police, a Waymo spokesperson told the Los Angeles Times (12 September). The San Francisco Police Department said officers made a “high-risk vehicle stop” and detained the two juvenile passengers. They found a loaded AR-style assault rifle, suspected marijuana and mace spray, and the two were taken to juvenile hall. The Independent, citing SFGATE, quotes SFPD spokesperson Allison Maxie calling the weapon an “AR-styled assault rifle also known as a ghost gun”. It says the boy and girl were booked into the Juvenile Justice Center on firearm-possession charges. The California Post puts the stop at 40th Avenue and Cabrillo Street. No source says how the firearm was detected; The Independent and Xataka both say this is unclear." } }, "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": "Waymo driverless ride-hail vehicle (Waymo Driver) with the company's in-ride monitoring and terms-of-service 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": "Waymo LLC" } }, "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 Waymo robotaxi is pulled over in San Francisco's Richmond District after the company detects a firearm violation; police find a loaded AR-style “ghost gun” and book two juveniles", "headline": "Waymo pulls a car over; police find a loaded ghost gun with two minors" } }, "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": [ "7f2a79c8b929f473a713655466f3ce80d16f4090bad88be71a75ef0b00da5a91" ], "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": "Shortly before 4 a.m. on Thursday 3 September 2026, Waymo pulled one of its driverless cars over in San Francisco's Richmond District. It had detected “a violation of our terms of service involving a firearm”. Waymo alerted emergency services and “cooperated fully” with police, a Waymo spokesperson told the Los Angeles Times (12 September). The San Francisco Police Department said officers made a “high-risk vehicle stop” and detained the two juvenile passengers. They found a loaded AR-style assault rifle, suspected marijuana and mace spray, and the two were taken to juvenile hall. The Independent, citing SFGATE, quotes SFPD spokesperson Allison Maxie calling the weapon an “AR-styled assault rifle also known as a ghost gun”. It says the boy and girl were booked into the Juvenile Justice Center on firearm-possession charges. The California Post puts the stop at 40th Avenue and Cabrillo Street. No source says how the firearm was detected; The Independent and Xataka both say this is unclear.", "replication": { "produced_by": [ "Interior cameras recording the ride, which Waymo says it may review to check rule compliance and respond to emergencies (Xataka)", "A company process that pulls the car over and calls emergency services on a firearm violation", "Police able to reach the stopped car quickly before 4 a.m." ], "must_hold": [ "The weapon is visible or otherwise detectable inside the cabin", "Waymo staff or systems act on the detection in time", "Riders stay in the car until police arrive. The Independent notes the doors were not locked in the July San Mateo case" ], "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-0375", "case_url": "records/AIBD-0375.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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-0226", "case_url": "records/AIBD-0226.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. Neither quantifies response time or states whether anyone else had called police." }, "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." } ], "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", "model_version": null, "outcome": "recurred", "source": "https://www.govtech.com/public-safety/ai-gun-detection-system-leads-to-charges-in-new-mexico" } ] }, "paired_incident": null, "headline": "ZeroEyes alert sends FSU police to a man pointing a stolen Glock", "metric": null, "comparator": null, "later": { "status": "litigated", "date": "2026-05-03", "note": "Farmer was charged on four counts and bonded out of jail on Sunday 3 May 2026, according to court documents cited by WCTV.", "quote": "He was arrested and bonded out of jail on Sunday, according to court documents.", "url": "https://www.wctv.tv/2026/05/04/court-documents-19-year-old-arrested-after-brandishing-weapon-fsu-parking-garage-early-saturday-morning/" }, "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.wctv.tv/2026/05/04/court-documents-19-year-old-arrested-after-brandishing-weapon-fsu-parking-garage-early-saturday-morning/", "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://zeroeyes.com/contact/; 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.", "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-0226", "catalog_version": "1.0.0", "source_fingerprint": "7fad5474a387556d45b9263837e3e6d278b86523681e910cc04197cbf55cbb7d", "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-0226-source-0001", "AIBD-0226-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": "ZeroEyes", "deployer": "Florida State University 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": "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." } } }, "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": "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." } } }, "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 armed man waving a loaded, stolen handgun at bystanders was arrested and the weapon recovered, per court documents reported by WCTV." }, { "group": "personal", "reason": "The four bystanders are not identified and no source documents what the arrest prevented for them." } ] } }, "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. 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 armed man waving a loaded, stolen handgun at bystanders was arrested and the weapon recovered, per court documents reported by WCTV." }, { "group": "personal", "reason": "The four bystanders are not identified and no source documents what the arrest prevented for them." } ] } }, "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 armed man waving a loaded, stolen handgun at bystanders was arrested and the weapon recovered, per court documents reported by WCTV." }, { "group": "personal", "reason": "The four bystanders are not identified and no source documents what the arrest prevented for them." } ] } }, "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", 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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-0245", "case_url": "records/AIBD-0245.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Per the report, the attention-monitoring logic issued warnings and then stopped the vehicle on the emergency lane. The agency, the vehicle data and the duration of the stop are not given. The police framed the episode as misuse rather than a rescue." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0245", "title": "Tesla on Autopilot stops on the I-75 shoulder at Sarasota with a drunk driver asleep; Florida police charge the 37-year-old with DUI", "summary": "Ouest-France (3 May 2026, citing ABC 7): around 2 a.m. on 24 Apr 2026 police found a 37-year-old woman unconscious in her stopped Tesla on Interstate 75; blood alcohol over twice the limit; charged...", "date_observed": "2026-04-24", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Alexandre Chauvel reported in Ouest-France on 3 May 2026, relaying the Sarasota station ABC 7, on a car stopped on Interstate 75. Officers were alerted at about 2 a.m. on Friday 24 April 2026 to a car on the emergency lane at Sarasota, Florida. They found a 37-year-old woman unconscious at the wheel of a Tesla. Local police, quoted without naming the agency or officer, said she should not have been driving. They said her blood alcohol was more than twice the legal limit. They said she had assumed the Tesla would bring her home safely on Autopilot. The article explains that cabin sensors check that the driver is looking ahead and warn them. If the driver does not reopen their eyes after the first warning, the vehicle stops. She was arrested and is being prosecuted for driving under the influence. The Tesla model is not stated. Neither a police report nor the ABC 7 story is among the sources. 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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": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Autopilot engaged by an impaired driver on a limited-access highway", "Cabin camera attention monitoring with escalating warnings", "A stop-in-lane or shoulder-stop fallback when warnings go unanswered" ], "must_hold": [ "The driver does not respond to warnings", "The road has a shoulder or lane where a stopped car is not struck", "Police attend before another vehicle collides with the stationary car" ], "echoes": [ { "date": "2026-02-19", "setting": "Cybertruck owner Rishi Vohra says FSD's driver monitoring detected his blackout on a freeway and pulled the truck onto the shoulder (owner's account via Teslarati)", "outcome": "recurred", "source": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/" }, { "date": "2025-06-01", "setting": "NIO World Model emergency pull-over stopped a sleeping owner's car at 110 km/h on a rainy highway in China and placed an SOS call (owner's video reposted by NIO's founder)", "outcome": "recurred", "source": "https://news.mydrivers.com/1/1051/1051320.htm" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Tesla,_Inc.", "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": "Road vehicle crash avoidance", "segment": "Consumer driver assistance / driver monitoring" } }, "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": "Chauvel A (3 May 2026). Ivre, elle s'endort dans sa Tesla en mode Autopilot : la voiture s'arrête sur l'autoroute. Ouest-France.", "title": "Ivre, elle s’endort dans sa Tesla en mode Autopilot : la voiture s’arrête sur l’autoroute", "url": "https://web.archive.org/web/2026/https://www.ouest-france.fr/societe/faits-divers/ivre-elle-sendort-dans-sa-tesla-en-mode-autopilot-la-voiture-sarrete-sur-lautoroute-8519ae64-42eb-11f1-8492-0dabd481308d", "date": "2026-05-03", "written_by": "independent", "quote": "Malheureusement, son alcoolémie était plus de deux fois supérieure à la limite légale et elle a supposé que la Tesla la ramènerait chez elle en toute sécurité" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-05-03-516b: Intoxicated Driver Relies on Tesla Autopilot, Car Stops on Florida Highway.", "title": "Intoxicated Driver Relies on Tesla Autopilot, Car Stops on Florida Highway", "url": "https://oecd.ai/en/incidents/2026-05-03-516b", "date": "2026-05-03", "written_by": "independent", "quote": "She fell asleep, and the AI system eventually stopped the car on the highway after detecting her unresponsiveness." } ] } }, "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-0245-source-0001", "AIBD-0245-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": "Chauvel A (3 May 2026). Ivre, elle s'endort dans sa Tesla en mode Autopilot : la voiture s'arrête sur l'autoroute. Ouest-France.", "title": "Ivre, elle s’endort dans sa Tesla en mode Autopilot : la voiture s’arrête sur l’autoroute", "url": "https://web.archive.org/web/2026/https://www.ouest-france.fr/societe/faits-divers/ivre-elle-sendort-dans-sa-tesla-en-mode-autopilot-la-voiture-sarrete-sur-lautoroute-8519ae64-42eb-11f1-8492-0dabd481308d", "date": "2026-05-03", "written_by": "independent", "quote": "Malheureusement, son alcoolémie était plus de deux fois supérieure à la limite légale et elle a supposé que la Tesla la ramènerait chez elle en toute sécurité" }, { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, incident 2026-05-03-516b: Intoxicated Driver Relies on Tesla Autopilot, Car Stops on Florida Highway.", "title": "Intoxicated Driver Relies on Tesla Autopilot, Car Stops on Florida Highway", "url": "https://oecd.ai/en/incidents/2026-05-03-516b", "date": "2026-05-03", "written_by": "independent", "quote": "She fell asleep, and the AI system eventually stopped the car on the highway after detecting her unresponsiveness." } ] } }, "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": "Ouest-France (3 May 2026, citing ABC 7): around 2 a.m. on 24 Apr 2026 police found a 37-year-old woman unconscious in her stopped Tesla on Interstate 75; blood alcohol over twice the limit; charged...", "account": "Alexandre Chauvel reported in Ouest-France on 3 May 2026, relaying the Sarasota station ABC 7, on a car stopped on Interstate 75. Officers were alerted at about 2 a.m. on Friday 24 April 2026 to a car on the emergency lane at Sarasota, Florida. They found a 37-year-old woman unconscious at the wheel of a Tesla. Local police, quoted without naming the agency or officer, said she should not have been driving. They said her blood alcohol was more than twice the legal limit. They said she had assumed the Tesla would bring her home safely on Autopilot. The article explains that cabin sensors check that the driver is looking ahead and warn them. If the driver does not reopen their eyes after the first warning, the vehicle stops. She was arrested and is being prosecuted for driving under the influence. The Tesla model is not stated. Neither a police report nor the ABC 7 story is among the sources. The OECD AI Incidents Monitor lists the event as a protective response with French-language 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": "Tesla Autopilot with cabin driver-attention monitoring (model not stated)" } }, "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": "Tesla" } }, "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": "Tesla on Autopilot stops on the I-75 shoulder at Sarasota with a drunk driver asleep; Florida police charge the 37-year-old with DUI", "headline": "Tesla Autopilot stops on I-75 shoulder; drunk driver charged" } }, "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": [ "5dc89dc014bbf9b98920f5a4e5698ac635aadd66e26fc9e1c06e961514fcfed6" ], "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": "Alexandre Chauvel reported in Ouest-France on 3 May 2026, relaying the Sarasota station ABC 7, on a car stopped on Interstate 75. Officers were alerted at about 2 a.m. on Friday 24 April 2026 to a car on the emergency lane at Sarasota, Florida. They found a 37-year-old woman unconscious at the wheel of a Tesla. Local police, quoted without naming the agency or officer, said she should not have been driving. They said her blood alcohol was more than twice the legal limit. They said she had assumed the Tesla would bring her home safely on Autopilot. The article explains that cabin sensors check that the driver is looking ahead and warn them. If the driver does not reopen their eyes after the first warning, the vehicle stops. She was arrested and is being prosecuted for driving under the influence. The Tesla model is not stated. Neither a police report nor the ABC 7 story is among the sources. The OECD AI Incidents Monitor lists the event as a protective response with French-language sources.", "replication": { "produced_by": [ "Autopilot engaged by an impaired driver on a limited-access highway", "Cabin camera attention monitoring with escalating warnings", "A stop-in-lane or shoulder-stop fallback when warnings go unanswered" ], "must_hold": [ "The driver does not respond to warnings", "The road has a shoulder or lane where a stopped car is not struck", "Police attend before another vehicle collides with the stationary car" ], "echoes": [ { "date": "2026-02-19", "setting": "Cybertruck owner Rishi Vohra says FSD's driver monitoring detected his blackout on a freeway and pulled the truck onto the shoulder (owner's account via Teslarati)", "outcome": "recurred", "source": "https://www.teslarati.com/tesla-cybertruck-fsd-saving-life-freeway-medical-emergency/" }, { "date": "2025-06-01", "setting": "NIO World Model emergency pull-over stopped a sleeping owner's car at 110 km/h on a rainy highway in China and placed an SOS call (owner's video reposted by NIO's founder)", "outcome": "recurred", "source": "https://news.mydrivers.com/1/1051/1051320.htm" } ] } } }, "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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "the previous reactive practice of attending blockages after a flooding or pollution event", "url": "https://www.dailyecho.co.uk/news/26045031.fatberg-found-gosport-removed-causing-damage/" }, { "label": "Before", "value": "turning up after the event to clean up and commiserate with devastated customers" }, { "label": "After", "value": "spotting hundreds of potential blockages before it is too late" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. 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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-0312", "case_url": "records/AIBD-0312.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "the first 911 call reporting a fire", "url": "https://www.courthousenews.com/states-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection/" }, { "label": "Before", "value": "the first 911 call" }, { "label": "After", "value": "about 45 minutes faster on average than the first 911 call" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0312", "catalog_version": "1.0.0", "source_fingerprint": "c81aed4312d5a518f38cb56ae8aac7ef5e300608c8f387fa0b37d080e2be4538", "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-0312-source-0001", "AIBD-0312-source-0002", "AIBD-0312-source-0003", "AIBD-0312-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": "Pano AI, by implication of AP's customer list; not stated for this camera", "deployer": "Arizona Public Service" } }, "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": "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." } } }, "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": "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." } } }, "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": "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" }, "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. 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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. 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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-0155", "case_url": "records/AIBD-0155.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The system's documented contribution is the first alert. By the temple's own log it detected smoke at 05:30, a minute before the lithium batteries burst at 05:33. It messaged the front desk, the general manager and the chairman in sequence over LINE. That is how a chairman asleep in Nara came to follow the fire on the temple's cameras. The suppression was human. The clerk's extinguisher failed once the batteries burst. The Yunlin County Fire Bureau's eight trucks and eighteen firefighters, called at 05:36, controlled the fire at 06:10. UDN reports that a residential smoke alarm (住警器) sounded as well. The Third Brigade commander 陳岳謄 attributed the containment to the building's reinforced-concrete structure and the crews' quick arrival without mentioning the AI. The LINE relay from the vendor is the chairman's account, not the bureau's." }, { "label": "Comparison", "value": "the temple's overnight duty staff relying on their own watch, without the AI smoke alert", "url": "https://n.yam.com/Article/20260205197916" }, { "label": "After", "value": "在火勢轉劇前一分鐘便偵測到煙霧" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0155", "title": "LEADERG's “AI door god” camera analytics at Beigang Wude Temple flags smoke in an empty office a minute before charging lithium batteries burst; the night clerk and Yunlin firefighters hold the 5 February 2026 fire to ten square meters, nobody hurt, main hall untouched", "summary": "AI camera analytics flagged smoke in an empty temple office a minute before a battery fire; held to 10 m², no injuries, main hall intact.", "date_observed": "2026-02-05", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At about 05:30 on 5 February 2026 the AI門神 (AI door god) system flagged smoke in a first-floor office and machine room, empty after hours. The system is run by 立達軟體 (LEADERG Inc.) over the closed-circuit television (CCTV) of Beigang Wude Temple, Yunlin County, Taiwan. It messaged the front desk, the general manager and chairman 林安樂, in Japan, by LINE. The night-duty clerk reached the room with an extinguisher within a minute. At 05:33 overheated lithium batteries beside a tool charger burst and flames spread. Staff called 119 at 05:36. Yunlin County Fire Bureau crews arrived at 05:44, controlled it at 06:10 and put it out at 06:14. Ten square meters burned. Nobody was hurt. The main hall and statues were untouched.", "category": "protective response", "unexpected": false, "purpose": "AI門神 (“AI door god: temple AI fire-and-theft prevention system”) was built to run AI image recognition over a temple's existing CCTV. It was built to detect excess smoke, flame and suspicious behavior, and to alert staff by SMS, LINE or email. CTS reported in December 2024 that Wude Temple's door-god system would sound an alarm at once on abnormal smoke. Flagging smoke before a fire was exactly its designed purpose.", "system": "AI門神：宮廟AI防火防盜系統 (AI Door God: temple AI fire-and-theft prevention system), AI image recognition over the temple's existing CCTV with alerts by SMS, LINE and email", "model_version": "Not named. Advantech's case study says the image-recognition algorithms were developed with a large language model as the basis and run on an Advantech IPC-730 with a SKY-QUAD-5000A-32 accelerator card at about five seconds per frame.", "developer": "立達軟體科技股份有限公司 (LEADERG Inc.), Taiwan", "deployer": "北港武德宮 (Beigang Wude Temple)", "location": "Beigang, Taiwan", "domain": "Fire safety (religious site)", "segment": "Temple and cultural-asset protection: AI video analytics on existing CCTV", "benefit_type": "Reduced Risk", "taxonomy_note": "Fire spread averted is read as reduced potential for harm; Trustible's description stresses legal harm.", "registry_ids": { "oecd_aim": "2026-02-04-4944" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The temple's loss was the contents of one first-floor office of about ten square meters (staff lockers, some tea, sweets, incense and joss paper). The main hall, statues and worship areas were untouched. The temple stayed open in the run-up to the Lunar New Year crowds. The fire bureau credited the reinforced-concrete structure and its own eight-minute arrival for the containment. A conventional smoke alarm sounded as well. So the share attributable to the AI alert is a minute or two of earlier warning, not the whole saved building." } ], "ai_role": { "status": "assessed", "statement": "The system's documented contribution is the first alert. By the temple's own log it detected smoke at 05:30, a minute before the lithium batteries burst at 05:33. It messaged the front desk, the general manager and the chairman in sequence over LINE. That is how a chairman asleep in Nara came to follow the fire on the temple's cameras. The suppression was human. The clerk's extinguisher failed once the batteries burst. The Yunlin County Fire Bureau's eight trucks and eighteen firefighters, called at 05:36, controlled the fire at 06:10. UDN reports that a residential smoke alarm (住警器) sounded as well. The Third Brigade commander 陳岳謄 attributed the containment to the building's reinforced-concrete structure and the crews' quick arrival without mentioning the AI. The LINE relay from the vendor is the chairman's account, not the bureau's." }, "evidence": [ { "kind": "news report", "citation": "蔡維斌 / 聯合新聞網 (2026). 「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷. UDN, 5 February 2026.", "title": "「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷", "url": "https://udn.com/news/story/7320/9310088", "date": "2026-02-05", "written_by": "independent", "quote": "幸廟內設有AI警報系統及住警器，馬上發出警報聲響" }, { "kind": "news report", "citation": "蔡維斌 / 聯合新聞網 (2026). 強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場. UDN, 5 February 2026.", "title": "強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場", "url": "https://udn.com/news/story/7320/9310526", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢發生前一分鐘系統即已示警煙霧產生" }, { "kind": "news report", "citation": "吳淑萍 / TVBS新聞網 (2026). 北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟. TVBS, 5 February 2026.", "title": "北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟", "url": "https://news.tvbs.com.tw/local/3120819", "date": "2026-02-05", "written_by": "independent", "quote": "5點30分系統偵測到煙霧，5點32分人工察看確認狀況" }, { "kind": "news report", "citation": "蘇榮泉 / 觀傳媒 (2026). （有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "（有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控", "url": "https://n.yam.com/Article/20260205444546", "date": "2026-02-05", "written_by": "independent", "quote": "消防人員於6時10分成功控制火勢，並於6時14分完全撲滅" }, { "kind": "news report", "citation": "洪佳伶 / 觀傳媒 (2026). AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢", "url": "https://n.yam.com/Article/20260205197916", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢轉劇前一分鐘便偵測到煙霧，並依序通知櫃檯、總幹事" }, { "kind": "news report", "citation": "華視新聞 / CTS (2026). 「AI門神」立功！　武德宮火警偵測回報　快速控制火勢. CTS News, 5 February 2026.", "title": "「AI門神」立功！　武德宮火警偵測回報　快速控制火勢", "url": "https://news.cts.com.tw/cts/general/202602/202602052559104.html", "date": "2026-02-05", "written_by": "independent", "quote": "好險有AI門神，幫我們守護住，所以我們可以趕快第一時間" }, { "kind": "news report", "citation": "鄧庭婷 / NOWnews今日新聞 (2026). 北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸. NOWnews, 5 February 2026.", "title": "北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸", "url": "https://www.nownews.com/news/6783602", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火起前的關鍵一分鐘發出示警" }, { "kind": "news report", "citation": "莊曜聰 / 壹蘋新聞網 (2026). 北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」. NextApple, 5 February 2026.", "title": "北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」", "url": "https://news.nextapple.com/local/20260205/E631BE3355F1C46E511BAEBE0A5D0D61", "date": "2026-02-05", "written_by": "independent", "quote": "還有「門神系統」可以示警防災，這次果然派上用場" }, { "kind": "news report", "citation": "蘇峯毅 / 台灣好報 (2026). 清晨火警驚魂　北港武德宮即時搶救守住信仰中心. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "清晨火警驚魂　北港武德宮即時搶救守住信仰中心", "url": "https://n.yam.com/Article/20260205590605", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火勢形成前即已示警，加上長年夜間留守制度" }, { "kind": "news report", "citation": "華視新聞 / CTS (2024). AI進駐宮廟　統計人流.預防火災還能「解籤詩」. Syndicated on Yahoo奇摩新聞, 23 December 2024.", "title": "AI進駐宮廟　統計人流.預防火災還能「解籤詩」", "url": "https://tw.news.yahoo.com/ai%E9%80%B2%E9%A7%90%E5%AE%AE%E5%BB%9F-%E7%B5%B1%E8%A8%88%E4%BA%BA%E6%B5%81-%E9%A0%90%E9%98%B2%E7%81%AB%E7%81%BD%E9%82%84%E8%83%BD-%E8%A7%A3%E7%B1%A4%E8%A9%A9-121500289.html", "date": "2024-12-23", "written_by": "independent", "quote": "門神系統，只要偵測到煙霧異常，有火災的疑慮，會立刻發出警報" }, { "kind": "press release", "citation": "Advantech (2025). 立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護. Advantech case study, 15 April 2025.", "title": "立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護", "url": "https://www.advantech.com/en-us/resources/case-study/%E7%AB%8B%E9%81%94%E8%BB%9F%E9%AB%94%E6%94%9C%E6%89%8B%E7%A0%94%E8%8F%AFai%E9%96%80%E7%A5%9E%EF%BC%9A%E5%89%B5%E6%96%B0%E6%8A%80%E8%A1%93%E5%8A%A9%E5%8A%9B%E5%AF%BA%E5%BB%9F%E6%96%87%E5%8C%96%E8%B3%87%E7%94%A2%E4%BF%9D%E8%AD%B7", "date": "2025-04-15", "written_by": "makers", "quote": "偵測特定區域內是否有過量的煙霧、火焰及可疑行為" }, { "kind": "news report", "citation": "聯合新聞網 UDN (2026). 指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」. udn.com, 25 August 2026.", "title": "指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」", "url": "https://udn.com/news/story/7326/9714008", "date": "2026-08-25", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "A camera-analytics system (AI門神) already watching the office over the temple's existing CCTV. It was set to alert on small amounts of smoke and to message a named chain of people (front desk, general manager, chairman) over LINE.", "A staffed night watch. The temple has kept someone on duty every night since it moved to the site in 1980 (民國69年). So the alert had a clerk a minute away with an extinguisher.", "A slow start to the fire. A tool charger and lithium batteries smoked before the batteries burst at 05:33, which gave the alert a minute's lead.", "A fire brigade eight minutes away and a reinforced-concrete annex that held the fire to one room, as the Third Brigade commander said." ], "must_hold": [ "The alert reaches a person who can act within minutes. At an unstaffed site the lead time is worth little.", "The camera covers the rooms where fires start (here an office and machine room with chargers, beside stored joss paper), not only the shrine.", "The AI alert complements conventional detectors rather than replacing them. A residential smoke alarm sounded here as well." ], "echoes": [ { "date": null, "setting": "OECD AIM 2023-09-12-f6ac: ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna, human operators verify it and Cal Fire holds the fire (camera smoke detection with a human in the loop and a brigade response).", "model_version": null, "outcome": "unknown", "source": null }, { "date": null, "setting": "OECD AIM 2025-09-13-b5dc: Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner (Taiwanese camera smoke analytics in a different use).", "model_version": null, "outcome": "unknown", "source": null } ] }, "paired_incident": null, "headline": "LEADERG's AI door god flags smoke; temple fire held to 10 m²", "metric": "held to 10 m², no injuries, main hall intact", "comparator": { "against": "the temple's overnight duty staff relying on their own watch, without the AI smoke alert", "before": null, "after": "在火勢轉劇前一分鐘便偵測到煙霧", "basis": "assumed", "quote": "AI系統非常專業，在火勢轉劇前一分鐘便偵測到煙霧，並依序通知櫃檯、總幹事及我本人", "url": "https://n.yam.com/Article/20260205197916" }, "later": { "status": "persisted", "date": "2026-08-25", "note": "In August 2026, after the Zhinan Temple fire in Taipei, UDN reported Wude Temple still relying on its AI alarm system combined with the cameras, recounting the February fire as the case that proved it; no other temple adopting the system is reported.", "quote": "今年2月廟的右側辦公室、倉庫，也在清晨沒人時發生火警，幸好廟方全面改裝「AI警報系統」結合監視器", "url": "https://udn.com/news/story/7326/9714008" }, "value": null, "developer_country": "Taiwan", "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 protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a smoke alert at 05:30, a minute before the batteries burst, relayed over LINE to the front desk, general manager and chairman) is documented by the temple's own timeline as reported by TVBS, UDN, NOWnews, 觀傳媒 and CTS, and the outcome by the Yunlin County Fire Bureau's figures, but the counterfactual is not shown: UDN reports that a residential smoke alarm (住警器) sounded as well, a clerk was on night duty in any case, and the Third Brigade commander 陳岳謄 credited the RC structure and the crews' quick arrival without mentioning the AI. The Monitor's date of 4 February is the UTC date; every source puts the fire at about 05:30 on 5 February 2026 Taiwan time (21:30 UTC on 4 February), so the date observed is 5 February. Times differ between the temple's log (fire controlled 05:51) and the fire bureau (controlled 06:10, extinguished 06:14); the record uses the bureau's. The cause (a tool charger and lithium batteries) is the chairman's account; UDN's first report says the bureau was still investigating and the secretary general 蘇建華 told TVBS he suspected wiring. The vendor is named by the chairman as 立達軟體; its identity as LEADERG Inc. and the system's design (AI image recognition over existing CCTV on Advantech hardware, alerts by SMS, LINE or email, deployed at Wude Temple) come from Advantech's April 2025 case study, which is partner-authored and used only as an additional source. The Monitor lists China Times, which could not be read (Cloudflare challenge directly and through a text proxy; web.archive.org is blocked for this tool); its four yam.com links are syndications of 觀傳媒 (two), 台灣好報 and 創新聞. The CTS broadcast report of 5 February and CTS's December 2024 piece on the door-god system were found through Brave, as WebSearch was exhausted; DuckDuckGo and Mojeek returned nothing. The chairman's Facebook post was not read; it is quoted by UDN, NOWnews and NextApple. No Yunlin County Fire Bureau release was found on its own site; its statements are quoted by 觀傳媒, 台灣好報 and 創新聞, and CTS quotes the Beigang sub-station chief 陳思淵. The Monitor's country field is “Chinese Taipei”; the record says Taiwan, as every source does. Raw HTML of every source read is kept in src_4944.", "source": "https://oecd.ai/en/incidents/2026-02-04-4944", "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://n.yam.com/Article/20260205197916; https://udn.com/news/story/7326/9714008", "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.leaderg.com/tw/contact_us; https://zh.wikipedia.org/wiki/北港武德宮", "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: 11 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-0155", "catalog_version": "1.0.0", "source_fingerprint": "d9e50b5220d086e0c9bd46b934073b730fafc534e423027f5f3acbdea2c79fa3", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0155-source-0001", "AIBD-0155-source-0002", "AIBD-0155-source-0003", "AIBD-0155-source-0004", "AIBD-0155-source-0005", "AIBD-0155-source-0006", "AIBD-0155-source-0007", "AIBD-0155-source-0008", "AIBD-0155-source-0009", "AIBD-0155-source-0010", "AIBD-0155-source-0011", "AIBD-0155-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": "立達軟體科技股份有限公司 (LEADERG Inc.), Taiwan", "deployer": "北港武德宮 (Beigang Wude Temple)" } }, "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's documented contribution is the first alert. By the temple's own log it detected smoke at 05:30, a minute before the lithium batteries burst at 05:33. It messaged the front desk, the general manager and the chairman in sequence over LINE. That is how a chairman asleep in Nara came to follow the fire on the temple's cameras. The suppression was human. The clerk's extinguisher failed once the batteries burst. The Yunlin County Fire Bureau's eight trucks and eighteen firefighters, called at 05:36, controlled the fire at 06:10. UDN reports that a residential smoke alarm (住警器) sounded as well. The Third Brigade commander 陳岳謄 attributed the containment to the building's reinforced-concrete structure and the crews' quick arrival without mentioning the AI. The LINE relay from the vendor is the chairman's account, not the bureau's." } } }, "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": "According to the temple’s log, AI Door God detected smoke at 05:30 and sent alerts through LINE. The chairman followed the developing fire through the temple’s cameras.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The system's documented contribution is the first alert. By the temple's own log it detected smoke at 05:30, a minute before the lithium batteries burst at 05:33. It messaged the front desk, the general manager and the chairman in sequence over LINE. That is how a chairman asleep in Nara came to follow the fire on the temple's cameras. The suppression was human. The clerk's extinguisher failed once the batteries burst. The Yunlin County Fire Bureau's eight trucks and eighteen firefighters, called at 05:36, controlled the fire at 06:10. UDN reports that a residential smoke alarm (住警器) sounded as well. The Third Brigade commander 陳岳謄 attributed the containment to the building's reinforced-concrete structure and the crews' quick arrival without mentioning the AI. The LINE relay from the vendor is the chairman's account, not the bureau's." } } }, "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 temple's loss was the contents of one first-floor office of about ten square meters (staff lockers, some tea, sweets, incense and joss paper). The main hall, statues and worship areas were untouched. The temple stayed open in the run-up to the Lunar New Year crowds. The fire bureau credited the reinforced-concrete structure and its own eight-minute arrival for the containment. A conventional smoke alarm sounded as well. So the share attributable to the AI alert is a minute or two of earlier warning, not the whole saved building." } ] } }, "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: 11 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": "organizational", "depth": 2, "reason": "The temple's loss was the contents of one first-floor office of about ten square meters (staff lockers, some tea, sweets, incense and joss paper). The main hall, statues and worship areas were untouched. The temple stayed open in the run-up to the Lunar New Year crowds. The fire bureau credited the reinforced-concrete structure and its own eight-minute arrival for the containment. A conventional smoke alarm sounded as well. So the share attributable to the AI alert is a minute or two of earlier warning, not the whole saved building." } ] } }, "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 fire damaged one office, while the main hall and worship areas remained usable. The temple stayed open. The fire bureau credited the concrete structure and firefighters’ arrival for containment. The AI alert provided earlier warning, but the evidence does not isolate its contribution to the damage 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": "The temple's loss was the contents of one first-floor office of about ten square meters (staff lockers, some tea, sweets, incense and joss paper). The main hall, statues and worship areas were untouched. The temple stayed open in the run-up to the Lunar New Year crowds. The fire bureau credited the reinforced-concrete structure and its own eight-minute arrival for the containment. A conventional smoke alarm sounded as well. So the share attributable to the AI alert is a minute or two of earlier warning, not the whole saved building." } ] } }, "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": "The temple reports that the software detected smoke about one minute before the batteries burst. A conventional smoke alarm also sounded. The AI alert’s separate effect on the damage is unmeasured." } }, "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": "Warning time, area damaged, injuries, and continued use of the temple." } }, "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 temple’s account supplies the alert chronology. A residential smoke alarm also sounded. The fire service attributed containment to the building’s concrete structure and the firefighters’ arrival, without identifying a separate contribution from AI.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2026-02-04-4944" } } }, "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": "2026-08-25", "note": "In August 2026, after the Zhinan Temple fire in Taipei, UDN reported Wude Temple still relying on its AI alarm system combined with the cameras, recounting the February fire as the case that proved it; no other temple adopting the system is reported.", "quote": "今年2月廟的右側辦公室、倉庫，也在清晨沒人時發生火警，幸好廟方全面改裝「AI警報系統」結合監視器", "url": "https://udn.com/news/story/7326/9714008" } } }, "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": "A clerk attempted to extinguish the fire. Firefighters controlled it after the temple called the fire service." }, "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-0155" } }, "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": "Beigang Wude Temple and the people who used it." }, "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": "Beigang Wude Temple and the people who used it." } }, "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": "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": "At about 05:30 on 5 February 2026 the AI門神 (AI door god) system flagged smoke in a first-floor office and machine room, empty after hours. The system is run by 立達軟體 (LEADERG Inc.) over the closed-circuit television (CCTV) of Beigang Wude Temple, Yunlin County, Taiwan. It messaged the front desk, the general manager and chairman 林安樂, in Japan, by LINE. The night-duty clerk reached the room with an extinguisher within a minute. At 05:33 overheated lithium batteries beside a tool charger burst and flames spread. Staff called 119 at 05:36. Yunlin County Fire Bureau crews arrived at 05:44, controlled it at 06:10 and put it out at 06:14. Ten square meters burned. Nobody was hurt. The main hall and statues were untouched.", "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": "Staff and firefighters contained the temple fire to about 10 m². No one was injured, and the main hall remained intact." } }, "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 temple's loss was the contents of one first-floor office of about ten square meters (staff lockers, some tea, sweets, incense and joss paper). The main hall, statues and worship areas were untouched. The temple stayed open in the run-up to the Lunar New Year crowds. The fire bureau credited the reinforced-concrete structure and its own eight-minute arrival for the containment. A conventional smoke alarm sounded as well. So the share attributable to the AI alert is a minute or two of earlier warning, not the whole saved building." } ] } }, "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": "AI門神 (“AI door god: temple AI fire-and-theft prevention system”) was built to run AI image recognition over a temple's existing CCTV. It was built to detect excess smoke, flame and suspicious behavior, and to alert staff by SMS, LINE or email. CTS reported in December 2024 that Wude Temple's door-god system would sound an alarm at once on abnormal smoke. Flagging smoke before a fire 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": "Beigang, Taiwan", "developer_country": "Taiwan", "deployer_country": "Taiwan" } }, "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": "AI門神 (“AI door god: temple AI fire-and-theft prevention system”) was built to run AI image recognition over a temple's existing CCTV. It was built to detect excess smoke, flame and suspicious behavior, and to alert staff by SMS, LINE or email. CTS reported in December 2024 that Wude Temple's door-god system would sound an alarm at once on abnormal smoke. Flagging smoke before a fire 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": "2026-02-05", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a smoke alert at 05:30, a minute before the batteries burst, relayed over LINE to the front desk, general manager and chairman) is documented by the temple's own timeline as reported by TVBS, UDN, NOWnews, 觀傳媒 and CTS, and the outcome by the Yunlin County Fire Bureau's figures, but the counterfactual is not shown: UDN reports that a residential smoke alarm (住警器) sounded as well, a clerk was on night duty in any case, and the Third Brigade commander 陳岳謄 credited the RC structure and the crews' quick arrival without mentioning the AI. The Monitor's date of 4 February is the UTC date; every source puts the fire at about 05:30 on 5 February 2026 Taiwan time (21:30 UTC on 4 February), so the date observed is 5 February. Times differ between the temple's log (fire controlled 05:51) and the fire bureau (controlled 06:10, extinguished 06:14); the record uses the bureau's. The cause (a tool charger and lithium batteries) is the chairman's account; UDN's first report says the bureau was still investigating and the secretary general 蘇建華 told TVBS he suspected wiring. The vendor is named by the chairman as 立達軟體; its identity as LEADERG Inc. and the system's design (AI image recognition over existing CCTV on Advantech hardware, alerts by SMS, LINE or email, deployed at Wude Temple) come from Advantech's April 2025 case study, which is partner-authored and used only as an additional source. The Monitor lists China Times, which could not be read (Cloudflare challenge directly and through a text proxy; web.archive.org is blocked for this tool); its four yam.com links are syndications of 觀傳媒 (two), 台灣好報 and 創新聞. The CTS broadcast report of 5 February and CTS's December 2024 piece on the door-god system were found through Brave, as WebSearch was exhausted; DuckDuckGo and Mojeek returned nothing. The chairman's Facebook post was not read; it is quoted by UDN, NOWnews and NextApple. No Yunlin County Fire Bureau release was found on its own site; its statements are quoted by 觀傳媒, 台灣好報 and 創新聞, and CTS quotes the Beigang sub-station chief 陳思淵. The Monitor's country field is “Chinese Taipei”; the record says Taiwan, as every source does. Raw HTML of every source read is kept in src_4944.", "source": "https://oecd.ai/en/incidents/2026-02-04-4944", "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://n.yam.com/Article/20260205197916; https://udn.com/news/story/7326/9714008", "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.leaderg.com/tw/contact_us; https://zh.wikipedia.org/wiki/北港武德宮", "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 camera-analytics system (AI門神) already watching the office over the temple's existing CCTV. It was set to alert on small amounts of smoke and to message a named chain of people (front desk, general manager, chairman) over LINE.", "A staffed night watch. The temple has kept someone on duty every night since it moved to the site in 1980 (民國69年). So the alert had a clerk a minute away with an extinguisher.", "A slow start to the fire. A tool charger and lithium batteries smoked before the batteries burst at 05:33, which gave the alert a minute's lead.", "A fire brigade eight minutes away and a reinforced-concrete annex that held the fire to one room, as the Third Brigade commander said." ], "must_hold": [ "The alert reaches a person who can act within minutes. At an unstaffed site the lead time is worth little.", "The camera covers the rooms where fires start (here an office and machine room with chargers, beside stored joss paper), not only the shrine.", "The AI alert complements conventional detectors rather than replacing them. A residential smoke alarm sounded here as well." ], "echoes": [ { "setting": "OECD AIM 2023-09-12-f6ac: ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna, human operators verify it and Cal Fire holds the fire (camera smoke detection with a human in the loop and a brigade response).", "outcome": "unknown" }, { "setting": "OECD AIM 2025-09-13-b5dc: Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner (Taiwanese camera smoke analytics in a different use).", "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 camera-analytics system (AI門神) already watching the office over the temple's existing CCTV. It was set to alert on small amounts of smoke and to message a named chain of people (front desk, general manager, chairman) over LINE.", "A staffed night watch. The temple has kept someone on duty every night since it moved to the site in 1980 (民國69年). So the alert had a clerk a minute away with an extinguisher.", "A slow start to the fire. A tool charger and lithium batteries smoked before the batteries burst at 05:33, which gave the alert a minute's lead.", "A fire brigade eight minutes away and a reinforced-concrete annex that held the fire to one room, as the Third Brigade commander said." ], "must_hold": [ "The alert reaches a person who can act within minutes. At an unstaffed site the lead time is worth little.", "The camera covers the rooms where fires start (here an office and machine room with chargers, beside stored joss paper), not only the shrine.", "The AI alert complements conventional detectors rather than replacing them. A residential smoke alarm sounded here as well." ], "echoes": [ { "setting": "OECD AIM 2023-09-12-f6ac: ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna, human operators verify it and Cal Fire holds the fire (camera smoke detection with a human in the loop and a brigade response).", "outcome": "unknown" }, { "setting": "OECD AIM 2025-09-13-b5dc: Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner (Taiwanese camera smoke analytics in a different use).", "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": "Cameras need to cover rooms where a fire could start. Alerts need to reach staff who can respond within minutes. Conventional smoke alarms remain necessary." } }, "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 protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (a smoke alert at 05:30, a minute before the batteries burst, relayed over LINE to the front desk, general manager and chairman) is documented by the temple's own timeline as reported by TVBS, UDN, NOWnews, 觀傳媒 and CTS, and the outcome by the Yunlin County Fire Bureau's figures, but the counterfactual is not shown: UDN reports that a residential smoke alarm (住警器) sounded as well, a clerk was on night duty in any case, and the Third Brigade commander 陳岳謄 credited the RC structure and the crews' quick arrival without mentioning the AI. The Monitor's date of 4 February is the UTC date; every source puts the fire at about 05:30 on 5 February 2026 Taiwan time (21:30 UTC on 4 February), so the date observed is 5 February. Times differ between the temple's log (fire controlled 05:51) and the fire bureau (controlled 06:10, extinguished 06:14); the record uses the bureau's. The cause (a tool charger and lithium batteries) is the chairman's account; UDN's first report says the bureau was still investigating and the secretary general 蘇建華 told TVBS he suspected wiring. The vendor is named by the chairman as 立達軟體; its identity as LEADERG Inc. and the system's design (AI image recognition over existing CCTV on Advantech hardware, alerts by SMS, LINE or email, deployed at Wude Temple) come from Advantech's April 2025 case study, which is partner-authored and used only as an additional source. The Monitor lists China Times, which could not be read (Cloudflare challenge directly and through a text proxy; web.archive.org is blocked for this tool); its four yam.com links are syndications of 觀傳媒 (two), 台灣好報 and 創新聞. The CTS broadcast report of 5 February and CTS's December 2024 piece on the door-god system were found through Brave, as WebSearch was exhausted; DuckDuckGo and Mojeek returned nothing. The chairman's Facebook post was not read; it is quoted by UDN, NOWnews and NextApple. No Yunlin County Fire Bureau release was found on its own site; its statements are quoted by 觀傳媒, 台灣好報 and 創新聞, and CTS quotes the Beigang sub-station chief 陳思淵. The Monitor's country field is “Chinese Taipei”; the record says Taiwan, as every source does. Raw HTML of every source read is kept in src_4944.", "source": "https://oecd.ai/en/incidents/2026-02-04-4944", "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://n.yam.com/Article/20260205197916; https://udn.com/news/story/7326/9714008", "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.leaderg.com/tw/contact_us; https://zh.wikipedia.org/wiki/北港武德宮", "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": "Fire safety (religious site)", "segment": "Temple and cultural-asset protection: AI video analytics on existing CCTV" } }, "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": "蔡維斌 / 聯合新聞網 (2026). 「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷. UDN, 5 February 2026.", "title": "「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷", "url": "https://udn.com/news/story/7320/9310088", "date": "2026-02-05", "written_by": "independent", "quote": "幸廟內設有AI警報系統及住警器，馬上發出警報聲響" }, { "kind": "news report", "citation": "蔡維斌 / 聯合新聞網 (2026). 強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場. UDN, 5 February 2026.", "title": "強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場", "url": "https://udn.com/news/story/7320/9310526", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢發生前一分鐘系統即已示警煙霧產生" }, { "kind": "news report", "citation": "吳淑萍 / TVBS新聞網 (2026). 北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟. TVBS, 5 February 2026.", "title": "北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟", "url": "https://news.tvbs.com.tw/local/3120819", "date": "2026-02-05", "written_by": "independent", "quote": "5點30分系統偵測到煙霧，5點32分人工察看確認狀況" }, { "kind": "news report", "citation": "蘇榮泉 / 觀傳媒 (2026). （有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "（有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控", "url": "https://n.yam.com/Article/20260205444546", "date": "2026-02-05", "written_by": "independent", "quote": "消防人員於6時10分成功控制火勢，並於6時14分完全撲滅" }, { "kind": "news report", "citation": "洪佳伶 / 觀傳媒 (2026). AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢", "url": "https://n.yam.com/Article/20260205197916", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢轉劇前一分鐘便偵測到煙霧，並依序通知櫃檯、總幹事" }, { "kind": "news report", "citation": "華視新聞 / CTS (2026). 「AI門神」立功！　武德宮火警偵測回報　快速控制火勢. CTS News, 5 February 2026.", "title": "「AI門神」立功！　武德宮火警偵測回報　快速控制火勢", "url": "https://news.cts.com.tw/cts/general/202602/202602052559104.html", "date": "2026-02-05", "written_by": "independent", "quote": "好險有AI門神，幫我們守護住，所以我們可以趕快第一時間" }, { "kind": "news report", "citation": "鄧庭婷 / NOWnews今日新聞 (2026). 北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸. NOWnews, 5 February 2026.", "title": "北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸", "url": "https://www.nownews.com/news/6783602", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火起前的關鍵一分鐘發出示警" }, { "kind": "news report", "citation": "莊曜聰 / 壹蘋新聞網 (2026). 北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」. NextApple, 5 February 2026.", "title": "北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」", "url": "https://news.nextapple.com/local/20260205/E631BE3355F1C46E511BAEBE0A5D0D61", "date": "2026-02-05", "written_by": "independent", "quote": "還有「門神系統」可以示警防災，這次果然派上用場" }, { "kind": "news report", "citation": "蘇峯毅 / 台灣好報 (2026). 清晨火警驚魂　北港武德宮即時搶救守住信仰中心. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "清晨火警驚魂　北港武德宮即時搶救守住信仰中心", "url": "https://n.yam.com/Article/20260205590605", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火勢形成前即已示警，加上長年夜間留守制度" }, { "kind": "news report", "citation": "華視新聞 / CTS (2024). AI進駐宮廟　統計人流.預防火災還能「解籤詩」. Syndicated on Yahoo奇摩新聞, 23 December 2024.", "title": "AI進駐宮廟　統計人流.預防火災還能「解籤詩」", "url": "https://tw.news.yahoo.com/ai%E9%80%B2%E9%A7%90%E5%AE%AE%E5%BB%9F-%E7%B5%B1%E8%A8%88%E4%BA%BA%E6%B5%81-%E9%A0%90%E9%98%B2%E7%81%AB%E7%81%BD%E9%82%84%E8%83%BD-%E8%A7%A3%E7%B1%A4%E8%A9%A9-121500289.html", "date": "2024-12-23", "written_by": "independent", "quote": "門神系統，只要偵測到煙霧異常，有火災的疑慮，會立刻發出警報" }, { "kind": "press release", "citation": "Advantech (2025). 立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護. Advantech case study, 15 April 2025.", "title": "立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護", "url": "https://www.advantech.com/en-us/resources/case-study/%E7%AB%8B%E9%81%94%E8%BB%9F%E9%AB%94%E6%94%9C%E6%89%8B%E7%A0%94%E8%8F%AFai%E9%96%80%E7%A5%9E%EF%BC%9A%E5%89%B5%E6%96%B0%E6%8A%80%E8%A1%93%E5%8A%A9%E5%8A%9B%E5%AF%BA%E5%BB%9F%E6%96%87%E5%8C%96%E8%B3%87%E7%94%A2%E4%BF%9D%E8%AD%B7", "date": "2025-04-15", "written_by": "makers", "quote": "偵測特定區域內是否有過量的煙霧、火焰及可疑行為" }, { "kind": "news report", "citation": "聯合新聞網 UDN (2026). 指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」. udn.com, 25 August 2026.", "title": "指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」", "url": "https://udn.com/news/story/7326/9714008", "date": "2026-08-25", "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-0155-source-0001", "AIBD-0155-source-0002", "AIBD-0155-source-0003", "AIBD-0155-source-0004", "AIBD-0155-source-0005", "AIBD-0155-source-0006", "AIBD-0155-source-0007", "AIBD-0155-source-0008", "AIBD-0155-source-0009", "AIBD-0155-source-0010", "AIBD-0155-source-0011", "AIBD-0155-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": "news report", "citation": "蔡維斌 / 聯合新聞網 (2026). 「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷. UDN, 5 February 2026.", "title": "「財神祖廟」北港武德宮火警！AI警報響起快速撲滅…人員幸運無傷", "url": "https://udn.com/news/story/7320/9310088", "date": "2026-02-05", "written_by": "independent", "quote": "幸廟內設有AI警報系統及住警器，馬上發出警報聲響" }, { "kind": "news report", "citation": "蔡維斌 / 聯合新聞網 (2026). 強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場. UDN, 5 February 2026.", "title": "強大AI！人在日本的武德宮主委被AI警報嚇醒…跨海還原火警現場", "url": "https://udn.com/news/story/7320/9310526", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢發生前一分鐘系統即已示警煙霧產生" }, { "kind": "news report", "citation": "吳淑萍 / TVBS新聞網 (2026). 北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟. TVBS, 5 February 2026.", "title": "北港武德宮清晨竄火！鋰電池充電爆炸　「AI門神」示警救廟", "url": "https://news.tvbs.com.tw/local/3120819", "date": "2026-02-05", "written_by": "independent", "quote": "5點30分系統偵測到煙霧，5點32分人工察看確認狀況" }, { "kind": "news report", "citation": "蘇榮泉 / 觀傳媒 (2026). （有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "（有影片）／AI火警系統即時示警　北港武德宮火勢10分鐘內受控", "url": "https://n.yam.com/Article/20260205444546", "date": "2026-02-05", "written_by": "independent", "quote": "消防人員於6時10分成功控制火勢，並於6時14分完全撲滅" }, { "kind": "news report", "citation": "洪佳伶 / 觀傳媒 (2026). AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "AI門神立大功！北港武德宮幹部辦公室火警 主委跨海監控、10分鐘內控制火勢", "url": "https://n.yam.com/Article/20260205197916", "date": "2026-02-05", "written_by": "independent", "quote": "在火勢轉劇前一分鐘便偵測到煙霧，並依序通知櫃檯、總幹事" }, { "kind": "news report", "citation": "華視新聞 / CTS (2026). 「AI門神」立功！　武德宮火警偵測回報　快速控制火勢. CTS News, 5 February 2026.", "title": "「AI門神」立功！　武德宮火警偵測回報　快速控制火勢", "url": "https://news.cts.com.tw/cts/general/202602/202602052559104.html", "date": "2026-02-05", "written_by": "independent", "quote": "好險有AI門神，幫我們守護住，所以我們可以趕快第一時間" }, { "kind": "news report", "citation": "鄧庭婷 / NOWnews今日新聞 (2026). 北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸. NOWnews, 5 February 2026.", "title": "北港武德宮火警！「1科技」即時通報立大功　主委：不幸中的大幸", "url": "https://www.nownews.com/news/6783602", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火起前的關鍵一分鐘發出示警" }, { "kind": "news report", "citation": "莊曜聰 / 壹蘋新聞網 (2026). 北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」. NextApple, 5 February 2026.", "title": "北港武德宮火警損失輕微靠「兩功臣」　主委林安樂赴日收警報嘆「沒放假的命」", "url": "https://news.nextapple.com/local/20260205/E631BE3355F1C46E511BAEBE0A5D0D61", "date": "2026-02-05", "written_by": "independent", "quote": "還有「門神系統」可以示警防災，這次果然派上用場" }, { "kind": "news report", "citation": "蘇峯毅 / 台灣好報 (2026). 清晨火警驚魂　北港武德宮即時搶救守住信仰中心. Syndicated on yam 蕃新聞, 5 February 2026.", "title": "清晨火警驚魂　北港武德宮即時搶救守住信仰中心", "url": "https://n.yam.com/Article/20260205590605", "date": "2026-02-05", "written_by": "independent", "quote": "AI系統在火勢形成前即已示警，加上長年夜間留守制度" }, { "kind": "news report", "citation": "華視新聞 / CTS (2024). AI進駐宮廟　統計人流.預防火災還能「解籤詩」. Syndicated on Yahoo奇摩新聞, 23 December 2024.", "title": "AI進駐宮廟　統計人流.預防火災還能「解籤詩」", "url": "https://tw.news.yahoo.com/ai%E9%80%B2%E9%A7%90%E5%AE%AE%E5%BB%9F-%E7%B5%B1%E8%A8%88%E4%BA%BA%E6%B5%81-%E9%A0%90%E9%98%B2%E7%81%AB%E7%81%BD%E9%82%84%E8%83%BD-%E8%A7%A3%E7%B1%A4%E8%A9%A9-121500289.html", "date": "2024-12-23", "written_by": "independent", "quote": "門神系統，只要偵測到煙霧異常，有火災的疑慮，會立刻發出警報" }, { "kind": "press release", "citation": "Advantech (2025). 立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護. Advantech case study, 15 April 2025.", "title": "立達軟體攜手研華AI門神：創新技術助力寺廟文化資產保護", "url": "https://www.advantech.com/en-us/resources/case-study/%E7%AB%8B%E9%81%94%E8%BB%9F%E9%AB%94%E6%94%9C%E6%89%8B%E7%A0%94%E8%8F%AFai%E9%96%80%E7%A5%9E%EF%BC%9A%E5%89%B5%E6%96%B0%E6%8A%80%E8%A1%93%E5%8A%A9%E5%8A%9B%E5%AF%BA%E5%BB%9F%E6%96%87%E5%8C%96%E8%B3%87%E7%94%A2%E4%BF%9D%E8%AD%B7", "date": "2025-04-15", "written_by": "makers", "quote": "偵測特定區域內是否有過量的煙霧、火焰及可疑行為" }, { "kind": "news report", "citation": "聯合新聞網 UDN (2026). 指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」. udn.com, 25 August 2026.", "title": "指南宮大火啟示錄！雲林兩大廟分享防火經驗「要神也要人」", "url": "https://udn.com/news/story/7326/9714008", "date": "2026-08-25", "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 camera analytics flagged smoke in an empty temple office a minute before a battery fire; held to 10 m², no injuries, main hall intact.", "account": "At about 05:30 on 5 February 2026 the AI門神 (AI door god) system flagged smoke in a first-floor office and machine room, empty after hours. The system is run by 立達軟體 (LEADERG Inc.) over the closed-circuit television (CCTV) of Beigang Wude Temple, Yunlin County, Taiwan. It messaged the front desk, the general manager and chairman 林安樂, in Japan, by LINE. The night-duty clerk reached the room with an extinguisher within a minute. At 05:33 overheated lithium batteries beside a tool charger burst and flames spread. Staff called 119 at 05:36. Yunlin County Fire Bureau crews arrived at 05:44, controlled it at 06:10 and put it out at 06:14. Ten square meters burned. Nobody was hurt. The main hall and statues were untouched." } }, "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 detected smoke in camera images and alerted temple staff." }, "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 image-recognition software ran on an Advantech IPC-730 computer with a SKY-QUAD-5000A-32 accelerator card. Advantech reports processing about one frame every five seconds." }, "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 software analyzed CCTV images and sent smoke alerts to temple staff through LINE." }, "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. Advantech's case study says the image-recognition algorithms were developed with a large language model as the basis and run on an Advantech IPC-730 with a SKY-QUAD-5000A-32 accelerator card at about five seconds per frame." } }, "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門神：宮廟AI防火防盜系統 (AI Door God: temple AI fire-and-theft prevention system), AI image recognition over the temple's existing CCTV with alerts by SMS, LINE and email" } }, "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": "立達軟體科技股份有限公司 (LEADERG Inc.), Taiwan" } }, "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": "LEADERG's “AI door god” camera analytics at Beigang Wude Temple flags smoke in an empty office a minute before charging lithium batteries burst; the night clerk and Yunlin firefighters hold the 5 February 2026 fire to ten square meters, nobody hurt, main hall untouched", "headline": "LEADERG's AI door god flags smoke; temple fire held to 10 m²" } }, "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": [ "d9e50b5220d086e0c9bd46b934073b730fafc534e423027f5f3acbdea2c79fa3" ], "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 05:30 on 5 February 2026 the AI門神 (AI door god) system flagged smoke in a first-floor office and machine room, empty after hours. The system is run by 立達軟體 (LEADERG Inc.) over the closed-circuit television (CCTV) of Beigang Wude Temple, Yunlin County, Taiwan. It messaged the front desk, the general manager and chairman 林安樂, in Japan, by LINE. The night-duty clerk reached the room with an extinguisher within a minute. At 05:33 overheated lithium batteries beside a tool charger burst and flames spread. Staff called 119 at 05:36. Yunlin County Fire Bureau crews arrived at 05:44, controlled it at 06:10 and put it out at 06:14. Ten square meters burned. Nobody was hurt. The main hall and statues were untouched.", "replication": { "produced_by": [ "A camera-analytics system (AI門神) already watching the office over the temple's existing CCTV. It was set to alert on small amounts of smoke and to message a named chain of people (front desk, general manager, chairman) over LINE.", "A staffed night watch. The temple has kept someone on duty every night since it moved to the site in 1980 (民國69年). So the alert had a clerk a minute away with an extinguisher.", "A slow start to the fire. A tool charger and lithium batteries smoked before the batteries burst at 05:33, which gave the alert a minute's lead.", "A fire brigade eight minutes away and a reinforced-concrete annex that held the fire to one room, as the Third Brigade commander said." ], "must_hold": [ "The alert reaches a person who can act within minutes. At an unstaffed site the lead time is worth little.", "The camera covers the rooms where fires start (here an office and machine room with chargers, beside stored joss paper), not only the shrine.", "The AI alert complements conventional detectors rather than replacing them. A residential smoke alarm sounded here as well." ], "echoes": [ { "setting": "OECD AIM 2023-09-12-f6ac: ALERTCalifornia's AI flags a 3 am smoke column near Mount Laguna, human operators verify it and Cal Fire holds the fire (camera smoke detection with a human in the loop and a brigade response).", "outcome": "unknown" }, { "setting": "OECD AIM 2025-09-13-b5dc: Taichung EPB's AI smoke-recognition system catches a serial roadside waste burner (Taiwanese camera smoke analytics in a different use).", "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": "AI門神 detected smoke through cameras that already covered the temple and alerted staff through LINE. The night clerk reached the room with an extinguisher. Firefighters arrived eight minutes later." } } }, "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-0185", "case_url": "records/AIBD-0185.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. OpenAI had no role beyond providing the consumer product." }, "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." } ], "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": [] }, "paired_incident": null, "headline": "ChatGPT flags fake adviser; victim reports Rs 67.10 lakh fraud", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "Reports as of January 2026 say a case was registered and the money trail is being traced; no arrest or recovery was found.", "quote": null, "url": null }, "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 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.", "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-0185", "catalog_version": "1.0.0", "source_fingerprint": "f6c0a087d5b24db8a8b779d822e10293d456ab2962ad17de12e5fcfedd1835c7", "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-0185-source-0001", "AIBD-0185-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": "OpenAI", "deployer": "The victim, using the consumer app himself" } }, "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 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." } } }, "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": "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." } } }, "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 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", "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "reason": "The police registered a case. No organizational 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. 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 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", "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "reason": "The police registered a case. No organizational 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": "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", "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "reason": "The police registered a case. No organizational 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": "unknown", "note": "Reports as of January 2026 say a case was registered and the money trail is being traced; no arrest or recovery 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-0185" } }, "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 loss had occurred; the good is recognition and reporting of the fraud." } }, "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 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.", "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 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", "reason": "No outcome beyond the single complaint is documented." }, { "group": "organizational", "reason": "The police registered a case. No organizational 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": "General-purpose conversational assistant that answers users' questions." } }, "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": "Bengaluru, India", "developer_country": "United States" } }, "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": "General-purpose conversational assistant that answers users' questions." } }, "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": "2026-01-06", "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 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 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": [ "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 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": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Investment fraud", "segment": "Pre-IPO share scam" } }, "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 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[].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-0153", "case_url": "records/AIBD-0153.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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. 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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-0148", "case_url": "records/AIBD-0148.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "inventory shrinkage under the store's previous staffed-only opening hours", "url": "https://profitline.hu/ujabb-hibrid-okosboltok-kialakitasat-tervezi-a-coop-szeged-zrt-479176" }, { "label": "After", "value": "nem nőtt meg a leltárhiány" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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-0136", "case_url": "records/AIBD-0136.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "ChatGPT's contribution, on her account, was an unambiguous instruction to go immediately when she was unsure. The assessment and any treatment were the ER's. Whether earlier arrival changed her outcome is unknown. Everything traces to her TikTok, relayed by Reach plc tabloids and Indian lifestyle desks. Hindustan Times states that it “has not independently verified the claims”." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0136", "title": "ChatGPT tells a Wolfforth, Texas woman with a week-old spider bite, days of vomiting and a numb arm to go to the ER; doctors say she belongs there", "summary": "ChatGPT sent Holli of Wolfforth, Texas to the ER for a worsening spider bite on 27 August 2025; doctors agreed and said the tissue was dying.", "date_observed": "2025-08-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Holli posts on TikTok as @hair.queen.holli from Wolfforth, Texas. She said a spider had bitten her “a little over a week ago”. The bite was getting worse. She had vomited for days and could not keep water down. She woke with a numb arm. She asked ChatGPT whether she should go to the emergency room (ER). It answered that her symptoms were “a ton of red flags” and that she should go immediately. At the ER, she said, staff told her “Yeah, you should be here”. They said the skin tissue was dying or dead. Her video, recorded at the ER, was posted on 27 August 2025. The hospital, diagnosis and treatment were not reported. No clinician has spoken.", "category": "protective response", "unexpected": false, "purpose": "ChatGPT is a general-purpose conversational assistant. Answering a user's question, here whether to go to the emergency room, is its core function. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted it. The system gave triage advice rather than a diagnosis.", "system": "ChatGPT", "model_version": null, "developer": "OpenAI", "deployer": null, "location": "Wolfforth, United States", "domain": "Healthcare (emergency self-triage)", "segment": "Consumer use of a general chatbot for triage advice", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-09-02-0516" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "Holli got emergency care after the chatbot overrode her hesitation. She says doctors told her the bite's tissue was dying or dead. The diagnosis, treatment and outcome were not reported. The size of the benefit is therefore unknown." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT's contribution, on her account, was an unambiguous instruction to go immediately when she was unsure. The assessment and any treatment were the ER's. Whether earlier arrival changed her outcome is unknown. Everything traces to her TikTok, relayed by Reach plc tabloids and Indian lifestyle desks. Hindustan Times states that it “has not independently verified the claims”." }, "evidence": [ { "kind": "other", "citation": "Holli (@hair.queen.holli) (2025). TikTok video 7543332854705540365, recorded at the emergency room. TikTok, 27 August 2025.", "title": "The Dr said the skin is dying or already dead, I can't really remember… so it is an emergency", "url": "https://www.tiktok.com/@hair.queen.holli/video/7543332854705540365", "date": "2025-08-27", "written_by": "beneficiary", "quote": "The Dr said the skin is dying or already dead, I can’t really remember… so it is an emergency" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Express (2025). Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E. Express.co.uk, 2 September 2025.", "title": "Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E", "url": "https://www.express.co.uk/life-style/health/2103355/woman-bit-spider-asks-ai-chatbot-advice-told-me-go-e", "date": "2025-09-02", "written_by": "independent", "quote": "They looked at it and said, 'Yeah, you should be here'. So now here I am" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Star (2025). Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job. Daily Star, 2 September 2025.", "title": "Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job", "url": "https://www.dailystar.co.uk/real-life/woman-asks-chat-gpt-whether-she-should-go-to-ae-35834743", "date": "2025-09-02", "written_by": "independent", "quote": "your symptoms are a ton of red flags, you need to go immediately" }, { "kind": "news report", "citation": "Akanksha Agnihotri / Hindustan Times (2025). Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'. Hindustan Times, 3 September 2025.", "title": "Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'", "url": "https://www.hindustantimes.com/lifestyle/health/woman-credits-chatgpt-for-saving-her-life-after-spider-bite-left-her-arm-numb-it-told-me-to-immediately-go-to-the-er-101756886357629.html", "date": "2025-09-03", "written_by": "independent", "quote": "This report is based on user-generated content from social media. HT.com has not independently verified the claims and does not endorse them." }, { "kind": "news report", "citation": "ET Online / The Economic Times (2025). Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life. The Economic Times, 3 September 2025.", "title": "Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life", "url": "https://economictimes.indiatimes.com/magazines/panache/woman-bitten-by-spider-turns-to-chatgpt-for-help-and-doctors-say-it-saved-her-life/articleshow/123680161.cms", "date": "2025-09-03", "written_by": "independent", "quote": "Our Services are not intended for use in the diagnosis or treatment of any health condition" } ], "replication": { "produced_by": [ "Escalating symptoms (days of vomiting, inability to keep water down, new numbness in the arm) in a person hesitating to seek care.", "A yes/no triage question put to a chatbot that answered without hedging: “you need to go immediately”.", "An emergency department within reach that could assess the bite the same day." ], "must_hold": [ "The user acts on the advice promptly rather than waiting for a second opinion.", "The advice is to escalate to clinicians, who then examine and diagnose. 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"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": "2025-09-02-0516" } } }, "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 report on Holli's diagnosis, treatment or recovery after the 27 August 2025 video was found; the press pieces of 2-3 September 2025 only retell the video." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ 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"Improved Physical Health" } }, "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": "Holli posts on TikTok as @hair.queen.holli from Wolfforth, Texas. 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No clinician has spoken.", "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": "Holli got emergency care after the chatbot overrode her hesitation. She says doctors told her the bite's tissue was dying or dead. The diagnosis, treatment and outcome were not reported. The size of the benefit is therefore unknown." } ] } }, "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. Answering a user's question, here whether to go to the emergency room, is its core function. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted it. The system gave triage advice rather than a diagnosis." } }, "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": "Wolfforth, United States", "developer_country": "United States" } }, "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": "ChatGPT is a general-purpose conversational assistant. Answering a user's question, here whether to go to the emergency room, is its core function. OpenAI told the Mirror that its services “are not intended for use in the diagnosis or treatment of any health condition”. The Economic Times quoted it. The system gave triage advice rather than a diagnosis." } }, "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": "2025-08-27", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (an instruction to go to the ER, which doctors endorsed) is documented only by Holli's own video, and no diagnosis, treatment or outcome has been reported, so no specific prevented harm is evidenced; the Monitor's “treated her” is not in any source. The TikTok was dated from its page metadata (createTime 1756319056, 27 August 2025) and still existed on 20 October 2026. Reach plc ran the same text in the Express and the Daily Star under one byline; The Economic Times credits the Mirror. No local Texas report, clinician or hospital statement was found.", "source": "https://oecd.ai/en/incidents/2025-09-02-0516", "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": "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, 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": [ "Escalating symptoms (days of vomiting, inability to keep water down, new numbness in the arm) in a person hesitating to seek care.", "A yes/no triage question put to a chatbot that answered without hedging: “you need to go immediately”.", "An emergency department within reach that could assess the bite the same day." ], "must_hold": [ "The user acts on the advice promptly rather than waiting for a second opinion.", "The advice is to escalate to clinicians, who then examine and diagnose. The chatbot made no diagnosis.", "Such cases are self-published. No clinician, hospital or document has confirmed this one. The record stands or falls with her 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 the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: the protective action (an instruction to go to the ER, which doctors endorsed) is documented only by Holli's own video, and no diagnosis, treatment or outcome has been reported, so no specific prevented harm is evidenced; the Monitor's “treated her” is not in any source. The TikTok was dated from its page metadata (createTime 1756319056, 27 August 2025) and still existed on 20 October 2026. Reach plc ran the same text in the Express and the Daily Star under one byline; The Economic Times credits the Mirror. No local Texas report, clinician or hospital statement was found.", "source": "https://oecd.ai/en/incidents/2025-09-02-0516", "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": "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, 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": "Healthcare (emergency self-triage)", "segment": "Consumer use of a general chatbot for triage advice" } }, "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": "other", "citation": "Holli (@hair.queen.holli) (2025). TikTok video 7543332854705540365, recorded at the emergency room. TikTok, 27 August 2025.", "title": "The Dr said the skin is dying or already dead, I can't really remember… so it is an emergency", "url": "https://www.tiktok.com/@hair.queen.holli/video/7543332854705540365", "date": "2025-08-27", "written_by": "beneficiary", "quote": "The Dr said the skin is dying or already dead, I can’t really remember… so it is an emergency" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Express (2025). Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E. Express.co.uk, 2 September 2025.", "title": "Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E", "url": "https://www.express.co.uk/life-style/health/2103355/woman-bit-spider-asks-ai-chatbot-advice-told-me-go-e", "date": "2025-09-02", "written_by": "independent", "quote": "They looked at it and said, 'Yeah, you should be here'. So now here I am" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Star (2025). Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job. Daily Star, 2 September 2025.", "title": "Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job", "url": "https://www.dailystar.co.uk/real-life/woman-asks-chat-gpt-whether-she-should-go-to-ae-35834743", "date": "2025-09-02", "written_by": "independent", "quote": "your symptoms are a ton of red flags, you need to go immediately" }, { "kind": "news report", "citation": "Akanksha Agnihotri / Hindustan Times (2025). Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'. Hindustan Times, 3 September 2025.", "title": "Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'", "url": "https://www.hindustantimes.com/lifestyle/health/woman-credits-chatgpt-for-saving-her-life-after-spider-bite-left-her-arm-numb-it-told-me-to-immediately-go-to-the-er-101756886357629.html", "date": "2025-09-03", "written_by": "independent", "quote": "This report is based on user-generated content from social media. HT.com has not independently verified the claims and does not endorse them." }, { "kind": "news report", "citation": "ET Online / The Economic Times (2025). Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life. The Economic Times, 3 September 2025.", "title": "Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life", "url": "https://economictimes.indiatimes.com/magazines/panache/woman-bitten-by-spider-turns-to-chatgpt-for-help-and-doctors-say-it-saved-her-life/articleshow/123680161.cms", "date": "2025-09-03", "written_by": "independent", "quote": "Our Services are not intended for use in the diagnosis or treatment of any health condition" } ] } }, "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-0136-source-0001", "AIBD-0136-source-0002", "AIBD-0136-source-0003", "AIBD-0136-source-0004", "AIBD-0136-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": "other", "citation": "Holli (@hair.queen.holli) (2025). TikTok video 7543332854705540365, recorded at the emergency room. TikTok, 27 August 2025.", "title": "The Dr said the skin is dying or already dead, I can't really remember… so it is an emergency", "url": "https://www.tiktok.com/@hair.queen.holli/video/7543332854705540365", "date": "2025-08-27", "written_by": "beneficiary", "quote": "The Dr said the skin is dying or already dead, I can’t really remember… so it is an emergency" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Express (2025). Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E. Express.co.uk, 2 September 2025.", "title": "Woman is 'thankful' after asking Chat GPT for medical advice and is told to go to A&E", "url": "https://www.express.co.uk/life-style/health/2103355/woman-bit-spider-asks-ai-chatbot-advice-told-me-go-e", "date": "2025-09-02", "written_by": "independent", "quote": "They looked at it and said, 'Yeah, you should be here'. So now here I am" }, { "kind": "news report", "citation": "Danielle Kate Wroe / Daily Star (2025). Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job. Daily Star, 2 September 2025.", "title": "Woman asks Chat GPT whether she should go to A&E and doctors say it's a good job", "url": "https://www.dailystar.co.uk/real-life/woman-asks-chat-gpt-whether-she-should-go-to-ae-35834743", "date": "2025-09-02", "written_by": "independent", "quote": "your symptoms are a ton of red flags, you need to go immediately" }, { "kind": "news report", "citation": "Akanksha Agnihotri / Hindustan Times (2025). Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'. Hindustan Times, 3 September 2025.", "title": "Woman credits ChatGPT for saving her life after spider bite left her arm numb: 'It told me to immediately go to the ER'", "url": "https://www.hindustantimes.com/lifestyle/health/woman-credits-chatgpt-for-saving-her-life-after-spider-bite-left-her-arm-numb-it-told-me-to-immediately-go-to-the-er-101756886357629.html", "date": "2025-09-03", "written_by": "independent", "quote": "This report is based on user-generated content from social media. HT.com has not independently verified the claims and does not endorse them." }, { "kind": "news report", "citation": "ET Online / The Economic Times (2025). Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life. The Economic Times, 3 September 2025.", "title": "Woman bitten by spider turns to ChatGPT for help, and doctors say it saved her life", "url": "https://economictimes.indiatimes.com/magazines/panache/woman-bitten-by-spider-turns-to-chatgpt-for-help-and-doctors-say-it-saved-her-life/articleshow/123680161.cms", "date": "2025-09-03", "written_by": "independent", "quote": "Our Services are not intended for use in the diagnosis or treatment of any health condition" } ] } }, "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": "ChatGPT sent Holli of Wolfforth, Texas to the ER for a worsening spider bite on 27 August 2025; doctors agreed and said the tissue was dying.", "account": "Holli posts on TikTok as @hair.queen.holli from Wolfforth, Texas. She said a spider had bitten her “a little over a week ago”. The bite was getting worse. She had vomited for days and could not keep water down. She woke with a numb arm. She asked ChatGPT whether she should go to the emergency room (ER). It answered that her symptoms were “a ton of red flags” and that she should go immediately. At the ER, she said, staff told her “Yeah, you should be here”. They said the skin tissue was dying or dead. Her video, recorded at the ER, was posted on 27 August 2025. The hospital, diagnosis and treatment were not reported. No clinician has spoken." } }, "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 tells a Wolfforth, Texas woman with a week-old spider bite, days of vomiting and a numb arm to go to the ER; doctors say she belongs there", "headline": "ChatGPT sends a woman with a spider bite to the ER; staff agree" } }, "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": [ "947ff4e410c722ad2357a2f6a262ab3f0de11fa3c5c6a57ad117dbc535a7ac7a" ], "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": "Holli posts on TikTok as @hair.queen.holli from Wolfforth, Texas. She said a spider had bitten her “a little over a week ago”. The bite was getting worse. She had vomited for days and could not keep water down. She woke with a numb arm. She asked ChatGPT whether she should go to the emergency room (ER). It answered that her symptoms were “a ton of red flags” and that she should go immediately. At the ER, she said, staff told her “Yeah, you should be here”. They said the skin tissue was dying or dead. Her video, recorded at the ER, was posted on 27 August 2025. The hospital, diagnosis and treatment were not reported. No clinician has spoken.", "replication": { "produced_by": [ "Escalating symptoms (days of vomiting, inability to keep water down, new numbness in the arm) in a person hesitating to seek care.", "A yes/no triage question put to a chatbot that answered without hedging: “you need to go immediately”.", "An emergency department within reach that could assess the bite the same day." ], "must_hold": [ "The user acts on the advice promptly rather than waiting for a second opinion.", "The advice is to escalate to clinicians, who then examine and diagnose. The chatbot made no diagnosis.", "Such cases are self-published. No clinician, hospital or document has confirmed this one. The record stands or falls with her 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": 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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. The claim rests on Google.", "category": "protective response", "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).", "system": "Big Sleep", "model_version": null, "developer": "DeepMind and Google Project Zero", "deployer": "Google (Threat Analysis Group / Big Sleep team)", "location": null, "domain": "Cybersecurity / vulnerability research", "segment": "Technology", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "SQLite fixed the flaw in 3.50.2 (release log entry on aggregate terms exceeding column count) before public exploitation was reported." }, { "group": "societal", "depth": null, "reason": "No source documents an outcome for users beyond the patch. 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. Neither confirms the threat-actor staging." }, "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." } ], "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", "model_version": null, "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", "model_version": null, "outcome": "recurred", "source": "https://daniel.haxx.se/blog/2025/10/10/a-new-breed-of-analyzers/" } ] }, "paired_incident": null, "headline": "Big Sleep may have isolated SQLite zero-day CVE-2025–6965", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2025-08-11", "note": "Big Sleep went on to report a legitimate curl vulnerability on 11 August 2025, later published as CVE-2025-9086, which curl's maintainer confirmed.", "quote": "On August 11, 2025 there was a curl vulnerability reported against curl that would turn out legitimate and it would later be published as CVE-2025-9086. 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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-0147", "case_url": "records/AIBD-0147.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "not being identified (no live facial recognition check)", "url": "https://southwarknews.co.uk/area/camberwell/sex-offender-caught-in-denmark-hill-thanks-to-facial-recognition-cameras/" }, { "label": "Before", "value": "it’s possible if he hadn’t been identified using this technology, he could have gone on to abuse this child" }, { "label": "After", "value": "his face was identified by Live Facial Recognition cameras attached to the back of a van, alerting officers to his presence" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. 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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", "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": { "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": "2025-05-20-368b" } } }, "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": "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" } } }, "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": "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 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. 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" }, "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. 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": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "London, 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", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "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." } }, "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. 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": [ "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 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": [ "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. 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[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0132-source-0001", "AIBD-0132-source-0002", "AIBD-0132-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": "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. 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-0349", "case_url": "records/AIBD-0349.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "local seismic networks (AFAD stations)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13558587/fullTextXML" }, { "label": "After", "value": "AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users." }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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. 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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. 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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-0128", "case_url": "records/AIBD-0128.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The app did the detecting. Staff scanned the admit card's QR code and captured the woman's photo. It flagged a mismatch with the photo on record (New Indian Express). It “revealed that she was not the genuine candidate” (The Hindu). The Hindu and the Times of India both report the forged card's contents. It carried her own photograph with the genuine candidate's hall ticket number and QR code. Comparing the card with her face would therefore not have exposed her. The flag depended on the app's match against the registered photograph. Staff then acted, but she fled before the principal was told. No arrest or sanction is attributable to the system. KEA's probe and the promised legal action were still pending in every report." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0128", "title": "KEA's QR-code facial recognition app exposes a woman trying to enter a Bengaluru CET hall on another candidate's forged admit card", "summary": "KEA's facial recognition app flagged a woman with a forged CET admit card in Bengaluru on 17 April 2025; she fled before writing the paper.", "date_observed": "2025-04-17", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On Thursday 17 April 2025 a woman calling herself Tuba Fathima Jameel tried to enter the CET-2025 mathematics paper. The center was Silver Valley Public PU College in Malleswaram, Bengaluru. She had spent nearly an hour in the restroom. Her admit card carried her own pasted photograph and an altered name. It bore the hall ticket number and QR code of a genuine candidate, Tabu Naz. Staff scanned the code and captured her photo with the new Karnataka Examinations Authority (KEA) facial recognition app. It reported that she was not the candidate on record. She fled before the principal was told. KEA executive director H. Prasanna ordered a report with video footage and said legal action would follow.", "category": "harm prevented", "unexpected": false, "purpose": "KEA built the app to verify candidates at the hall entrance and prevent impersonation. It scans the admit card's QR code and matches a live photograph against the photo submitted at registration. Exposing this impersonator was exactly that purpose.", "system": "Karnataka Examinations Authority QR code-based facial recognition app (CET-2025 candidate verification)", "model_version": null, "developer": "Karnataka Examinations Authority (The Hindu and ETV Bharat: 'developed by KEA'; no vendor named)", "deployer": "Karnataka Examinations Authority", "location": "Bengaluru, India", "domain": "Education (state entrance examinations)", "segment": "Public sector examination administration", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-04-14-e47b" }, "ratings": [ { "group": "organizational", "depth": 2, "reason": "KEA stopped a forged-entry attempt in the app's first state-wide run. The Hindu reports that all verifications ran without technical glitches on the second day. The benefit to the authority is a working integrity control across 3,30,808 registrants, demonstrated on one case." }, { "group": "societal", "depth": 1, "reason": "Genuine CET-2025 candidates: one person who was not the candidate on record was kept out of a competition for seats among 3.3 lakh registrants. It is a small benefit spread across the cohort. The sources do not say what she meant to gain." } ], "ai_role": { "status": "assessed", "statement": "The app did the detecting. Staff scanned the admit card's QR code and captured the woman's photo. It flagged a mismatch with the photo on record (New Indian Express). It “revealed that she was not the genuine candidate” (The Hindu). The Hindu and the Times of India both report the forged card's contents. It carried her own photograph with the genuine candidate's hall ticket number and QR code. Comparing the card with her face would therefore not have exposed her. The flag depended on the app's match against the registered photograph. Staff then acted, but she fled before the principal was told. No arrest or sanction is attributable to the system. KEA's probe and the promised legal action were still pending in every report." }, "evidence": [ { "kind": "news report", "citation": "The Hindu Bureau (2025). CET: Impersonator caught using facial recognition app, probe ordered. The Hindu, Bengaluru, 17 April 2025.", "title": "CET: Impersonator caught using facial recognition app, probe ordered", "url": "https://www.thehindu.com/news/cities/bangalore/cet-impersonator-caught-using-facial-recognition-app-probe-ordered/article69461334.ece", "date": "2025-04-17", "written_by": "independent", "quote": "when her photo was captured and verified through the facial recognition app, it revealed that she was not the genuine candidate" }, { "kind": "news report", "citation": "The Times of India (2025). Facial recognition app foils impersonation attempt at Bengaluru CET centre. The Times of India, Bengaluru, 17 April 2025.", "title": "Facial recognition app foils impersonation attempt at Bengaluru CET centre", "url": "https://timesofindia.indiatimes.com/city/bengaluru/cet-facial-recognition-app-nails-fake-candidate-in-bengaluru/articleshow/120388988.cms", "date": "2025-04-17", "written_by": "independent", "quote": "She had changed the original candidate's name but retained the hall ticket number and QR code." }, { "kind": "news report", "citation": "Express News Service (2025). QR code facial recognition app exposes impersonator in Bengaluru. The New Indian Express, 18 April 2025.", "title": "QR code facial recognition app exposes impersonator in Bengaluru", "url": "https://www.newindianexpress.com/cities/bengaluru/2025/Apr/18/qr-code-facial-recognition-app-exposes-impersonator-in-bengaluru", "date": "2025-04-18", "written_by": "independent", "quote": "the facial recognition app flagged a mismatch between her face and the photo on record, revealing she was not the actual candidate" }, { "kind": "news report", "citation": "ETV Bharat (2025). Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On. ETV Bharat, 18 April 2025.", "title": "Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On", "url": "https://www.etvbharat.com/en/!state/karnataka-examination-authoritys-new-facial-recognition-system-helps-identify-a-proxy-candidate-investigation-underway-enn25041801048", "date": "2025-04-18", "written_by": "independent", "quote": "designed to detect impersonation by matching real-time photographs of candidates with those submitted at the time of registration" }, { "kind": "news report", "citation": "The Times of India (2025). Karnataka Common Entrance Test set to begin with new security norms for candidates. The Times of India, Bengaluru, 14 April 2025.", "title": "Karnataka Common Entrance Test set to begin with new security norms for candidates", "url": "https://timesofindia.indiatimes.com/city/bengaluru/karnataka-common-entrance-test-set-to-begin-with-new-security-norms-for-candidates/articleshow/120288622.cms", "date": "2025-04-14", "written_by": "independent", "quote": "This was piloted in a few centres earlier and will be in force across all centres this time round." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2025-04-14-e47b", "title": "Facial recognition app exposes exam impersonator during Karnataka CET", "url": "https://oecd.ai/en/incidents/2025-04-14-e47b", "date": "2025-04-14", "written_by": "independent", "quote": "The system flagged a mismatch when Tuba Fatima Jameel attempted to impersonate another student, thwarting her fraud and prompting a probe." }, { "kind": "news report", "citation": "ETV Bharat (2026). Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24. ETV Bharat, 22 April 2026.", "title": "Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24", "url": "https://www.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "date": "2026-04-22", "written_by": "independent", "quote": null } ], "replication": { "produced_by": [ "Admit cards printed with a QR code that resolves to the candidate's registered record and photograph. A photo pasted over the card therefore does not change what the app compares against.", "A live photograph taken at the hall door and matched by the app against the registered photo before entry. This ran at every center rather than the earlier pilot subset.", "Center staff instructed to act on a mismatch and escalate to the principal and KEA. Video coverage of the entrance that KEA could call for afterwards." ], "must_hold": [ "The registered photograph must be of the genuine candidate. The control fails if the fraud is committed at registration rather than at the door.", "Verification must be completed before the candidate is admitted. This depends on staffing and app reliability at scale. KEA reported no glitches this time.", "A flagged person must be detained or identified on the spot. Here she fled, so the deterrent rests on the probe and the video footage rather than on an arrest." ], "echoes": [] }, "paired_incident": null, "headline": "KEA's facial recognition app exposes a forged CET admit card", "metric": null, "comparator": null, "later": { "status": "persisted", "date": "2026-04-22", "note": "A year later KEA announced that facial recognition and QR-code verification would again be done for every candidate at K-CET 2026 on 23-24 April 2026.", "quote": "In order to avoid impersonation of candidates, facial recognition and QR code verification will be done for every candidate.", "url": "https://www.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512" }, "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 OECD Monitor entry and the cited sources. Filed as specific harm prevented, as the classifier proposed: the harm was specific (entry to the mathematics paper under a genuine candidate's hall ticket number) and the supporting evidence is that the forged card carried the impersonator's own photograph, so the app's match against the registered photo, not a visual check, produced the flag, and she left without writing the paper. Every account traces to a statement by KEA executive director H. Prasanna; KEA both built and ran the app, and no police record, court filing or outcome of the promised legal action was found. The Hindu says the catch came on the app's second day, the Times of India on its third (counting the Kannada paper of 15 April). Dated to the attempt, Thursday 17 April 2025; the Monitor's ID date (14 April) is the Times of India preview of the new security norms. The Monitor spells the name “Tuba Fatima Jameel” after ETV Bharat; The Hindu, the Times of India and the New Indian Express print “Tuba Fathima Jameel”, and the genuine candidate appears as “Tabu Naz” (Hindu, TOI, NIE) and “Tabu Naaz” (ETV). Udayavani's page sat behind a bot check and was not read.", "source": "https://oecd.ai/en/incidents/2025-04-14-e47b", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (1 sources and the registry id carried over); the duplicate was removed.", "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.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "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 a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0128", "catalog_version": "1.0.0", "source_fingerprint": "130ac3437aa9f0f7724f683c02021f8757b8e3ec53ac7f5901d17585acecdfdb", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0128-source-0001", "AIBD-0128-source-0002", "AIBD-0128-source-0003", "AIBD-0128-source-0004", "AIBD-0128-source-0005", "AIBD-0128-source-0006", "AIBD-0128-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": "Karnataka Examinations Authority (The Hindu and ETV Bharat: 'developed by KEA'; no vendor named)", "deployer": "Karnataka Examinations Authority" } }, "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 app did the detecting. Staff scanned the admit card's QR code and captured the woman's photo. It flagged a mismatch with the photo on record (New Indian Express). It “revealed that she was not the genuine candidate” (The Hindu). The Hindu and the Times of India both report the forged card's contents. It carried her own photograph with the genuine candidate's hall ticket number and QR code. Comparing the card with her face would therefore not have exposed her. The flag depended on the app's match against the registered photograph. Staff then acted, but she fled before the principal was told. No arrest or sanction is attributable to the system. KEA's probe and the promised legal action were still pending in every report." } } }, "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 examination app compared a captured photograph with the registered candidate’s photograph and flagged a mismatch. Comparing the person only with the forged admit card would not have exposed the substitution.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "harm prevented", "ai_role": { "status": "assessed", "statement": "The app did the detecting. Staff scanned the admit card's QR code and captured the woman's photo. It flagged a mismatch with the photo on record (New Indian Express). It “revealed that she was not the genuine candidate” (The Hindu). The Hindu and the Times of India both report the forged card's contents. It carried her own photograph with the genuine candidate's hall ticket number and QR code. Comparing the card with her face would therefore not have exposed her. The flag depended on the app's match against the registered photograph. Staff then acted, but she fled before the principal was told. No arrest or sanction is attributable to the system. KEA's probe and the promised legal action were still pending in every report." } } }, "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": "KEA stopped a forged-entry attempt in the app's first state-wide run. The Hindu reports that all verifications ran without technical glitches on the second day. The benefit to the authority is a working integrity control across 3,30,808 registrants, demonstrated on one case." }, { "group": "societal", "depth": 1, "reason": "Genuine CET-2025 candidates: one person who was not the candidate on record was kept out of a competition for seats among 3.3 lakh registrants. It is a small benefit spread across the cohort. The sources do not say what she meant to gain." } ] } }, "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 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": "KEA stopped a forged-entry attempt in the app's first state-wide run. The Hindu reports that all verifications ran without technical glitches on the second day. The benefit to the authority is a working integrity control across 3,30,808 registrants, demonstrated on one case." }, { "group": "societal", "depth": 1, "reason": "Genuine CET-2025 candidates: one person who was not the candidate on record was kept out of a competition for seats among 3.3 lakh registrants. It is a small benefit spread across the cohort. The sources do not say what she meant to gain." } ] } }, "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": "KEA stopped a forged-entry attempt in the app's first state-wide run. The Hindu reports that all verifications ran without technical glitches on the second day. The benefit to the authority is a working integrity control across 3,30,808 registrants, demonstrated on one case." }, { "group": "societal", "depth": 1, "reason": "Genuine CET-2025 candidates: one person who was not the candidate on record was kept out of a competition for seats among 3.3 lakh registrants. It is a small benefit spread across the cohort. The sources do not say what she meant to gain." } ] } }, "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 person left before the principal was informed. The reports describe a pending investigation and do not establish an arrest or sanction.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-04-14-e47b" } } }, "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": "2026-04-22", "note": "A year later KEA announced that facial recognition and QR-code verification would again be done for every candidate at K-CET 2026 on 23-24 April 2026.", "quote": "In order to avoid impersonation of candidates, facial recognition and QR code verification will be done for every candidate.", "url": "https://www.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512" } } }, "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": "Examination staff scanned the admit card, captured the photograph, and acted on the mismatch." }, "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-0128" } }, "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": "On Thursday 17 April 2025 a woman calling herself Tuba Fathima Jameel tried to enter the CET-2025 mathematics paper. The center was Silver Valley Public PU College in Malleswaram, Bengaluru. She had spent nearly an hour in the restroom. Her admit card carried her own pasted photograph and an altered name. It bore the hall ticket number and QR code of a genuine candidate, Tabu Naz. Staff scanned the code and captured her photo with the new Karnataka Examinations Authority (KEA) facial recognition app. It reported that she was not the candidate on record. She fled before the principal was told. KEA executive director H. Prasanna ordered a report with video footage and said legal action would follow.", "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": "KEA stopped a forged-entry attempt in the app's first state-wide run. The Hindu reports that all verifications ran without technical glitches on the second day. The benefit to the authority is a working integrity control across 3,30,808 registrants, demonstrated on one case." }, { "group": "societal", "depth": 1, "reason": "Genuine CET-2025 candidates: one person who was not the candidate on record was kept out of a competition for seats among 3.3 lakh registrants. It is a small benefit spread across the cohort. The sources do not say what she meant to gain." } ] } }, "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": "KEA built the app to verify candidates at the hall entrance and prevent impersonation. It scans the admit card's QR code and matches a live photograph against the photo submitted at registration. Exposing this impersonator was exactly 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": "Bengaluru, India" } }, "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": "KEA built the app to verify candidates at the hall entrance and prevent impersonation. It scans the admit card's QR code and matches a live photograph against the photo submitted at registration. Exposing this impersonator was exactly 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": "2025-04-17", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as specific harm prevented, as the classifier proposed: the harm was specific (entry to the mathematics paper under a genuine candidate's hall ticket number) and the supporting evidence is that the forged card carried the impersonator's own photograph, so the app's match against the registered photo, not a visual check, produced the flag, and she left without writing the paper. Every account traces to a statement by KEA executive director H. Prasanna; KEA both built and ran the app, and no police record, court filing or outcome of the promised legal action was found. The Hindu says the catch came on the app's second day, the Times of India on its third (counting the Kannada paper of 15 April). Dated to the attempt, Thursday 17 April 2025; the Monitor's ID date (14 April) is the Times of India preview of the new security norms. The Monitor spells the name “Tuba Fatima Jameel” after ETV Bharat; The Hindu, the Times of India and the New Indian Express print “Tuba Fathima Jameel”, and the genuine candidate appears as “Tabu Naz” (Hindu, TOI, NIE) and “Tabu Naaz” (ETV). Udayavani's page sat behind a bot check and was not read.", "source": "https://oecd.ai/en/incidents/2025-04-14-e47b", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (1 sources and the registry id carried over); the duplicate was removed.", "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.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "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": [ "Admit cards printed with a QR code that resolves to the candidate's registered record and photograph. A photo pasted over the card therefore does not change what the app compares against.", "A live photograph taken at the hall door and matched by the app against the registered photo before entry. This ran at every center rather than the earlier pilot subset.", "Center staff instructed to act on a mismatch and escalate to the principal and KEA. Video coverage of the entrance that KEA could call for afterwards." ], "must_hold": [ "The registered photograph must be of the genuine candidate. The control fails if the fraud is committed at registration rather than at the door.", "Verification must be completed before the candidate is admitted. This depends on staffing and app reliability at scale. KEA reported no glitches this time.", "A flagged person must be detained or identified on the spot. Here she fled, so the deterrent rests on the probe and the video footage rather than on an arrest." ], "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 specific harm prevented, as the classifier proposed: the harm was specific (entry to the mathematics paper under a genuine candidate's hall ticket number) and the supporting evidence is that the forged card carried the impersonator's own photograph, so the app's match against the registered photo, not a visual check, produced the flag, and she left without writing the paper. Every account traces to a statement by KEA executive director H. Prasanna; KEA both built and ran the app, and no police record, court filing or outcome of the promised legal action was found. The Hindu says the catch came on the app's second day, the Times of India on its third (counting the Kannada paper of 15 April). Dated to the attempt, Thursday 17 April 2025; the Monitor's ID date (14 April) is the Times of India preview of the new security norms. The Monitor spells the name “Tuba Fatima Jameel” after ETV Bharat; The Hindu, the Times of India and the New Indian Express print “Tuba Fathima Jameel”, and the genuine candidate appears as “Tabu Naz” (Hindu, TOI, NIE) and “Tabu Naaz” (ETV). Udayavani's page sat behind a bot check and was not read.", "source": "https://oecd.ai/en/incidents/2025-04-14-e47b", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Merged with a second write-up of the same event (1 sources and the registry id carried over); the duplicate was removed.", "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.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "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": "Education (state entrance examinations)", "segment": "Public sector examination 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": "The Hindu Bureau (2025). CET: Impersonator caught using facial recognition app, probe ordered. The Hindu, Bengaluru, 17 April 2025.", "title": "CET: Impersonator caught using facial recognition app, probe ordered", "url": "https://www.thehindu.com/news/cities/bangalore/cet-impersonator-caught-using-facial-recognition-app-probe-ordered/article69461334.ece", "date": "2025-04-17", "written_by": "independent", "quote": "when her photo was captured and verified through the facial recognition app, it revealed that she was not the genuine candidate" }, { "kind": "news report", "citation": "The Times of India (2025). Facial recognition app foils impersonation attempt at Bengaluru CET centre. The Times of India, Bengaluru, 17 April 2025.", "title": "Facial recognition app foils impersonation attempt at Bengaluru CET centre", "url": "https://timesofindia.indiatimes.com/city/bengaluru/cet-facial-recognition-app-nails-fake-candidate-in-bengaluru/articleshow/120388988.cms", "date": "2025-04-17", "written_by": "independent", "quote": "She had changed the original candidate's name but retained the hall ticket number and QR code." }, { "kind": "news report", "citation": "Express News Service (2025). QR code facial recognition app exposes impersonator in Bengaluru. The New Indian Express, 18 April 2025.", "title": "QR code facial recognition app exposes impersonator in Bengaluru", "url": "https://www.newindianexpress.com/cities/bengaluru/2025/Apr/18/qr-code-facial-recognition-app-exposes-impersonator-in-bengaluru", "date": "2025-04-18", "written_by": "independent", "quote": "the facial recognition app flagged a mismatch between her face and the photo on record, revealing she was not the actual candidate" }, { "kind": "news report", "citation": "ETV Bharat (2025). Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On. ETV Bharat, 18 April 2025.", "title": "Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On", "url": "https://www.etvbharat.com/en/!state/karnataka-examination-authoritys-new-facial-recognition-system-helps-identify-a-proxy-candidate-investigation-underway-enn25041801048", "date": "2025-04-18", "written_by": "independent", "quote": "designed to detect impersonation by matching real-time photographs of candidates with those submitted at the time of registration" }, { "kind": "news report", "citation": "The Times of India (2025). Karnataka Common Entrance Test set to begin with new security norms for candidates. The Times of India, Bengaluru, 14 April 2025.", "title": "Karnataka Common Entrance Test set to begin with new security norms for candidates", "url": "https://timesofindia.indiatimes.com/city/bengaluru/karnataka-common-entrance-test-set-to-begin-with-new-security-norms-for-candidates/articleshow/120288622.cms", "date": "2025-04-14", "written_by": "independent", "quote": "This was piloted in a few centres earlier and will be in force across all centres this time round." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2025-04-14-e47b", "title": "Facial recognition app exposes exam impersonator during Karnataka CET", "url": "https://oecd.ai/en/incidents/2025-04-14-e47b", "date": "2025-04-14", "written_by": "independent", "quote": "The system flagged a mismatch when Tuba Fatima Jameel attempted to impersonate another student, thwarting her fraud and prompting a probe." }, { "kind": "news report", "citation": "ETV Bharat (2026). Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24. ETV Bharat, 22 April 2026.", "title": "Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24", "url": "https://www.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "date": "2026-04-22", "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-0128-source-0001", "AIBD-0128-source-0002", "AIBD-0128-source-0003", "AIBD-0128-source-0004", "AIBD-0128-source-0005", "AIBD-0128-source-0006", "AIBD-0128-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": "The Hindu Bureau (2025). CET: Impersonator caught using facial recognition app, probe ordered. The Hindu, Bengaluru, 17 April 2025.", "title": "CET: Impersonator caught using facial recognition app, probe ordered", "url": "https://www.thehindu.com/news/cities/bangalore/cet-impersonator-caught-using-facial-recognition-app-probe-ordered/article69461334.ece", "date": "2025-04-17", "written_by": "independent", "quote": "when her photo was captured and verified through the facial recognition app, it revealed that she was not the genuine candidate" }, { "kind": "news report", "citation": "The Times of India (2025). Facial recognition app foils impersonation attempt at Bengaluru CET centre. The Times of India, Bengaluru, 17 April 2025.", "title": "Facial recognition app foils impersonation attempt at Bengaluru CET centre", "url": "https://timesofindia.indiatimes.com/city/bengaluru/cet-facial-recognition-app-nails-fake-candidate-in-bengaluru/articleshow/120388988.cms", "date": "2025-04-17", "written_by": "independent", "quote": "She had changed the original candidate's name but retained the hall ticket number and QR code." }, { "kind": "news report", "citation": "Express News Service (2025). QR code facial recognition app exposes impersonator in Bengaluru. The New Indian Express, 18 April 2025.", "title": "QR code facial recognition app exposes impersonator in Bengaluru", "url": "https://www.newindianexpress.com/cities/bengaluru/2025/Apr/18/qr-code-facial-recognition-app-exposes-impersonator-in-bengaluru", "date": "2025-04-18", "written_by": "independent", "quote": "the facial recognition app flagged a mismatch between her face and the photo on record, revealing she was not the actual candidate" }, { "kind": "news report", "citation": "ETV Bharat (2025). Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On. ETV Bharat, 18 April 2025.", "title": "Proxy CET Examinee Caught Red-Handed In Karnataka, Investigation On", "url": "https://www.etvbharat.com/en/!state/karnataka-examination-authoritys-new-facial-recognition-system-helps-identify-a-proxy-candidate-investigation-underway-enn25041801048", "date": "2025-04-18", "written_by": "independent", "quote": "designed to detect impersonation by matching real-time photographs of candidates with those submitted at the time of registration" }, { "kind": "news report", "citation": "The Times of India (2025). Karnataka Common Entrance Test set to begin with new security norms for candidates. The Times of India, Bengaluru, 14 April 2025.", "title": "Karnataka Common Entrance Test set to begin with new security norms for candidates", "url": "https://timesofindia.indiatimes.com/city/bengaluru/karnataka-common-entrance-test-set-to-begin-with-new-security-norms-for-candidates/articleshow/120288622.cms", "date": "2025-04-14", "written_by": "independent", "quote": "This was piloted in a few centres earlier and will be in force across all centres this time round." }, { "kind": "registry label", "citation": "OECD AI Incidents Monitor, incident 2025-04-14-e47b", "title": "Facial recognition app exposes exam impersonator during Karnataka CET", "url": "https://oecd.ai/en/incidents/2025-04-14-e47b", "date": "2025-04-14", "written_by": "independent", "quote": "The system flagged a mismatch when Tuba Fatima Jameel attempted to impersonate another student, thwarting her fraud and prompting a probe." }, { "kind": "news report", "citation": "ETV Bharat (2026). Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24. ETV Bharat, 22 April 2026.", "title": "Karnataka Examination Authority All Set To Conduct K-CET On April 23, 24", "url": "https://www.etvbharat.com/en/state/karnataka-education-authority-to-conduct-k-cet-on-april-23-and-24-enn26042204512", "date": "2026-04-22", "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": "KEA's facial recognition app flagged a woman with a forged CET admit card in Bengaluru on 17 April 2025; she fled before writing the paper.", "account": "On Thursday 17 April 2025 a woman calling herself Tuba Fathima Jameel tried to enter the CET-2025 mathematics paper. The center was Silver Valley Public PU College in Malleswaram, Bengaluru. She had spent nearly an hour in the restroom. Her admit card carried her own pasted photograph and an altered name. It bore the hall ticket number and QR code of a genuine candidate, Tabu Naz. Staff scanned the code and captured her photo with the new Karnataka Examinations Authority (KEA) facial recognition app. It reported that she was not the candidate on record. She fled before the principal was told. KEA executive director H. Prasanna ordered a report with video footage and said legal action would 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": "Karnataka Examinations Authority QR code-based facial recognition app (CET-2025 candidate 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": "Karnataka Examinations Authority (The Hindu and ETV Bharat: 'developed by KEA'; no vendor 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": "KEA's QR-code facial recognition app exposes a woman trying to enter a Bengaluru CET hall on another candidate's forged admit card", "headline": "KEA's facial recognition app exposes a forged CET admit card" } }, "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": [ "130ac3437aa9f0f7724f683c02021f8757b8e3ec53ac7f5901d17585acecdfdb" ], "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 17 April 2025 a woman calling herself Tuba Fathima Jameel tried to enter the CET-2025 mathematics paper. The center was Silver Valley Public PU College in Malleswaram, Bengaluru. She had spent nearly an hour in the restroom. Her admit card carried her own pasted photograph and an altered name. It bore the hall ticket number and QR code of a genuine candidate, Tabu Naz. Staff scanned the code and captured her photo with the new Karnataka Examinations Authority (KEA) facial recognition app. It reported that she was not the candidate on record. She fled before the principal was told. KEA executive director H. Prasanna ordered a report with video footage and said legal action would follow.", "replication": { "produced_by": [ "Admit cards printed with a QR code that resolves to the candidate's registered record and photograph. A photo pasted over the card therefore does not change what the app compares against.", "A live photograph taken at the hall door and matched by the app against the registered photo before entry. This ran at every center rather than the earlier pilot subset.", "Center staff instructed to act on a mismatch and escalate to the principal and KEA. Video coverage of the entrance that KEA could call for afterwards." ], "must_hold": [ "The registered photograph must be of the genuine candidate. The control fails if the fraud is committed at registration rather than at the door.", "Verification must be completed before the candidate is admitted. This depends on staffing and app reliability at scale. KEA reported no glitches this time.", "A flagged person must be detained or identified on the spot. Here she fled, so the deterrent rests on the probe and the video footage rather than on an arrest." ], "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-0081", "case_url": "records/AIBD-0081.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The CGU release states that each tender “teve alerta do Robô ALICE” and that auditors then confirmed the overpricing risk. The suspension, revocation and re-estimation are described as results of that audit action. Human auditors and hospital managers made the decisions. ALICE's role was detection and triage. Developer and deployer are the same public body, so this is the body documenting its own tool. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, { "label": "Comparison", "value": "the hospital's original price estimate for tender 90017/2024", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "label": "Before", "value": "valor estimado em R$ 15.450.714,80" }, { "label": "After", "value": "R$ 12.386.939,96" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "record": { "id": "AIBD-0081", "title": "CGU's ALICE robot flags overpricing in three 2024 Rio de Janeiro federal hospital tenders; one suspended, one revoked, one re-estimated", "summary": "CGU ALICE alerts: three 2024 federal hospital tenders in Rio de Janeiro state suspended, revoked or re-estimated; R$19.4m overpricing risk flagged (sum of three confirmed risks)", "date_observed": "2025-01-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 14 January 2025 the Controladoria-Geral da União (CGU) reported on ALICE (Analisador de Licitações, Contratos e Editais). ALICE, its in-house tool, reads procurement documents with artificial-intelligence techniques and issues risk alerts. It had flagged three 2024 tenders of federal hospitals in Rio de Janeiro state. At the Hospital Federal de Ipanema, tender 90016/2024 for laminated gowns (estimated R$16,726,635.12) was suspended after auditors confirmed an overpricing risk of R$10,925,029.92. Tender 90017/2024 for nursing supplies (estimated R$15,450,714.80) had an overpricing risk of R$5,994,645.60. It was re-estimated at R$12,386,939.96, a saving of R$3,063,774.84. At the Hospital Federal dos Servidores do Estado, tender 90002/2024 (estimated R$26,578,591.02) was revoked after a confirmed risk of R$2,475,054.36. The release gives no total. The three confirmed risks sum to R$19,394,729.88, the R$19.4 million of the headline and metric. The release gives no dates for the suspension and revocation.", "category": "harm prevented", "unexpected": false, "purpose": "Continuous automated audit of federal procurement notices to detect risks such as inflated price estimates before contracts are awarded", "system": "ALICE (Analisador de Licitações, Contratos e Editais), CGU text-mining and AI alert tool", "model_version": null, "developer": "Controladoria-Geral da União (CGU), in-house", "deployer": "Controladoria-Geral da União (CGU)", "location": "Rio de Janeiro (state), Brazil", "domain": "Public administration: government audit and procurement control", "segment": "Federal internal control (anti-corruption and waste prevention)", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identifiable individual beneficiary is named; the beneficiaries are public bodies and, indirectly, taxpayers." }, { "group": "organizational", "depth": 2, "reason": "Material to the two hospitals and the federal budget. One tender saw a documented R$3,063,774.84 reduction. One suspension and one revocation removed R$13.4 million of confirmed overpricing risk." }, { "group": "societal", "depth": 1, "reason": "Small at national scale; three tenders among 118,069 processes analyzed in 2024." } ], "ai_role": { "status": "assessed", "statement": "The CGU release states that each tender “teve alerta do Robô ALICE” and that auditors then confirmed the overpricing risk. The suspension, revocation and re-estimation are described as results of that audit action. Human auditors and hospital managers made the decisions. ALICE's role was detection and triage. Developer and deployer are the same public body, so this is the body documenting its own tool. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, "evidence": [ { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "a licitação 90016/2024 - Relatório 1686291 -– teve alerta do Robô ALICE e foi analisada pelos auditores que indicaram risco de sobrepreço de R$ 10.925.029,92" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Same release, Hospital Federal dos Servidores do Estado section.", "title": "Atuação do Robô ALICE segue auxiliando auditores (HFSE section)", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "Como resultado da atuação, a licitação foi revogada." }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "Das auditorias concluídas, foram economizados R$ 257,25 milhões" }, { "kind": "news report", "citation": "R7 Planalto (2025). ALICE: robô da CGU previne erros e desperdícios de licitações públicas, 28 Jan 2025, republished by Licitação.net.", "title": "ALICE: robô da CGU previne erros e desperdícios de licitações públicas", "url": "https://www.licitacao.net/noticias/alice:-robo-da-cgu-previne-erros-e-desperdicios-de-licitacoes-publicas", "date": "2025-01-28", "written_by": "independent", "quote": "Nos primeiros relatórios de auditoria de 2024, ALICE identificou riscos de sobrepreço em três licitações de Hospitais Federais no Rio de Janeiro, relacionadas à compra de equipamentos e insumos." }, { "kind": "news report", "citation": "Observatório da Nova Lei de Licitações (2025). Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações, 21 Jan 2025 (with information from CGU).", "title": "Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações", "url": "https://www.novaleilicitacao.com.br/2025/01/21/atuacao-do-robo-alice-auxilia-cgu-a-rastrear-r-25-bilhoes-em-licitacoes/", "date": "2025-01-21", "written_by": "independent", "quote": "uma licitação para a compra de aventais laminados, inicialmente estimada em R$ 16,7 milhões, foi suspensa após a detecção de um possível sobrepreço de R$ 10,9 milhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal. doi:10.36428/revistadacgu.v14i26.530", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" } ], "replication": { "produced_by": [ "ALICE audit trails that flag price surveys with abnormally high estimates in documents published on Compras.gov.br and bank procurement systems", "CGU preventive audit teams that examine flagged tenders before the auction concludes and recommend corrections", "Managers' response to audit recommendations (suspension, revocation or new price estimate)" ], "must_hold": [ "Procurement planning documents and notices must be machine-readable in the monitored systems", "Audit capacity must exist to act on alerts before award", "Reference prices must be available to confirm overpricing" ], "echoes": [ { "date": "2021-07-21", "setting": "Hospital Federal de Ipanema glove tender (Pregão 15/2021), CGU audit with ALICE support, reported 4 October 2023", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" }, { "date": "2024-01-01", "setting": "Federal road-works engineering tender: about R$100 million cut from the reference value after an ALICE alert (CGU, 30 December 2024); the release gives no date for this tender, so the year is inferred from the 2024 year-end release", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, { "date": "2022-12-01", "setting": "TCE-MG state-level use of Alice: in December 2022 TCE-MG published the result of an audit covering 2,297 state and municipal tenders in August 2022 alone, with corrections to notices worth R$91,723,011.31 (Atricon, 28 June 2023; publication month only)", "model_version": null, "outcome": "recurred", "source": "https://atricon.org.br/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "738", "title": "Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud", "url": "https://incidentdatabase.ai/cite/738", "severity": null, "group": "organizational", "basis": "Government fraud-risk flagging algorithm, like ALICE's tender alerts, sent 200,000 housing-benefit claims to investigation, two-thirds legitimate, wasting public funds and stressing claimants." }, "headline": "CGU's ALICE flags R$19.4m overpricing risk in three Rio tenders", "metric": "a saving of R$3,063,774.84", "comparator": { "against": "the hospital's original price estimate for tender 90017/2024", "before": "valor estimado em R$ 15.450.714,80", "after": "R$ 12.386.939,96", "basis": "measured", "quote": "Como resultado da atuação, os gestores procederam nova estimativa de preços, reduzindo o valor total estimado da compra para R$ 12.386.939,96, o que representou uma economia de mais de R$ 3 milhões (R$ 3.063.774,84).", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, "later": { "status": "unknown", "date": null, "note": "The two later press items (21 and 28 January 2025) restate the release; no later development was found,.", "quote": null, "url": null }, "value": { "amount": "R$ 3.063.774,84", "what": "reduction of the estimated value of tender 90017/2024 after the re-estimate (the release gives no total for the three tenders)", "who": "Hospital Federal de Ipanema, federal budget", "estimate": false, "quote": "Como resultado da atuação, os gestores procederam nova estimativa de preços, reduzindo o valor total estimado da compra para R$ 12.386.939,96, o que representou uma economia de mais de R$ 3 milhões (R$ 3.063.774,84).", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, "developer_country": "Brazil", "deployer_country": "Brazil", "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 738 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/738", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Extended the Licitação.net quote to the end of its sentence; added the R$5,994,645.60 risk on tender 90017/2024 and stated that the R$19.4 million headline figure is the registry's sum of the three confirmed risks (R$19,394,729.88) because the CGU gives no total; dated the Revista da CGU article 20 December 2022 and re-described it as a practitioner report (relato) in the CGU's own journal; moved the record to “harm prevented” because the overpriced tenders were in progress and the CGU records their suspension, revocation and re-estimate as results of the alert-driven audit; set the benefit type to Trustible's “Reduced Risk” from the published framework (entry quoted in the taxonomy note; data/taxonomy.json must carry it) so that the type names the benefit instead of a closest fit; noted that the road-works echo year is inferred from the 2024 year-end release and that December 2022 is when TCE-MG published its audit result; and located the hospitals in the state of Rio de Janeiro as the release does.", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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: three independent news reports, 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-0081", "catalog_version": "1.0.0", "source_fingerprint": "d6b958633d54ed56d5548b8102bad819ea3cf9c0ce6721455405dbd8fe2e4ff7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0081-source-0001", "AIBD-0081-source-0002", "AIBD-0081-source-0003", "AIBD-0081-source-0004", "AIBD-0081-source-0005", "AIBD-0081-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-0081-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": "Controladoria-Geral da União (CGU), in-house", "deployer": "Controladoria-Geral da União (CGU)" } }, "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 CGU release states that each tender “teve alerta do Robô ALICE” and that auditors then confirmed the overpricing risk. The suspension, revocation and re-estimation are described as results of that audit action. Human auditors and hospital managers made the decisions. ALICE's role was detection and triage. Developer and deployer are the same public body, so this is the body documenting its own tool. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." } } }, "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 CGU release states that each tender “teve alerta do Robô ALICE” and that auditors then confirmed the overpricing risk. 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One suspension and one revocation removed R$13.4 million of confirmed overpricing risk." }, { "group": "societal", "depth": 1, "reason": "Small at national scale; three tenders among 118,069 processes analyzed in 2024." } ] } }, "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": "Government fraud-risk flagging algorithm, like ALICE's tender alerts, sent 200,000 housing-benefit claims to investigation, two-thirds legitimate, wasting public funds and stressing claimants.", "links": [ { "database": "AI Incident Database", "identifier": "738", "title": "Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud", "url": "https://incidentdatabase.ai/cite/738", "group": "organizational", "basis": "Government fraud-risk flagging algorithm, like ALICE's tender alerts, sent 200,000 housing-benefit claims to investigation, two-thirds legitimate, wasting public funds and stressing claimants.", "relation": "comparable_harm" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Existing paired_incident classification; 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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": "unknown", "note": "The two later press items (21 and 28 January 2025) restate the release; no later development 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-0081" } }, "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": "On 14 January 2025 the Controladoria-Geral da União (CGU) reported on ALICE (Analisador de Licitações, Contratos e Editais). ALICE, its in-house tool, reads procurement documents with artificial-intelligence techniques and issues risk alerts. It had flagged three 2024 tenders of federal hospitals in Rio de Janeiro state. At the Hospital Federal de Ipanema, tender 90016/2024 for laminated gowns (estimated R$16,726,635.12) was suspended after auditors confirmed an overpricing risk of R$10,925,029.92. Tender 90017/2024 for nursing supplies (estimated R$15,450,714.80) had an overpricing risk of R$5,994,645.60. It was re-estimated at R$12,386,939.96, a saving of R$3,063,774.84. At the Hospital Federal dos Servidores do Estado, tender 90002/2024 (estimated R$26,578,591.02) was revoked after a confirmed risk of R$2,475,054.36. The release gives no total. The three confirmed risks sum to R$19,394,729.88, the R$19.4 million of the headline and metric. The release gives no dates for the suspension and revocation.", "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": "No identifiable individual beneficiary is named; the beneficiaries are public bodies and, indirectly, taxpayers." }, { "group": "organizational", "depth": 2, "reason": "Material to the two hospitals and the federal budget. One tender saw a documented R$3,063,774.84 reduction. One suspension and one revocation removed R$13.4 million of confirmed overpricing risk." }, { "group": "societal", "depth": 1, "reason": "Small at national scale; three tenders among 118,069 processes analyzed in 2024." } ] } }, "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": "Continuous automated audit of federal procurement notices to detect risks such as inflated price estimates before contracts are awarded" } }, "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": "Rio de Janeiro (state), Brazil", "developer_country": "Brazil", "deployer_country": "Brazil" } }, "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": "Continuous automated audit of federal procurement notices to detect risks such as inflated price estimates before contracts are awarded" } }, "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": "2025-01-14", "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 738 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/738", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Extended the Licitação.net quote to the end of its sentence; added the R$5,994,645.60 risk on tender 90017/2024 and stated that the R$19.4 million headline figure is the registry's sum of the three confirmed risks (R$19,394,729.88) because the CGU gives no total; dated the Revista da CGU article 20 December 2022 and re-described it as a practitioner report (relato) in the CGU's own journal; moved the record to “harm prevented” because the overpriced tenders were in progress and the CGU records their suspension, revocation and re-estimate as results of the alert-driven audit; set the benefit type to Trustible's “Reduced Risk” from the published framework (entry quoted in the taxonomy note; data/taxonomy.json must carry it) so that the type names the benefit instead of a closest fit; noted that the road-works echo year is inferred from the 2024 year-end release and that December 2022 is when TCE-MG published its audit result; and located the hospitals in the state of Rio de Janeiro as the release does.", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "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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and located the hospitals in the state of Rio de Janeiro as the release does.", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "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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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": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "a licitação 90016/2024 - Relatório 1686291 -– teve alerta do Robô ALICE e foi analisada pelos auditores que indicaram risco de sobrepreço de R$ 10.925.029,92" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Same release, Hospital Federal dos Servidores do Estado section.", "title": "Atuação do Robô ALICE segue auxiliando auditores (HFSE section)", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "Como resultado da atuação, a licitação foi revogada." }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "Das auditorias concluídas, foram economizados R$ 257,25 milhões" }, { "kind": "news report", "citation": "R7 Planalto (2025). ALICE: robô da CGU previne erros e desperdícios de licitações públicas, 28 Jan 2025, republished by Licitação.net.", "title": "ALICE: robô da CGU previne erros e desperdícios de licitações públicas", "url": "https://www.licitacao.net/noticias/alice:-robo-da-cgu-previne-erros-e-desperdicios-de-licitacoes-publicas", "date": "2025-01-28", "written_by": "independent", "quote": "Nos primeiros relatórios de auditoria de 2024, ALICE identificou riscos de sobrepreço em três licitações de Hospitais Federais no Rio de Janeiro, relacionadas à compra de equipamentos e insumos." }, { "kind": "news report", "citation": "Observatório da Nova Lei de Licitações (2025). Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações, 21 Jan 2025 (with information from CGU).", "title": "Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações", "url": "https://www.novaleilicitacao.com.br/2025/01/21/atuacao-do-robo-alice-auxilia-cgu-a-rastrear-r-25-bilhoes-em-licitacoes/", "date": "2025-01-21", "written_by": "independent", "quote": "uma licitação para a compra de aventais laminados, inicialmente estimada em R$ 16,7 milhões, foi suspensa após a detecção de um possível sobrepreço de R$ 10,9 milhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal. doi:10.36428/revistadacgu.v14i26.530", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" } ] } }, "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-0081-source-0001", "AIBD-0081-source-0002", "AIBD-0081-source-0003", "AIBD-0081-source-0004", "AIBD-0081-source-0005", "AIBD-0081-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": "press release", "citation": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "a licitação 90016/2024 - Relatório 1686291 -– teve alerta do Robô ALICE e foi analisada pelos auditores que indicaram risco de sobrepreço de R$ 10.925.029,92" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Same release, Hospital Federal dos Servidores do Estado section.", "title": "Atuação do Robô ALICE segue auxiliando auditores (HFSE section)", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": "Como resultado da atuação, a licitação foi revogada." }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "Das auditorias concluídas, foram economizados R$ 257,25 milhões" }, { "kind": "news report", "citation": "R7 Planalto (2025). ALICE: robô da CGU previne erros e desperdícios de licitações públicas, 28 Jan 2025, republished by Licitação.net.", "title": "ALICE: robô da CGU previne erros e desperdícios de licitações públicas", "url": "https://www.licitacao.net/noticias/alice:-robo-da-cgu-previne-erros-e-desperdicios-de-licitacoes-publicas", "date": "2025-01-28", "written_by": "independent", "quote": "Nos primeiros relatórios de auditoria de 2024, ALICE identificou riscos de sobrepreço em três licitações de Hospitais Federais no Rio de Janeiro, relacionadas à compra de equipamentos e insumos." }, { "kind": "news report", "citation": "Observatório da Nova Lei de Licitações (2025). Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações, 21 Jan 2025 (with information from CGU).", "title": "Atuação do robô ALICE auxilia CGU a rastrear R$ 25 bilhões em licitações", "url": "https://www.novaleilicitacao.com.br/2025/01/21/atuacao-do-robo-alice-auxilia-cgu-a-rastrear-r-25-bilhoes-em-licitacoes/", "date": "2025-01-21", "written_by": "independent", "quote": "uma licitação para a compra de aventais laminados, inicialmente estimada em R$ 16,7 milhões, foi suspensa após a detecção de um possível sobrepreço de R$ 10,9 milhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal. doi:10.36428/revistadacgu.v14i26.530", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" } ] } }, "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": "CGU ALICE alerts: three 2024 federal hospital tenders in Rio de Janeiro state suspended, revoked or re-estimated; R$19.4m overpricing risk flagged (sum of three confirmed risks)", "account": "On 14 January 2025 the Controladoria-Geral da União (CGU) reported on ALICE (Analisador de Licitações, Contratos e Editais). ALICE, its in-house tool, reads procurement documents with artificial-intelligence techniques and issues risk alerts. It had flagged three 2024 tenders of federal hospitals in Rio de Janeiro state. At the Hospital Federal de Ipanema, tender 90016/2024 for laminated gowns (estimated R$16,726,635.12) was suspended after auditors confirmed an overpricing risk of R$10,925,029.92. Tender 90017/2024 for nursing supplies (estimated R$15,450,714.80) had an overpricing risk of R$5,994,645.60. It was re-estimated at R$12,386,939.96, a saving of R$3,063,774.84. At the Hospital Federal dos Servidores do Estado, tender 90002/2024 (estimated R$26,578,591.02) was revoked after a confirmed risk of R$2,475,054.36. The release gives no total. The three confirmed risks sum to R$19,394,729.88, the R$19.4 million of the headline and metric. The release gives no dates for the suspension and revocation." } }, "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": "ALICE (Analisador de Licitações, Contratos e Editais), CGU text-mining and AI alert tool" } }, "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": "Controladoria-Geral da União (CGU), in-house" } }, "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": "CGU's ALICE robot flags overpricing in three 2024 Rio de Janeiro federal hospital tenders; one suspended, one revoked, one re-estimated", "headline": "CGU's ALICE flags R$19.4m overpricing risk in three Rio tenders" } }, "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": [ "d6b958633d54ed56d5548b8102bad819ea3cf9c0ce6721455405dbd8fe2e4ff7" ], "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 14 January 2025 the Controladoria-Geral da União (CGU) reported on ALICE (Analisador de Licitações, Contratos e Editais). ALICE, its in-house tool, reads procurement documents with artificial-intelligence techniques and issues risk alerts. It had flagged three 2024 tenders of federal hospitals in Rio de Janeiro state. At the Hospital Federal de Ipanema, tender 90016/2024 for laminated gowns (estimated R$16,726,635.12) was suspended after auditors confirmed an overpricing risk of R$10,925,029.92. Tender 90017/2024 for nursing supplies (estimated R$15,450,714.80) had an overpricing risk of R$5,994,645.60. It was re-estimated at R$12,386,939.96, a saving of R$3,063,774.84. At the Hospital Federal dos Servidores do Estado, tender 90002/2024 (estimated R$26,578,591.02) was revoked after a confirmed risk of R$2,475,054.36. The release gives no total. The three confirmed risks sum to R$19,394,729.88, the R$19.4 million of the headline and metric. The release gives no dates for the suspension and revocation.", "replication": { "produced_by": [ "ALICE audit trails that flag price surveys with abnormally high estimates in documents published on Compras.gov.br and bank procurement systems", "CGU preventive audit teams that examine flagged tenders before the auction concludes and recommend corrections", "Managers' response to audit recommendations (suspension, revocation or new price estimate)" ], "must_hold": [ "Procurement planning documents and notices must be machine-readable in the monitored systems", "Audit capacity must exist to act on alerts before award", "Reference prices must be available to confirm overpricing" ], "echoes": [ { "date": "2021-07-21", "setting": "Hospital Federal de Ipanema glove tender (Pregão 15/2021), CGU audit with ALICE support, reported 4 October 2023", "outcome": "recurred", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" }, { "date": "2024-01-01", "setting": "Federal road-works engineering tender: about R$100 million cut from the reference value after an ALICE alert (CGU, 30 December 2024); the release gives no date for this tender, so the year is inferred from the 2024 year-end release", "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, { "date": "2022-12-01", "setting": "TCE-MG state-level use of Alice: in December 2022 TCE-MG published the result of an audit covering 2,297 state and municipal tenders in August 2022 alone, with corrections to notices worth R$91,723,011.31 (Atricon, 28 June 2023; publication month only)", "outcome": "recurred", "source": "https://atricon.org.br/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" } ] } } }, "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": 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-0140", "case_url": "records/AIBD-0140.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Screenshots attached to the post show Grok answering three questions about the uploaded image. It said there is “a clear fracture line visible in the distal radius”. It said the line is “a fracture line rather than a fused growth plate”. It said the break is “quite obvious” with “dorsal displacement”. Each time it advised a professional consultation. The orthopaedist's reading matched. But the mother states that she did not mention Grok to the specialist and that the diagnosis was “completely independent”. She had already asked for the X-ray prints to take to the PCP. Grok's contribution was therefore to strengthen her resolve to seek a second opinion. Everything rests on her account. No clinician, clinic or record has spoken publicly. The next day Grok told a physician who asked that it cannot diagnose medical injuries." }, { "label": "Comparison", "value": "the urgent-care doctor's and radiologist's reading of the same X-rays", "url": "https://x.com/AJKayWriter/status/1878084198205907173" }, { "label": "Before", "value": "Both the doctor who saw her and the radiologist declared her free of breaks and, after answering some of our questions, sent her home with an ace wrap and ibuprofen." }, { "label": "After", "value": "the wrist specialist took new x-rays (multiple views), examined her, and confirmed Grok’s diagnosis of a distal radial head fracture with dorsal displacement." }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0140", "title": "Grok may have read a “clear fracture line in the distal radius” on a teenager's wrist X-ray that urgent care had cleared; a wrist specialist confirmed the displaced fracture and cast it (January 2025)", "summary": "By her mother's X post of 11 January 2025, Grok flagged a displaced wrist fracture urgent care missed; an orthopaedist confirmed and cast it.", "date_observed": "2025-01-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 11 January 2025 AJ Kay (@AJKayWriter, United States) posted about a multi-vehicle freeway crash the weekend before. An urgent-care doctor and radiologist had read her teenage daughter's wrist X-rays as free of breaks. They sent her home with an ace wrap and ibuprofen. With the hand cold and tingling and the thumb immobile, she uploaded the printed X-ray to Grok on X. It said there was “a clear fracture line in the distal radius”, not a growth plate, and called it “quite obvious”. After a PCP referral, a wrist specialist took new X-rays next day. The specialist diagnosed a distal radial head fracture with dorsal displacement and cast it, saying untreated displacement would likely have needed surgery.", "category": "protective response", "unexpected": false, "purpose": "Grok is xAI's general-purpose chatbot on X with image input. On 3 January 2025 Elon Musk invited users to upload “any image to Grok for analysis, from medical tests to video games”. So reading an X-ray was a promoted use rather than a designed clinical function. Grok's own replies told the user to consult a professional. When a physician asked it the next day, it said it cannot diagnose medical injuries.", "system": "Grok chatbot (xAI) on X, image analysis of an uploaded wrist X-ray", "model_version": "Grok 2 (the poster cites Musk's statement that 'Grok2 could read medical images'; not confirmed by xAI)", "developer": "xAI", "deployer": "X Corp (Grok on the X platform)", "location": null, "domain": "Healthcare - musculoskeletal radiology / fracture diagnosis", "segment": "Caregiver-initiated use of a consumer LLM", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-01-12-a928" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "By her mother's account the girl's displaced distal radius fracture was set and cast within days instead of staying in an ace wrap. The orthopaedist's remark that untreated displacement would likely have needed surgery comes only from the mother. No clinician, clinic or record is public. She had already asked for the films for the PCP. Whether the outcome would have differed without Grok is not established." } ], "ai_role": { "status": "assessed", "statement": "Screenshots attached to the post show Grok answering three questions about the uploaded image. It said there is “a clear fracture line visible in the distal radius”. It said the line is “a fracture line rather than a fused growth plate”. It said the break is “quite obvious” with “dorsal displacement”. Each time it advised a professional consultation. The orthopaedist's reading matched. But the mother states that she did not mention Grok to the specialist and that the diagnosis was “completely independent”. She had already asked for the X-ray prints to take to the PCP. Grok's contribution was therefore to strengthen her resolve to seek a second opinion. Everything rests on her account. No clinician, clinic or record has spoken publicly. The next day Grok told a physician who asked that it cannot diagnose medical injuries." }, "evidence": [ { "kind": "other", "citation": "Kay, A. J. (2025). True story: @Grok diagnosed my daughter's broken wrist last week. Post on X, @AJKayWriter, 11 January 2025 (14:19 UTC), status 1878084198205907173; three Grok screenshots and one photo of the cast attached; about 14.4 million views.", "title": "True story: @Grok diagnosed my daughter's broken wrist last week", "url": "https://x.com/AJKayWriter/status/1878084198205907173", "date": "2025-01-11", "written_by": "beneficiary", "quote": "the wrist specialist took new x-rays (multiple views), examined her, and confirmed Grok’s diagnosis of a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "NDTV (2025). Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It. NDTV Feature, 12 January 2025.", "title": "Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It", "url": "https://www.ndtv.com/feature/woman-claims-elon-musks-grok-detected-her-daughter-s-fracture-after-doctors-fail-to-diagnose-it-7456337", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that delayed treatment could have led to surgery, but thanks to Grok's intervention, they were able to avoid this outcome and provide timely treatment." }, { "kind": "news report", "citation": "Hindustan Times (2025). Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'. Hindustan Times Trending, 12 January 2025.", "title": "Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'", "url": "https://www.hindustantimes.com/trending/mother-claims-elon-musk-s-ai-detected-daughter-s-fracture-that-doctors-missed-this-was-eye-opening-for-me-101736662763447.html", "date": "2025-01-12", "written_by": "independent", "quote": "A wrist specialist confirmed Grok’s diagnosis—a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "Mukhopadhyay, S. (2025). AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it. Mint, 12 January 2025.", "title": "AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it", "url": "https://www.livemint.com/ai/artificial-intelligence/ai-chat-helps-avoid-surgery-elon-musk-s-grok-identifies-teenage-girl-s-wrist-fracture-after-doctors-fail-to-diagnose-it-11736671754200.html", "date": "2025-01-12", "written_by": "independent", "quote": "the mother consulted a wrist specialist, who confirmed Grok’s diagnosis using additional X-rays and physical examination." }, { "kind": "news report", "citation": "India Today Trending Desk (2025). Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post. India Today, 12 January 2025.", "title": "Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post", "url": "https://www.indiatoday.in/trending-news/story/elon-musk-grok-diagnoses-fracture-missed-by-doctors-us-woman-claims-in-viral-post-2663680-2025-01-12", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that any further delay could have led to surgery, but due to Grok's timely detection, proper treatment was promptly administered." }, { "kind": "news report", "citation": "Livemint (2025). Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check. Mint, 12 January 2025. Reports Dr Cyriac Abby Philips's exchange with Grok.", "title": "Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check", "url": "https://www.livemint.com/technology/tech-news/hey-liar-elon-musk-claims-grok-ai-can-diagnose-medical-injuries-liverdoc-abby-philips-gives-him-a-reality-check-11736678451620.html", "date": "2025-01-12", "written_by": "independent", "quote": "No, I cannot diagnose medical injuries." }, { "kind": "press release", "citation": "Musk, E. (2025). Grok can diagnose medical injuries. Post on X, @elonmusk, 11 January 2025 (22:12 UTC), status 1878203372513136854; Mint reports it was posted in reply to a post about the AJ Kay account.", "title": "Grok can diagnose medical injuries", "url": "https://x.com/elonmusk/status/1878203372513136854", "date": "2025-01-11", "written_by": "makers", "quote": "Grok can diagnose medical injuries" }, { "kind": "news report", "citation": "TOI Lifestyle Desk (2025). Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman. The Times of India, 15 January 2025. Quotes Musk's 3 January 2025 post inviting image uploads.", "title": "Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman", "url": "https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/elon-musks-grok-ai-diagnoses-fracture-missed-by-doctors-claims-woman/articleshow/117255256.cms", "date": "2025-01-15", "written_by": "independent", "quote": "You can upload any image to Grok for analysis, from medical tests to video games! Just tap the + button or paste the image into the entry bar" } ], "replication": { "produced_by": [ "A printed urgent-care wrist X-ray that the parent could photograph and upload", "Grok on X with image input, which Musk was publicly promoting for medical images in January 2025", "A parent who distrusted the first reading because of worsening signs (cold, tingling hand; immobile thumb; a wrist that did not look right). She had already asked for the films for the PCP", "Access within days to a PCP referral and a wrist specialist who re-imaged and examined the wrist" ], "must_hold": [ "Grok's reading was correct for this image on the mother's account only. Asked by a physician the next day, it said it cannot diagnose injuries. So its output is not reliable across users or images", "A clinician re-examined and re-imaged the wrist. Grok's output was never used as the diagnosis or the basis of treatment", "The only sources are the mother's post and news reports built from it. No clinic, clinician or record is public, and the surgery-avoidance statement is second-hand" ], "echoes": [] }, "paired_incident": null, "headline": "Grok spots a displaced wrist fracture that urgent care missed", "metric": null, "comparator": { "against": "the urgent-care doctor's and radiologist's reading of the same X-rays", "before": "Both the doctor who saw her and the radiologist declared her free of breaks and, after answering some of our questions, sent her home with an ace wrap and ibuprofen.", "after": "the wrist specialist took new x-rays (multiple views), examined her, and confirmed Grok’s diagnosis of a distal radial head fracture with dorsal displacement.", "basis": "assumed", "quote": "Both the doctor who saw her and the radiologist declared her free of breaks and, after answering some of our questions, sent her home with an ace wrap and ibuprofen.", "url": "https://x.com/AJKayWriter/status/1878084198205907173" }, "later": { "status": "unknown", "date": null, "note": "No dated follow-up on the daughter or statement from xAI is established. Reports that radiologists disputed the interpretation of her X-ray remain unverified.", "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/2025-01-12-a928", "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 Improved Physical Health 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 from the cited sources.", "source": "https://x.com/AJKayWriter/status/1878084198205907173", "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/XAI_(company); https://en.wikipedia.org/wiki/X_Corp.", "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 makers' press release and the beneficiary's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0140", "catalog_version": "1.0.0", "source_fingerprint": "db416bb5f18790c7f13e09932ddb9f93d8d0c7e42da0d89ef7177a1bf402d013", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0140-source-0001", "AIBD-0140-source-0002", "AIBD-0140-source-0003", "AIBD-0140-source-0004", "AIBD-0140-source-0005", "AIBD-0140-source-0006", "AIBD-0140-source-0007", "AIBD-0140-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": "xAI", "deployer": "X Corp (Grok on the X platform)" } }, "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": "Screenshots attached to the post show Grok answering three questions about the uploaded image. It said there is “a clear fracture line visible in the distal radius”. It said the line is “a fracture line rather than a fused growth plate”. It said the break is “quite obvious” with “dorsal displacement”. Each time it advised a professional consultation. The orthopaedist's reading matched. But the mother states that she did not mention Grok to the specialist and that the diagnosis was “completely independent”. She had already asked for the X-ray prints to take to the PCP. Grok's contribution was therefore to strengthen her resolve to seek a second opinion. Everything rests on her account. No clinician, clinic or record has spoken publicly. The next day Grok told a physician who asked that it cannot diagnose medical injuries." } } }, "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": "Grok identified a possible wrist fracture in an uploaded X-ray and advised medical consultation. The mother reported that its answers strengthened her decision to seek a second opinion.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "Screenshots attached to the post show Grok answering three questions about the uploaded image. It said there is “a clear fracture line visible in the distal radius”. It said the line is “a fracture line rather than a fused growth plate”. It said the break is “quite obvious” with “dorsal displacement”. Each time it advised a professional consultation. The orthopaedist's reading matched. But the mother states that she did not mention Grok to the specialist and that the diagnosis was “completely independent”. She had already asked for the X-ray prints to take to the PCP. Grok's contribution was therefore to strengthen her resolve to seek a second opinion. Everything rests on her account. No clinician, clinic or record has spoken publicly. The next day Grok told a physician who asked that it cannot diagnose medical injuries." } } }, "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": "By her mother's account the girl's displaced distal radius fracture was set and cast within days instead of staying in an ace wrap. The orthopaedist's remark that untreated displacement would likely have needed surgery comes only from the mother. No clinician, clinic or record is public. She had already asked for the films for the PCP. Whether the outcome would have differed without Grok 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: six independent news reports, the makers' press release and the beneficiary's other. 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": "By her mother's account the girl's displaced distal radius fracture was set and cast within days instead of staying in an ace wrap. The orthopaedist's remark that untreated displacement would likely have needed surgery comes only from the mother. No clinician, clinic or record is public. She had already asked for the films for the PCP. Whether the outcome would have differed without Grok 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": "personal", "depth": 2, "reason": "By her mother's account the girl's displaced distal radius fracture was set and cast within days instead of staying in an ace wrap. The orthopaedist's remark that untreated displacement would likely have needed surgery comes only from the mother. No clinician, clinic or record is public. She had already asked for the films for the PCP. Whether the outcome would have differed without Grok 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", 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She said that the specialist reached an independent diagnosis and that she had not mentioned Grok. No clinician or medical record independently verifies the sequence.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-01-12-a928" } } }, "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 dated follow-up on the daughter or statement from xAI is established. Reports that radiologists disputed the interpretation of her X-ray 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": "An orthopedist examined the X-ray and diagnosed the fracture." }, "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-0140" } }, "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" } }, "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 11 January 2025 AJ Kay (@AJKayWriter, United States) posted about a multi-vehicle freeway crash the weekend before. An urgent-care doctor and radiologist had read her teenage daughter's wrist X-rays as free of breaks. They sent her home with an ace wrap and ibuprofen. With the hand cold and tingling and the thumb immobile, she uploaded the printed X-ray to Grok on X. It said there was “a clear fracture line in the distal radius”, not a growth plate, and called it “quite obvious”. After a PCP referral, a wrist specialist took new X-rays next day. The specialist diagnosed a distal radial head fracture with dorsal displacement and cast it, saying untreated displacement would likely have needed surgery.", "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": "By her mother's account the girl's displaced distal radius fracture was set and cast within days instead of staying in an ace wrap. The orthopaedist's remark that untreated displacement would likely have needed surgery comes only from the mother. No clinician, clinic or record is public. She had already asked for the films for the PCP. Whether the outcome would have differed without Grok 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": "Grok is xAI's general-purpose chatbot on X with image input. On 3 January 2025 Elon Musk invited users to upload “any image to Grok for analysis, from medical tests to video games”. So reading an X-ray was a promoted use rather than a designed clinical function. Grok's own replies told the user to consult a professional. When a physician asked it the next day, it said it cannot diagnose medical injuries." } }, "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": "Grok is xAI's general-purpose chatbot on X with image input. On 3 January 2025 Elon Musk invited users to upload “any image to Grok for analysis, from medical tests to video games”. So reading an X-ray was a promoted use rather than a designed clinical function. Grok's own replies told the user to consult a professional. When a physician asked it the next day, it said it cannot diagnose medical injuries." } }, "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": "2025-01-11", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2025-01-12-a928", "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 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 from the cited sources.", "source": "https://x.com/AJKayWriter/status/1878084198205907173", "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/XAI_(company); https://en.wikipedia.org/wiki/X_Corp.", "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 printed urgent-care wrist X-ray that the parent could photograph and upload", "Grok on X with image input, which Musk was publicly promoting for medical images in January 2025", "A parent who distrusted the first reading because of worsening signs (cold, tingling hand; immobile thumb; a wrist that did not look right). She had already asked for the films for the PCP", "Access within days to a PCP referral and a wrist specialist who re-imaged and examined the wrist" ], "must_hold": [ "Grok's reading was correct for this image on the mother's account only. Asked by a physician the next day, it said it cannot diagnose injuries. So its output is not reliable across users or images", "A clinician re-examined and re-imaged the wrist. Grok's output was never used as the diagnosis or the basis of treatment", "The only sources are the mother's post and news reports built from it. No clinic, clinician or record is public, and the surgery-avoidance statement is second-hand" ], "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/2025-01-12-a928", "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 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 from the cited sources.", "source": "https://x.com/AJKayWriter/status/1878084198205907173", "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/XAI_(company); https://en.wikipedia.org/wiki/X_Corp.", "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": "Healthcare - musculoskeletal radiology / fracture diagnosis", "segment": "Caregiver-initiated use of a consumer LLM" } }, "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": "other", "citation": "Kay, A. J. (2025). True story: @Grok diagnosed my daughter's broken wrist last week. Post on X, @AJKayWriter, 11 January 2025 (14:19 UTC), status 1878084198205907173; three Grok screenshots and one photo of the cast attached; about 14.4 million views.", "title": "True story: @Grok diagnosed my daughter's broken wrist last week", "url": "https://x.com/AJKayWriter/status/1878084198205907173", "date": "2025-01-11", "written_by": "beneficiary", "quote": "the wrist specialist took new x-rays (multiple views), examined her, and confirmed Grok’s diagnosis of a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "NDTV (2025). Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It. NDTV Feature, 12 January 2025.", "title": "Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It", "url": "https://www.ndtv.com/feature/woman-claims-elon-musks-grok-detected-her-daughter-s-fracture-after-doctors-fail-to-diagnose-it-7456337", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that delayed treatment could have led to surgery, but thanks to Grok's intervention, they were able to avoid this outcome and provide timely treatment." }, { "kind": "news report", "citation": "Hindustan Times (2025). Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'. Hindustan Times Trending, 12 January 2025.", "title": "Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'", "url": "https://www.hindustantimes.com/trending/mother-claims-elon-musk-s-ai-detected-daughter-s-fracture-that-doctors-missed-this-was-eye-opening-for-me-101736662763447.html", "date": "2025-01-12", "written_by": "independent", "quote": "A wrist specialist confirmed Grok’s diagnosis—a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "Mukhopadhyay, S. (2025). AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it. Mint, 12 January 2025.", "title": "AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it", "url": "https://www.livemint.com/ai/artificial-intelligence/ai-chat-helps-avoid-surgery-elon-musk-s-grok-identifies-teenage-girl-s-wrist-fracture-after-doctors-fail-to-diagnose-it-11736671754200.html", "date": "2025-01-12", "written_by": "independent", "quote": "the mother consulted a wrist specialist, who confirmed Grok’s diagnosis using additional X-rays and physical examination." }, { "kind": "news report", "citation": "India Today Trending Desk (2025). Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post. India Today, 12 January 2025.", "title": "Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post", "url": "https://www.indiatoday.in/trending-news/story/elon-musk-grok-diagnoses-fracture-missed-by-doctors-us-woman-claims-in-viral-post-2663680-2025-01-12", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that any further delay could have led to surgery, but due to Grok's timely detection, proper treatment was promptly administered." }, { "kind": "news report", "citation": "Livemint (2025). Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check. Mint, 12 January 2025. Reports Dr Cyriac Abby Philips's exchange with Grok.", "title": "Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check", "url": "https://www.livemint.com/technology/tech-news/hey-liar-elon-musk-claims-grok-ai-can-diagnose-medical-injuries-liverdoc-abby-philips-gives-him-a-reality-check-11736678451620.html", "date": "2025-01-12", "written_by": "independent", "quote": "No, I cannot diagnose medical injuries." }, { "kind": "press release", "citation": "Musk, E. (2025). Grok can diagnose medical injuries. Post on X, @elonmusk, 11 January 2025 (22:12 UTC), status 1878203372513136854; Mint reports it was posted in reply to a post about the AJ Kay account.", "title": "Grok can diagnose medical injuries", "url": "https://x.com/elonmusk/status/1878203372513136854", "date": "2025-01-11", "written_by": "makers", "quote": "Grok can diagnose medical injuries" }, { "kind": "news report", "citation": "TOI Lifestyle Desk (2025). Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman. The Times of India, 15 January 2025. Quotes Musk's 3 January 2025 post inviting image uploads.", "title": "Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman", "url": "https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/elon-musks-grok-ai-diagnoses-fracture-missed-by-doctors-claims-woman/articleshow/117255256.cms", "date": "2025-01-15", "written_by": "independent", "quote": "You can upload any image to Grok for analysis, from medical tests to video games! Just tap the + button or paste the image into the entry bar" } ] } }, "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-0140-source-0001", "AIBD-0140-source-0002", "AIBD-0140-source-0003", "AIBD-0140-source-0004", "AIBD-0140-source-0005", "AIBD-0140-source-0006", "AIBD-0140-source-0007", "AIBD-0140-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": "other", "citation": "Kay, A. J. (2025). True story: @Grok diagnosed my daughter's broken wrist last week. Post on X, @AJKayWriter, 11 January 2025 (14:19 UTC), status 1878084198205907173; three Grok screenshots and one photo of the cast attached; about 14.4 million views.", "title": "True story: @Grok diagnosed my daughter's broken wrist last week", "url": "https://x.com/AJKayWriter/status/1878084198205907173", "date": "2025-01-11", "written_by": "beneficiary", "quote": "the wrist specialist took new x-rays (multiple views), examined her, and confirmed Grok’s diagnosis of a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "NDTV (2025). Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It. NDTV Feature, 12 January 2025.", "title": "Woman Claims Elon Musk's Grok Detected Her Daughter's Fracture After Doctors Fail To Diagnose It", "url": "https://www.ndtv.com/feature/woman-claims-elon-musks-grok-detected-her-daughter-s-fracture-after-doctors-fail-to-diagnose-it-7456337", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that delayed treatment could have led to surgery, but thanks to Grok's intervention, they were able to avoid this outcome and provide timely treatment." }, { "kind": "news report", "citation": "Hindustan Times (2025). Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'. Hindustan Times Trending, 12 January 2025.", "title": "Mother claims Elon Musk's AI detected daughter's fracture that doctors missed: 'This was eye-opening for me'", "url": "https://www.hindustantimes.com/trending/mother-claims-elon-musk-s-ai-detected-daughter-s-fracture-that-doctors-missed-this-was-eye-opening-for-me-101736662763447.html", "date": "2025-01-12", "written_by": "independent", "quote": "A wrist specialist confirmed Grok’s diagnosis—a distal radial head fracture with dorsal displacement." }, { "kind": "news report", "citation": "Mukhopadhyay, S. (2025). AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it. Mint, 12 January 2025.", "title": "AI chat helps avoid surgery: Elon Musk's Grok identifies teenage girl's wrist fracture after doctors fail to diagnose it", "url": "https://www.livemint.com/ai/artificial-intelligence/ai-chat-helps-avoid-surgery-elon-musk-s-grok-identifies-teenage-girl-s-wrist-fracture-after-doctors-fail-to-diagnose-it-11736671754200.html", "date": "2025-01-12", "written_by": "independent", "quote": "the mother consulted a wrist specialist, who confirmed Grok’s diagnosis using additional X-rays and physical examination." }, { "kind": "news report", "citation": "India Today Trending Desk (2025). Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post. India Today, 12 January 2025.", "title": "Elon Musk's Grok diagnoses fracture missed by doctors, US woman claims in viral post", "url": "https://www.indiatoday.in/trending-news/story/elon-musk-grok-diagnoses-fracture-missed-by-doctors-us-woman-claims-in-viral-post-2663680-2025-01-12", "date": "2025-01-12", "written_by": "independent", "quote": "The specialist warned that any further delay could have led to surgery, but due to Grok's timely detection, proper treatment was promptly administered." }, { "kind": "news report", "citation": "Livemint (2025). Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check. Mint, 12 January 2025. Reports Dr Cyriac Abby Philips's exchange with Grok.", "title": "Elon Musk claims Grok AI can diagnose medical injuries, Indian doctor gives him a reality check", "url": "https://www.livemint.com/technology/tech-news/hey-liar-elon-musk-claims-grok-ai-can-diagnose-medical-injuries-liverdoc-abby-philips-gives-him-a-reality-check-11736678451620.html", "date": "2025-01-12", "written_by": "independent", "quote": "No, I cannot diagnose medical injuries." }, { "kind": "press release", "citation": "Musk, E. (2025). Grok can diagnose medical injuries. Post on X, @elonmusk, 11 January 2025 (22:12 UTC), status 1878203372513136854; Mint reports it was posted in reply to a post about the AJ Kay account.", "title": "Grok can diagnose medical injuries", "url": "https://x.com/elonmusk/status/1878203372513136854", "date": "2025-01-11", "written_by": "makers", "quote": "Grok can diagnose medical injuries" }, { "kind": "news report", "citation": "TOI Lifestyle Desk (2025). Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman. The Times of India, 15 January 2025. Quotes Musk's 3 January 2025 post inviting image uploads.", "title": "Elon Musk's Grok AI diagnoses fracture missed by doctors, claims woman", "url": "https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/elon-musks-grok-ai-diagnoses-fracture-missed-by-doctors-claims-woman/articleshow/117255256.cms", "date": "2025-01-15", "written_by": "independent", "quote": "You can upload any image to Grok for analysis, from medical tests to video games! Just tap the + button or paste the image into the entry bar" } ] } }, "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": "By her mother's X post of 11 January 2025, Grok flagged a displaced wrist fracture urgent care missed; an orthopaedist confirmed and cast it.", "account": "On 11 January 2025 AJ Kay (@AJKayWriter, United States) posted about a multi-vehicle freeway crash the weekend before. An urgent-care doctor and radiologist had read her teenage daughter's wrist X-rays as free of breaks. They sent her home with an ace wrap and ibuprofen. With the hand cold and tingling and the thumb immobile, she uploaded the printed X-ray to Grok on X. It said there was “a clear fracture line in the distal radius”, not a growth plate, and called it “quite obvious”. After a PCP referral, a wrist specialist took new X-rays next day. The specialist diagnosed a distal radial head fracture with dorsal displacement and cast it, saying untreated displacement would likely have needed surgery." } }, "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": "Grok analyzed an uploaded X-ray and identified a possible fracture." }, "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": "Grok 2, according to the poster." }, "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": "Grok 2, according to the poster.", "editor_note": "The poster referred to Elon Musk’s statement about Grok 2 reading medical images. That identifies a plausible model, but does not establish the model used in the posted conversation." } }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "Grok 2 (the poster cites Musk's statement that 'Grok2 could read medical images'; not confirmed by xAI)" } }, "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": "Grok chatbot (xAI) on X, image analysis of an uploaded wrist X-ray" } }, "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": "xAI" } }, "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": "Grok may have read a “clear fracture line in the distal radius” on a teenager's wrist X-ray that urgent care had cleared; a wrist specialist confirmed the displaced fracture and cast it (January 2025)", "headline": "Grok spots a displaced wrist fracture that urgent care missed" } }, "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": [ "db416bb5f18790c7f13e09932ddb9f93d8d0c7e42da0d89ef7177a1bf402d013" ], "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 11 January 2025 AJ Kay (@AJKayWriter, United States) posted about a multi-vehicle freeway crash the weekend before. An urgent-care doctor and radiologist had read her teenage daughter's wrist X-rays as free of breaks. They sent her home with an ace wrap and ibuprofen. With the hand cold and tingling and the thumb immobile, she uploaded the printed X-ray to Grok on X. It said there was “a clear fracture line in the distal radius”, not a growth plate, and called it “quite obvious”. After a PCP referral, a wrist specialist took new X-rays next day. The specialist diagnosed a distal radial head fracture with dorsal displacement and cast it, saying untreated displacement would likely have needed surgery.", "replication": { "produced_by": [ "A printed urgent-care wrist X-ray that the parent could photograph and upload", "Grok on X with image input, which Musk was publicly promoting for medical images in January 2025", "A parent who distrusted the first reading because of worsening signs (cold, tingling hand; immobile thumb; a wrist that did not look right). She had already asked for the films for the PCP", "Access within days to a PCP referral and a wrist specialist who re-imaged and examined the wrist" ], "must_hold": [ "Grok's reading was correct for this image on the mother's account only. Asked by a physician the next day, it said it cannot diagnose injuries. So its output is not reliable across users or images", "A clinician re-examined and re-imaged the wrist. Grok's output was never used as the diagnosis or the basis of treatment", "The only sources are the mother's post and news reports built from it. No clinic, clinician or record is public, and the surgery-avoidance statement is second-hand" ], "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-0350", "case_url": "records/AIBD-0350.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "PNA's capability before Skylight's real-time fishing detection", "url": "https://www.skylight.global/news/argentina-success-story" }, { "label": "After", "value": "a Speed Range Event alert, then a Fishing Event alert 40 minutes later" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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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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.", "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 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; 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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.", "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": "Fisheries enforcement / illegal, unreported and unregulated fishing", "segment": "National coastguard using a free non-profit AI platform" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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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. 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[].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$915,000+ in fines, without case-level attribution (maker's figures)", "outcome": "recurred", "source": "https://www.skylight.global/news/impact-report-2025-2026" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0087", "case_url": "records/AIBD-0087.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The CGU states the chain explicitly: “A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais.” The adjustment was made by managers after a human audit. ALICE's role was the initial alert. Developer and deployer are the same public body. The page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, { "label": "Comparison", "value": "the tender's original reference value for the engineering services (figure not stated)", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, { "label": "Before", "value": "the original reference value, not stated" }, { "label": "After", "value": "redução de cerca de R$ 100 milhões no valor de referência" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0087", "title": "CGU ALICE alert leads to a cut of about R$100 million in the reference value of a federal road-works tender (30 December 2024)", "summary": "A CGU ALICE alert on a federal road-works tender led managers to cut its reference value by about R$100 million, per CGU's 30 December 2024 results release", "date_observed": "2024-12-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In its 30 December 2024 statement on the year's results, the Controladoria-Geral da União (CGU) reported on an alert from its ALICE robot. The alert led CGU auditors to examine the budget of a federal road-works engineering tender. Federal managers then adjusted it, reducing the reference value by about R$100 million (CGU audit report 1632530). The statement names neither the contracting agency nor the date of the case. Its “this year” wording applies only to a separate water-bottle example. The same release gives 2024 aggregates: 118,069 procurement processes analyzed and 206 preventive audits opened on purchases worth R$25.57 billion. It reports R$257.25 million saved in concluded audits. ALICE reads planning documents and notices on Compras.gov.br with artificial-intelligence techniques and issues risk alerts that auditors confirm.", "category": "protective response", "unexpected": false, "purpose": "Continuous automated audit of federal procurement to flag budget and pricing risks before contracting", "system": "ALICE (Analisador de Licitações, Contratos e Editais), CGU text-mining and AI alert tool", "model_version": null, "developer": "Controladoria-Geral da União (CGU), in-house", "deployer": "Controladoria-Geral da União (CGU)", "location": null, "domain": "Public administration: government audit and procurement control", "segment": "Federal infrastructure procurement", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identifiable individual beneficiary." }, { "group": "organizational", "depth": 2, "reason": "Material to the federal budget: about R$100 million removed from a single tender's reference value, per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale and the contracting agency is not named, limiting traceability." } ], "ai_role": { "status": "assessed", "statement": "The CGU states the chain explicitly: “A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais.” The adjustment was made by managers after a human audit. ALICE's role was the initial alert. Developer and deployer are the same public body. The page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, "evidence": [ { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Same release, preceding sentence.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE (road-works example)", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "a redução de cerca de R$ 100 milhões no valor de referência em um edital para contratação de serviços de engenharia" }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025. Secondary report restating the CGU release of 30 Dec 2024; names no sources of its own.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "houve redução de aproximadamente R$ 100 milhões" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "ALICE alert on a road-works budget", "CGU auditors' examination of the budget's adequacy", "Federal managers adjusting the reference value before contracting" ], "must_hold": [ "Budget spreadsheets published with the notice", "Audit review before the tender closes", "Managers' willingness to revise estimates" ], "echoes": [ { "date": "2025-01-14", "setting": "Three Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2021-07-21", "setting": "Hospital Federal de Ipanema glove tender cut by R$2,381,663.00 after CGU audit supported by ALICE (reported 4 October 2023)", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "738", "title": "Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud", "url": "https://incidentdatabase.ai/cite/738", "severity": null, "group": "organizational", "basis": "DWP's risk-flagging algorithm, the same alert-then-investigate design as ALICE, flagged mostly legitimate claims, wasting public funds instead of saving them." }, "headline": "CGU's ALICE alert leads to a R$100 million cut in a road-works tender", "metric": "reduced the reference value by about R$100 million", "comparator": { "against": "the tender's original reference value for the engineering services (figure not stated)", "before": "the original reference value, not stated", "after": "redução de cerca de R$ 100 milhões no valor de referência", "basis": "measured", "quote": "Outro exemplo relevante dos benefícios decorrentes do uso da ferramenta foi a redução de cerca de R$ 100 milhões no valor de referência em um edital para contratação de serviços de engenharia.", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, "later": { "status": "persisted", "date": "2025-01-14", "note": "Two weeks later the CGU reported the first 2025 audit reports built on ALICE alerts: three Rio de Janeiro federal hospital tenders, one suspended, one revoked and one re-estimated.", "quote": "Nos primeiros relatórios de auditoria publicados neste ano, a ferramenta criada pela Controladoria-Geral da União gerou alertas e as equipes de auditoria avaliaram três licitações de Hospitais Federais no estado do Rio de Janeiro apontando risco de sobrepreço na compra de equipamentos e insumos para estas instituições.", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, "value": { "amount": "cerca de R$ 100 milhões", "what": "reduction in the reference value of a federal road-works engineering tender after the ALICE alert", "who": "the federal government (contracting agency not named)", "estimate": false, "quote": "Outro exemplo relevante dos benefícios decorrentes do uso da ferramenta foi a redução de cerca de R$ 100 milhões no valor de referência em um edital para contratação de serviços de engenharia.", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, "developer_country": "Brazil", "deployer_country": "Brazil", "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 738 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/738", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Dropped the 2024 dating of the road-works case from the title, summary and account and set the date observed to the 30 December 2024 release date because neither source dates the case, refiled the category as a protective response because the CGU frames the alert as avoiding unnecessary spending and the sources do not show the overestimate would have been paid, and marked the Convergência Digital article as a secondary report of the CGU release.", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "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://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao; https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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-0087", "catalog_version": "1.0.0", "source_fingerprint": "bc9414218e46c8eba70cdf284c84c6816f8ffad4af63f151028c401ac34069f1", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0087-source-0001", "AIBD-0087-source-0002", "AIBD-0087-source-0003", "AIBD-0087-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-0087-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": "Controladoria-Geral da União (CGU), in-house", "deployer": "Controladoria-Geral da União (CGU)" } }, "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 CGU states the chain explicitly: “A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais.” The adjustment was made by managers after a human audit. ALICE's role was the initial alert. Developer and deployer are the same public body. The page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." } } }, "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 CGU states the chain explicitly: “A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais.” The adjustment was made by managers after a human audit. ALICE's role was the initial alert. Developer and deployer are the same public body. The page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." } } }, "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 individual beneficiary." }, { "group": "organizational", "depth": 2, "reason": "Material to the federal budget: about R$100 million removed from a single tender's reference value, per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale and the contracting agency is not named, limiting traceability." } ] } }, "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 makers. 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https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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": [ "ALICE alert on a road-works budget", "CGU auditors' examination of the budget's adequacy", "Federal managers adjusting the reference value before contracting" ], "must_hold": [ "Budget spreadsheets published with the notice", "Audit review before the tender closes", "Managers' willingness to revise estimates" ], "echoes": [ { "date": "2025-01-14", "setting": "Three Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2021-07-21", "setting": "Hospital Federal de Ipanema glove tender cut by R$2,381,663.00 after CGU audit supported by ALICE (reported 4 October 2023)", "outcome": "recurred", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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": "Public administration: government audit and procurement control", "segment": "Federal infrastructure procurement" } }, "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": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Same release, preceding sentence.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE (road-works example)", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "a redução de cerca de R$ 100 milhões no valor de referência em um edital para contratação de serviços de engenharia" }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025. Secondary report restating the CGU release of 30 Dec 2024; names no sources of its own.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "houve redução de aproximadamente R$ 100 milhões" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "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-0087-source-0001", "AIBD-0087-source-0002", "AIBD-0087-source-0003", "AIBD-0087-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": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "A partir do alerta do ALICE, auditores da CGU examinaram a adequação de um orçamento de uma obra rodoviária, o que gerou ajustes efetuados pelos gestores federais" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Same release, preceding sentence.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE (road-works example)", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": "a redução de cerca de R$ 100 milhões no valor de referência em um edital para contratação de serviços de engenharia" }, { "kind": "news report", "citation": "Grossmann, L. O. (2025). Robô da CGU rastreia R$ 25 bilhões em licitações. Convergência Digital, 15 Jan 2025. Secondary report restating the CGU release of 30 Dec 2024; names no sources of its own.", "title": "Robô da CGU rastreia R$ 25 bilhões em licitações", "url": "https://convergenciadigital.com.br/governo/robo-da-cgu-rastreia-r-25-bilhoes-em-licitacoes/", "date": "2025-01-15", "written_by": "independent", "quote": "houve redução de aproximadamente R$ 100 milhões" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2025). Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos, que ajudam a evitar desperdícios e irregularidades. gov.br/cgu, 14 Jan 2025.", "title": "Atuação do Robô ALICE segue auxiliando auditores a realizar trabalhos preventivos", "url": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/", "date": "2025-01-14", "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": "A CGU ALICE alert on a federal road-works tender led managers to cut its reference value by about R$100 million, per CGU's 30 December 2024 results release", "account": "In its 30 December 2024 statement on the year's results, the Controladoria-Geral da União (CGU) reported on an alert from its ALICE robot. The alert led CGU auditors to examine the budget of a federal road-works engineering tender. Federal managers then adjusted it, reducing the reference value by about R$100 million (CGU audit report 1632530). The statement names neither the contracting agency nor the date of the case. Its “this year” wording applies only to a separate water-bottle example. The same release gives 2024 aggregates: 118,069 procurement processes analyzed and 206 preventive audits opened on purchases worth R$25.57 billion. It reports R$257.25 million saved in concluded audits. ALICE reads planning documents and notices on Compras.gov.br with artificial-intelligence techniques and issues risk alerts that auditors confirm." } }, "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": "ALICE (Analisador de Licitações, Contratos e Editais), CGU text-mining and AI alert tool" } }, "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": "Controladoria-Geral da União (CGU), in-house" } }, "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": "CGU ALICE alert leads to a cut of about R$100 million in the reference value of a federal road-works tender (30 December 2024)", "headline": "CGU's ALICE alert leads to a R$100 million cut in a road-works tender" } }, "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": [ "bc9414218e46c8eba70cdf284c84c6816f8ffad4af63f151028c401ac34069f1" ], "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 its 30 December 2024 statement on the year's results, the Controladoria-Geral da União (CGU) reported on an alert from its ALICE robot. The alert led CGU auditors to examine the budget of a federal road-works engineering tender. Federal managers then adjusted it, reducing the reference value by about R$100 million (CGU audit report 1632530). The statement names neither the contracting agency nor the date of the case. Its “this year” wording applies only to a separate water-bottle example. The same release gives 2024 aggregates: 118,069 procurement processes analyzed and 206 preventive audits opened on purchases worth R$25.57 billion. It reports R$257.25 million saved in concluded audits. ALICE reads planning documents and notices on Compras.gov.br with artificial-intelligence techniques and issues risk alerts that auditors confirm.", "replication": { "produced_by": [ "ALICE alert on a road-works budget", "CGU auditors' examination of the budget's adequacy", "Federal managers adjusting the reference value before contracting" ], "must_hold": [ "Budget spreadsheets published with the notice", "Audit review before the tender closes", "Managers' willingness to revise estimates" ], "echoes": [ { "date": "2025-01-14", "setting": "Three Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2021-07-21", "setting": "Hospital Federal de Ipanema glove tender cut by R$2,381,663.00 after CGU audit supported by ALICE (reported 4 October 2023)", "outcome": "recurred", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } ] } } }, "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": 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-0095", "case_url": "records/AIBD-0095.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "recipients' own situation before the transfers (GiveDirectly follow-up survey; no control group)", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, { "label": "Before", "value": "weekly income ₦4,500; 97% facing food shortages" }, { "label": "After", "value": "weekly income ₦10,000; 7% facing food shortages" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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; 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.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "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: 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-0095", "catalog_version": "1.0.0", "source_fingerprint": "98c5275b8297241b39c99ee3714e3d5dfdb3a9236527256b7a4fc10d1caf9aed", "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": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0095-source-0001", "AIBD-0095-source-0002", "AIBD-0095-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-0095-legacy-value", "DIV-0017" ], "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-0095-legacy-value", "DIV-0017" ] }, "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 (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience", "deployer": "GiveDirectly" } }, "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": "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" ], "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. 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": "$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", "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. The investigation documents that some of the poorest residents were excluded." } }, "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": "$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" }, "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": "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.", "links": [ { "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.", "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" }, "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": "structured", "data": { "answer": "$105 (source equivalent: NGN177,866 per recipient). 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. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "primary_source_checked", "sources": [ { "url": "https://www.givedirectly.org/flood-forecast-ai", "title": "How AI helped send cash just before floods hit in Nigeria", "quote": "we sent $105 to over 4,600 individuals", "date": "2025-06-27", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text" } ] }, { "id": "ngn-equivalent", "relationship": "source_equivalent", "equivalent_to": "primary", "amount": { "source_text": "NGN177,866 per recipient", "value": "177866", "qualifier": "exact", "currency": "NGN", "currency_basis": "explicit", "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. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "source_checked", "sources": [ { "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "title": "Could AI save Nigerians from devastating floods?", "quote": "177,866 naira ($105)", "date": "2024-09-21", "primary": false, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "First anticipatory payment in Kogi" } ] } ] } ], "original_resolution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "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/" } } }, "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" } } }, "provenance": { "kind": "curatorial", "basis": "Mapped from eligible Fructus statements DIV-0017. 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. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "primary_source_checked", "sources": [ { "url": "https://www.givedirectly.org/flood-forecast-ai", "title": "How AI helped send cash just before floods hit in Nigeria", "quote": "we sent $105 to over 4,600 individuals", "date": "2025-06-27", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text" } ] }, { "id": "ngn-equivalent", "relationship": "source_equivalent", "equivalent_to": "primary", "amount": { "source_text": "NGN177,866 per recipient", "value": "177866", "qualifier": "exact", "currency": "NGN", "currency_basis": "explicit", "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. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "source_checked", "sources": [ { "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "title": "Could AI save Nigerians from devastating floods?", "quote": "177,866 naira ($105)", "date": "2024-09-21", "primary": false, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "First anticipatory payment in Kogi" } ] } ] } ] } }, "provenance": { "kind": "curatorial", "basis": "Mapped from eligible Fructus statements DIV-0017. 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.", "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[].currency_price_basis": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "USD, identified from the source context. NGN, stated in the source. 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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": "Specific harm prevented.", "details": [ { "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." }, { "label": "Comparison", "value": "locating a reported wildfire without the camera's coordinates", "url": "https://www.cbsnews.com/colorado/news/ai-powered-fire-spotting-cameras-dispatch-firefighters-wildfire/" }, { "label": "Before", "value": "the process of finding, which often time with wildfires can take an hour" }, { "label": "After", "value": "get a location, an actual lat/long and drive straight through it" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "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": "Specific harm prevented.", "details": [ { "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." }, { "label": "Comparison", "value": "waiting for a 911 call from the public", "url": "https://www.firerescue1.com/artificial-intelligence/over-1-000-ai-cameras-help-spot-calif-wildfires-before-the-first-911-call" }, { "label": "Before", "value": "Nobody had called 911" }, { "label": "After", "value": "put it out before it spread beyond a 30 x 30 foot area" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "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. 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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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "members of the public calling 911", "url": "https://www.denver7.com/news/wildfire/douglas-county-wildfire-first-detected-by-cameras-equipped-with-ai" }, { "label": "Before", "value": "anyone else calling 911" }, { "label": "After", "value": "within 10 minutes of detecting smoke, 10 to 12 minutes" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. 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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/" } } }, "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-0161" } }, "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" }, 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Denver7 quotes CORE's Jordan Ambrogi saying alerts typically beat 911 calls." }, { "group": "personal", "reason": "No individual beneficiary is documented 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": "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." } }, "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": "Douglas County, Colorado, 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": "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." } }, "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": "2024-06-16", "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 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.", "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 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; 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(2024). AI-powered, fire-spotting cameras dispatch firefighters to Colorado wildfire. 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(2024). AI-powered, fire-spotting cameras dispatch firefighters to Colorado wildfire. 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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." } }, "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-0139", "case_url": "records/AIBD-0139.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The only description of what FSD did is Franklin's. Engaged by a double click of the stalk, it drove 13 miles to the VA emergency room without intervention and offered to park on arrival. No Tesla vehicle data, dashcam footage, hospital record, emergency-service log or official statement has been found. No outlet reports having interviewed him or the hospital. Tesla's corporate account and Elon Musk reposted the story on 11 April 2024 but added no facts. The photos attached to the post are two hospital-bed selfies and a night shot of a Model Y in a wet car park. They show no route, timestamp or hospital name. Some syndicated coverage garbles the story (Sky TG24 places him in Virginia and describes low blood sugar). The counterfactual, calling emergency services, driving himself or not reaching care, is unexamined. Commenters cited by Il Fatto Quotidiano argued he should have called for help." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0139", "title": "Tesla FSD v12 may have driven owner Max Paul Franklin 13 miles to a VA emergency room during an insulin-pump failure at 2 a.m. in North Carolina", "summary": "By his own X account, FSD v12 drove Max Paul Franklin 13 miles to a VA emergency room at 2 a.m. on 2 April 2024; uncorroborated.", "date_observed": "2024-04-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "By his own account, posted on X on 10 April 2024, Max Paul Franklin, a North Carolina television producer, had severe dehydration. His blood glucose read 670 at 2 a.m. on 2 April 2024 after his insulin pump malfunctioned. He says he got into his Tesla Model Y and engaged the Full Self-Driving (Supervised) v12 trial unlocked the day before. He says it drove the 13 miles to the VA emergency room without intervention and offered to park. It is the Charles George VA Medical Center, Asheville, per Il Fatto Quotidiano. He reports a mild heart attack and discharge with no restrictions. Tesla and Elon Musk reposted it on 11 April 2024. No hospital or official source has confirmed it.", "category": "protective response", "unexpected": false, "purpose": "FSD (Supervised) is built to navigate a Tesla to a chosen destination while an attentive driver supervises it. Driving Franklin to the destination he set is that function. Use by a driver impaired by a medical emergency is not an intended use. Hindustan Times notes the system depends on driver intervention when needed. A Tesla owner quoted by Tesla Oracle calls it a Level 2 system. It requires hands on the wheel and eyes on the road even when active.", "system": "Tesla Full Self-Driving (Supervised) v12 on a Tesla Model Y", "model_version": "FSD v12 (Supervised), unlocked on the car on 1 April 2024 per Franklin and Tesla Oracle; the exact build is not stated (The Driven describes the then-current release as 12.3.3)", "developer": "Tesla", "deployer": null, "location": "Asheville, United States", "domain": "Road transport / access to emergency care", "segment": "Consumer driver-assistance software on a privately owned vehicle", "benefit_type": "Improved Physical Health", "taxonomy_note": "The documented good is transport to emergency care; no health outcome is reported.", "registry_ids": { "oecd_aim": "2024-04-12-a558" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "By his own account Franklin reached a VA emergency room during an insulin-pump failure with blood glucose at 670. He was treated for a mild heart attack and left with no restrictions. No clinician, hospital or official source confirms the emergency, the drive or that his life was at risk. So the life-saving claim in the headlines is not established." } ], "ai_role": { "status": "unassessed", "statement": "The only description of what FSD did is Franklin's. Engaged by a double click of the stalk, it drove 13 miles to the VA emergency room without intervention and offered to park on arrival. No Tesla vehicle data, dashcam footage, hospital record, emergency-service log or official statement has been found. No outlet reports having interviewed him or the hospital. Tesla's corporate account and Elon Musk reposted the story on 11 April 2024 but added no facts. The photos attached to the post are two hospital-bed selfies and a night shot of a Model Y in a wet car park. They show no route, timestamp or hospital name. Some syndicated coverage garbles the story (Sky TG24 places him in Virginia and describes low blood sugar). The counterfactual, calling emergency services, driving himself or not reaching care, is unexamined. Commenters cited by Il Fatto Quotidiano argued he should have called for help." }, "evidence": [ { "kind": "other", "citation": "Franklin, M. P. (2024). My Crazy True Story. Post on X, @MAXPAULFRANKLIN, 10 April 2024 (04:38 UTC), status 1777918962870718909; four photos attached.", "title": "My Crazy True Story", "url": "https://x.com/MAXPAULFRANKLIN/status/1777918962870718909", "date": "2024-04-10", "written_by": "beneficiary", "quote": "In a moment of dire need, at 2:00 am the following morning, I found myself grappling with severe dehydration and a blood glucose level of 670" }, { "kind": "news report", "citation": "Mahapatra, T. D. (2024). Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds. Hindustan Times, 12 April 2024.", "title": "Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds", "url": "https://www.hindustantimes.com/world-news/us-news/elon-musk-responds-to-tesla-full-self-driving-that-drove-a-man-to-the-hospital-saving-his-life-101712894057291.html", "date": "2024-04-12", "written_by": "independent", "quote": "Without any intervention, the car skillfully navigated the 13-mile journey from my home to the VA Emergency Room." }, { "kind": "news report", "citation": "Bleakley, D. (2024). Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital. The Driven, 12 April 2024.", "title": "Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital", "url": "https://thedriven.io/2024/04/12/autonomous-ev-saves-man-tesla-model-y-drives-heart-attack-victim-20-kms-to-hospital/", "date": "2024-04-12", "written_by": "independent", "quote": "Despite enduring a mild heart attack, I left the hospital with no restrictions on my exercise regimen" }, { "kind": "news report", "citation": "Il Fatto Quotidiano (2024). Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita: \"Ha guidato da sola per 20 km fino al pronto soccorso\". 14 April 2024.", "title": "Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita", "url": "https://www.ilfattoquotidiano.it/2024/04/14/ha-un-infarto-e-crisi-glicemica-mentre-e-al-volante-la-sua-tesla-gli-salva-la-vita-ha-guidato-da-sola-per-20-km-fino-al-pronto-soccorso/7513224/", "date": "2024-04-14", "written_by": "independent", "quote": "ha percorso circa 20 chilometri senza interventi umani fino al Charles George Medical Center di Asheville, un centro medico per veterani" }, { "kind": "news report", "citation": "Ali, I. (2024). The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life. Tesla Oracle (enthusiast site), 12 April 2024.", "title": "The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life", "url": "https://www.teslaoracle.com/2024/04/12/the-true-story-of-how-fsd-v12-helped-a-tesla-owner-reach-hospital-and-saved-his-life/", "date": "2024-04-12", "written_by": "independent", "quote": "both Tesla's official X company account and Elon Musk reposted the story on the social media platform" }, { "kind": "news report", "citation": "Siasat Daily (2024). Tesla's full self-driving feature saves man's life in US; Musk responds. 12 April 2024 (the source listed by the OECD Monitor).", "title": "Tesla's full self-driving feature saves man's life in US; Musk responds", "url": "https://www.siasat.com/teslas-full-self-driving-feature-saves-mans-life-in-us-musk-responds-3007838", "date": "2024-04-12", "written_by": "independent", "quote": "Glad Tesla FSD was there to help and that you're feeling well!" } ], "replication": { "produced_by": [ "A Tesla Model Y with the FSD (Supervised) v12 trial active, which Franklin says Tesla had unlocked for all US cars on 1 April 2024", "A driver who, by his account, could still reach the car, engage FSD by double-clicking the stalk and set the emergency room as destination", "A 13-mile route to the VA emergency room that the system, on his account, completed without intervention and then offered to park" ], "must_hold": [ "FSD (Supervised) requires an attentive driver with hands on the wheel and eyes on the road (Hindustan Times; Tesla Oracle quoting owner James Locke). A driver too impaired to supervise would be outside its intended use", "The account is Franklin's alone. No hospital, emergency-service, police or vehicle-log record of the drive has been found. The photos attached to the post do not show the route, a timestamp or the hospital", "Emergency services remain the documented first resort. Il Fatto Quotidiano reports commenters arguing that he should have called for help" ], "echoes": [] }, "paired_incident": null, "headline": "FSD v12 may have driven its owner 13 miles to a VA emergency room", "metric": "drove Max Paul Franklin 13 miles to a VA emergency room", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No hospital, Tesla or press verification and no follow-up on Franklin's account was found after the April 2024 reposts.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-04-12-a558", "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 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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Tesla,_Inc.", "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 beneficiary's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0139", "catalog_version": "1.0.0", "source_fingerprint": "30a7d74ae7f9787d5e21a108d1b2a9e070adae84f56e12764d35c518e90f877e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0139-source-0001", "AIBD-0139-source-0002", "AIBD-0139-source-0003", "AIBD-0139-source-0004", "AIBD-0139-source-0005", "AIBD-0139-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": "Tesla" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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": "Franklin reported that Tesla Full Self-Driving drove him 13 miles to an emergency department without intervention and offered to park on arrival.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "unassessed", "statement": "The only description of what FSD did is Franklin's. Engaged by a double click of the stalk, it drove 13 miles to the VA emergency room without intervention and offered to park on arrival. No Tesla vehicle data, dashcam footage, hospital record, emergency-service log or official statement has been found. No outlet reports having interviewed him or the hospital. Tesla's corporate account and Elon Musk reposted the story on 11 April 2024 but added no facts. The photos attached to the post are two hospital-bed selfies and a night shot of a Model Y in a wet car park. They show no route, timestamp or hospital name. Some syndicated coverage garbles the story (Sky TG24 places him in Virginia and describes low blood sugar). The counterfactual, calling emergency services, driving himself or not reaching care, is unexamined. Commenters cited by Il Fatto Quotidiano argued he should have called for help." } } }, "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": "By his own account Franklin reached a VA emergency room during an insulin-pump failure with blood glucose at 670. He was treated for a mild heart attack and left with no restrictions. No clinician, hospital or official source confirms the emergency, the drive or that his life was at risk. So the life-saving claim in the headlines 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 and the beneficiary's other. 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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": "drove Max Paul Franklin 13 miles to a VA emergency room", "editor_note": "" } }, 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Tesla and Elon Musk reposted it without adding evidence. The effect of using an alternative means of reaching care was not examined.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2024-04-12-a558" } } }, "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 hospital, Tesla or press verification and no follow-up on Franklin's account was found after the April 2024 reposts." } } }, "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-0139" } }, "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": "The documented good is transport to emergency care; no health outcome 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": "By his own account, posted on X on 10 April 2024, Max Paul Franklin, a North Carolina television producer, had severe dehydration. His blood glucose read 670 at 2 a.m. on 2 April 2024 after his insulin pump malfunctioned. He says he got into his Tesla Model Y and engaged the Full Self-Driving (Supervised) v12 trial unlocked the day before. He says it drove the 13 miles to the VA emergency room without intervention and offered to park. It is the Charles George VA Medical Center, Asheville, per Il Fatto Quotidiano. He reports a mild heart attack and discharge with no restrictions. Tesla and Elon Musk reposted it on 11 April 2024. No hospital or official source has confirmed it.", "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": "By his own account Franklin reached a VA emergency room during an insulin-pump failure with blood glucose at 670. He was treated for a mild heart attack and left with no restrictions. No clinician, hospital or official source confirms the emergency, the drive or that his life was at risk. So the life-saving claim in the headlines 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": "FSD (Supervised) is built to navigate a Tesla to a chosen destination while an attentive driver supervises it. Driving Franklin to the destination he set is that function. Use by a driver impaired by a medical emergency is not an intended use. Hindustan Times notes the system depends on driver intervention when needed. A Tesla owner quoted by Tesla Oracle calls it a Level 2 system. It requires hands on the wheel and eyes on the road even when active." } }, "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": "Asheville, United States", "developer_country": "United States" } }, "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": "FSD (Supervised) is built to navigate a Tesla to a chosen destination while an attentive driver supervises it. Driving Franklin to the destination he set is that function. Use by a driver impaired by a medical emergency is not an intended use. Hindustan Times notes the system depends on driver intervention when needed. A Tesla owner quoted by Tesla Oracle calls it a Level 2 system. It requires hands on the wheel and eyes on the road even when active." } }, "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": "2024-04-02", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2024-04-12-a558", "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 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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Tesla,_Inc.", "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 Tesla Model Y with the FSD (Supervised) v12 trial active, which Franklin says Tesla had unlocked for all US cars on 1 April 2024", "A driver who, by his account, could still reach the car, engage FSD by double-clicking the stalk and set the emergency room as destination", "A 13-mile route to the VA emergency room that the system, on his account, completed without intervention and then offered to park" ], "must_hold": [ "FSD (Supervised) requires an attentive driver with hands on the wheel and eyes on the road (Hindustan Times; Tesla Oracle quoting owner James Locke). A driver too impaired to supervise would be outside its intended use", "The account is Franklin's alone. No hospital, emergency-service, police or vehicle-log record of the drive has been found. The photos attached to the post do not show the route, a timestamp or the hospital", "Emergency services remain the documented first resort. Il Fatto Quotidiano reports commenters arguing that he should have called for help" ], "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/2024-04-12-a558", "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 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": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Tesla,_Inc.", "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": "Road transport / access to emergency care", "segment": "Consumer driver-assistance software on a privately owned vehicle" } }, "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": "other", "citation": "Franklin, M. P. (2024). My Crazy True Story. Post on X, @MAXPAULFRANKLIN, 10 April 2024 (04:38 UTC), status 1777918962870718909; four photos attached.", "title": "My Crazy True Story", "url": "https://x.com/MAXPAULFRANKLIN/status/1777918962870718909", "date": "2024-04-10", "written_by": "beneficiary", "quote": "In a moment of dire need, at 2:00 am the following morning, I found myself grappling with severe dehydration and a blood glucose level of 670" }, { "kind": "news report", "citation": "Mahapatra, T. D. (2024). Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds. Hindustan Times, 12 April 2024.", "title": "Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds", "url": "https://www.hindustantimes.com/world-news/us-news/elon-musk-responds-to-tesla-full-self-driving-that-drove-a-man-to-the-hospital-saving-his-life-101712894057291.html", "date": "2024-04-12", "written_by": "independent", "quote": "Without any intervention, the car skillfully navigated the 13-mile journey from my home to the VA Emergency Room." }, { "kind": "news report", "citation": "Bleakley, D. (2024). Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital. The Driven, 12 April 2024.", "title": "Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital", "url": "https://thedriven.io/2024/04/12/autonomous-ev-saves-man-tesla-model-y-drives-heart-attack-victim-20-kms-to-hospital/", "date": "2024-04-12", "written_by": "independent", "quote": "Despite enduring a mild heart attack, I left the hospital with no restrictions on my exercise regimen" }, { "kind": "news report", "citation": "Il Fatto Quotidiano (2024). Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita: \"Ha guidato da sola per 20 km fino al pronto soccorso\". 14 April 2024.", "title": "Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita", "url": "https://www.ilfattoquotidiano.it/2024/04/14/ha-un-infarto-e-crisi-glicemica-mentre-e-al-volante-la-sua-tesla-gli-salva-la-vita-ha-guidato-da-sola-per-20-km-fino-al-pronto-soccorso/7513224/", "date": "2024-04-14", "written_by": "independent", "quote": "ha percorso circa 20 chilometri senza interventi umani fino al Charles George Medical Center di Asheville, un centro medico per veterani" }, { "kind": "news report", "citation": "Ali, I. (2024). The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life. Tesla Oracle (enthusiast site), 12 April 2024.", "title": "The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life", "url": "https://www.teslaoracle.com/2024/04/12/the-true-story-of-how-fsd-v12-helped-a-tesla-owner-reach-hospital-and-saved-his-life/", "date": "2024-04-12", "written_by": "independent", "quote": "both Tesla's official X company account and Elon Musk reposted the story on the social media platform" }, { "kind": "news report", "citation": "Siasat Daily (2024). Tesla's full self-driving feature saves man's life in US; Musk responds. 12 April 2024 (the source listed by the OECD Monitor).", "title": "Tesla's full self-driving feature saves man's life in US; Musk responds", "url": "https://www.siasat.com/teslas-full-self-driving-feature-saves-mans-life-in-us-musk-responds-3007838", "date": "2024-04-12", "written_by": "independent", "quote": "Glad Tesla FSD was there to help and that you're feeling well!" } ] } }, "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-0139-source-0001", "AIBD-0139-source-0002", "AIBD-0139-source-0003", "AIBD-0139-source-0004", "AIBD-0139-source-0005", "AIBD-0139-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": "other", "citation": "Franklin, M. P. (2024). My Crazy True Story. Post on X, @MAXPAULFRANKLIN, 10 April 2024 (04:38 UTC), status 1777918962870718909; four photos attached.", "title": "My Crazy True Story", "url": "https://x.com/MAXPAULFRANKLIN/status/1777918962870718909", "date": "2024-04-10", "written_by": "beneficiary", "quote": "In a moment of dire need, at 2:00 am the following morning, I found myself grappling with severe dehydration and a blood glucose level of 670" }, { "kind": "news report", "citation": "Mahapatra, T. D. (2024). Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds. Hindustan Times, 12 April 2024.", "title": "Tesla FSD navigated 13-mile journey to Emergency Room to save a man, Elon Musk responds", "url": "https://www.hindustantimes.com/world-news/us-news/elon-musk-responds-to-tesla-full-self-driving-that-drove-a-man-to-the-hospital-saving-his-life-101712894057291.html", "date": "2024-04-12", "written_by": "independent", "quote": "Without any intervention, the car skillfully navigated the 13-mile journey from my home to the VA Emergency Room." }, { "kind": "news report", "citation": "Bleakley, D. (2024). Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital. The Driven, 12 April 2024.", "title": "Autonomous EV saves man: Tesla Model Y drives heart attack victim 20 kms to hospital", "url": "https://thedriven.io/2024/04/12/autonomous-ev-saves-man-tesla-model-y-drives-heart-attack-victim-20-kms-to-hospital/", "date": "2024-04-12", "written_by": "independent", "quote": "Despite enduring a mild heart attack, I left the hospital with no restrictions on my exercise regimen" }, { "kind": "news report", "citation": "Il Fatto Quotidiano (2024). Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita: \"Ha guidato da sola per 20 km fino al pronto soccorso\". 14 April 2024.", "title": "Ha un infarto e crisi glicemica mentre è al volante, la sua Tesla gli salva la vita", "url": "https://www.ilfattoquotidiano.it/2024/04/14/ha-un-infarto-e-crisi-glicemica-mentre-e-al-volante-la-sua-tesla-gli-salva-la-vita-ha-guidato-da-sola-per-20-km-fino-al-pronto-soccorso/7513224/", "date": "2024-04-14", "written_by": "independent", "quote": "ha percorso circa 20 chilometri senza interventi umani fino al Charles George Medical Center di Asheville, un centro medico per veterani" }, { "kind": "news report", "citation": "Ali, I. (2024). The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life. Tesla Oracle (enthusiast site), 12 April 2024.", "title": "The true story of how FSD V12 helped a Tesla owner reach hospital and saved his life", "url": "https://www.teslaoracle.com/2024/04/12/the-true-story-of-how-fsd-v12-helped-a-tesla-owner-reach-hospital-and-saved-his-life/", "date": "2024-04-12", "written_by": "independent", "quote": "both Tesla's official X company account and Elon Musk reposted the story on the social media platform" }, { "kind": "news report", "citation": "Siasat Daily (2024). Tesla's full self-driving feature saves man's life in US; Musk responds. 12 April 2024 (the source listed by the OECD Monitor).", "title": "Tesla's full self-driving feature saves man's life in US; Musk responds", "url": "https://www.siasat.com/teslas-full-self-driving-feature-saves-mans-life-in-us-musk-responds-3007838", "date": "2024-04-12", "written_by": "independent", "quote": "Glad Tesla FSD was there to help and that you're feeling well!" } ] } }, "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": "By his own X account, FSD v12 drove Max Paul Franklin 13 miles to a VA emergency room at 2 a.m. on 2 April 2024; uncorroborated.", "account": "By his own account, posted on X on 10 April 2024, Max Paul Franklin, a North Carolina television producer, had severe dehydration. His blood glucose read 670 at 2 a.m. on 2 April 2024 after his insulin pump malfunctioned. He says he got into his Tesla Model Y and engaged the Full Self-Driving (Supervised) v12 trial unlocked the day before. He says it drove the 13 miles to the VA emergency room without intervention and offered to park. It is the Charles George VA Medical Center, Asheville, per Il Fatto Quotidiano. He reports a mild heart attack and discharge with no restrictions. Tesla and Elon Musk reposted it on 11 April 2024. No hospital or official source has confirmed it." } }, "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": "Franklin reported that the system drove his car to an emergency department." }, "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": "Franklin reported using Full Self-Driving v12 (Supervised), which was enabled on the car on 1 April 2024." }, "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": "Franklin reported using Full Self-Driving v12 (Supervised), which was enabled on the car on 1 April 2024.", "editor_note": "Franklin identified FSD v12, but not the exact build. The Driven described 12.3.3 as the release available at that time, which does not establish the build installed in his car." } }, "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": "FSD v12 (Supervised), unlocked on the car on 1 April 2024 per Franklin and Tesla Oracle; the exact build is not stated (The Driven describes the then-current release as 12.3.3)" } }, "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": "Full Self-Driving v12. The exact installed build is not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Tesla Full Self-Driving (Supervised) v12 on a Tesla Model Y" } }, "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": "Tesla" } }, "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": "Tesla FSD v12 may have driven owner Max Paul Franklin 13 miles to a VA emergency room during an insulin-pump failure at 2 a.m. in North Carolina", "headline": "FSD v12 may have driven its owner 13 miles to a VA emergency room" } }, "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": [ "30a7d74ae7f9787d5e21a108d1b2a9e070adae84f56e12764d35c518e90f877e" ], "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": "By his own account, posted on X on 10 April 2024, Max Paul Franklin, a North Carolina television producer, had severe dehydration. His blood glucose read 670 at 2 a.m. on 2 April 2024 after his insulin pump malfunctioned. He says he got into his Tesla Model Y and engaged the Full Self-Driving (Supervised) v12 trial unlocked the day before. He says it drove the 13 miles to the VA emergency room without intervention and offered to park. It is the Charles George VA Medical Center, Asheville, per Il Fatto Quotidiano. He reports a mild heart attack and discharge with no restrictions. Tesla and Elon Musk reposted it on 11 April 2024. No hospital or official source has confirmed it.", "replication": { "produced_by": [ "A Tesla Model Y with the FSD (Supervised) v12 trial active, which Franklin says Tesla had unlocked for all US cars on 1 April 2024", "A driver who, by his account, could still reach the car, engage FSD by double-clicking the stalk and set the emergency room as destination", "A 13-mile route to the VA emergency room that the system, on his account, completed without intervention and then offered to park" ], "must_hold": [ "FSD (Supervised) requires an attentive driver with hands on the wheel and eyes on the road (Hindustan Times; Tesla Oracle quoting owner James Locke). A driver too impaired to supervise would be outside its intended use", "The account is Franklin's alone. No hospital, emergency-service, police or vehicle-log record of the drive has been found. The photos attached to the post do not show the route, a timestamp or the hospital", "Emergency services remain the documented first resort. Il Fatto Quotidiano reports commenters arguing that he should have called for help" ], "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-0227", "case_url": "records/AIBD-0227.html", "reason": "Protective response, prevention unproven.", "details": [ { "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. 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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. No court record is among the sources.", "replication": { "produced_by": [ "A gun visible to a municipal security camera connected to the detector", "Human verification at the vendor's operations center", "Dispatch integration with the local police department" ], "must_hold": [ "Camera coverage of the location", "Officers available to respond while the activity continues" ], "echoes": [ { "date": "2026-05-02", "setting": "Florida State University parking garage; arrest on four charges per court documents", "outcome": "recurred", "source": "https://www.wctv.tv/2026/05/04/court-documents-19-year-old-arrested-after-brandishing-weapon-fsu-parking-garage-early-saturday-morning/" } ] } } }, "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-0379", "case_url": "records/AIBD-0379.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "911 calls from the public as the first notice of a fire", "url": "https://english.aawsat.com/node/4540976" }, { "label": "Before", "value": "not stated" }, { "label": "After", "value": "the AI beats 911 calls about 40 percent of the time" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "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. 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under a quarter acre", "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 account does not establish when a 911 call would otherwise have arrived. Figures about alerts preceding 911 calls describe the wider program rather than this fire.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2023-09-12-f6ac" } } }, "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": "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" } } }, "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": "Operators checked the alert before dispatch because dust, insects, and weather can produce false detections. Fire crews responded and contained the fire." }, "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-0120" } }, "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": "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.", "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 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" }, "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 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." } }, "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": "Mount Laguna, California, 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": "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." } }, "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": "2023-08-05", "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": [ "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 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": [ "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). 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/", "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[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "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" ], "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": "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/", "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-0138", "case_url": "records/AIBD-0138.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "From the owner's typed description and two sets of blood results, GPT-4 explained the trend in the bloodwork. When asked what else could underlie the anemia, it listed candidate causes including IMHA. The owner chose IMHA to put to a second veterinarian, who confirmed it after further tests and treated the dog. Whether that vet would have reached IMHA unprompted is unknown. The owner himself notes IMHA is a common complication of babesiosis, so the counterfactual is unproven. The owner reported that GPT-3.5 did not reach the diagnosis. He said the tool “should be a tool to be used by professionals”." }, { "label": "Comparison", "value": "the first veterinarian, who found no cause after further tests and advised waiting", "url": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3" }, { "label": "Before", "value": "the vet had no clue what it could be" }, { "label": "After", "value": "After more blood tests, \"the diagnosis was confirmed. GPT4 was right.\"" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0138", "title": "GPT-4 lists immune-mediated hemolytic anemia among causes of a Border Collie's worsening anemia; a second vet confirms and treats", "summary": "GPT-4 listed IMHA among causes of a dog's worsening anemia; a second vet confirmed and treated; the dog recovered, per the owner's thread.", "date_observed": "2023-03-25", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A Twitter user, Cooper (@peakcooper), wrote on 25 March 2023 that his Border Collie, Sassy, had been treated for babesiosis but worsened. She had pale gums and more severe anemia. The first vet found no cause and advised waiting. He entered two sets of bloodwork into ChatGPT Plus running GPT-4. It said it was not a veterinarian, then listed possible underlying causes including immune-mediated hemolytic anemia (IMHA). At a second clinic he asked about IMHA. The vet agreed after further tests, treatment began, and Cooper reported an almost full recovery. The New York Post, India Today and Business Insider reported the thread (27 to 30 March). Cooper did not respond to Insider, and no veterinarian has spoken publicly.", "category": "protective response", "unexpected": false, "purpose": "GPT-4, served through ChatGPT, is a general-purpose conversational model built to answer questions and follow instructions. Answering a diagnostic question about a dog's bloodwork is an instance of that general use, not a designed veterinary function. The model itself prefaced its answer by saying it is not a veterinarian.", "system": "ChatGPT Plus (GPT-4)", "model_version": "GPT-4", "developer": "OpenAI", "deployer": "OpenAI", "location": null, "domain": "Veterinary medicine (consumer use)", "segment": "Consumer", "benefit_type": "Improved Physical Health", "taxonomy_note": "The patient is a dog, and the owner's gain is its recovery after a candidate diagnosis the model surfaced.", "registry_ids": { "oecd_aim": "2023-03-27-6c5c" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "By the owner's own account the diagnosis led to treatment and his dog's near-full recovery. The account is self-reported and unverified by any clinician. The beneficiary is one owner and one animal." } ], "ai_role": { "status": "assessed", "statement": "From the owner's typed description and two sets of blood results, GPT-4 explained the trend in the bloodwork. When asked what else could underlie the anemia, it listed candidate causes including IMHA. The owner chose IMHA to put to a second veterinarian, who confirmed it after further tests and treated the dog. Whether that vet would have reached IMHA unprompted is unknown. The owner himself notes IMHA is a common complication of babesiosis, so the counterfactual is unproven. The owner reported that GPT-3.5 did not reach the diagnosis. He said the tool “should be a tool to be used by professionals”." }, "evidence": [ { "kind": "news report", "citation": "Steinberg, B. / New York Post (2023). ChatGPT saved my dog's life after vets couldn't figure out diagnosis. New York Post, 27 March 2023.", "title": "ChatGPT saved my dog's life after vets couldn't figure out diagnosis", "url": "https://nypost.com/2023/03/27/chatgpt-saved-my-dogs-life-after-vet-couldnt-diagnosis-it/", "date": "2023-03-27", "written_by": "independent", "quote": "Once at the second vet, Cooper asked if it could be IMHA, and after many tests, the doctor confirmed that ChatGPT4 was correct." }, { "kind": "news report", "citation": "Nguyen, B. / Business Insider (2023). Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics. Business Insider, 30 March 2023.", "title": "Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics", "url": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3", "date": "2023-03-30", "written_by": "independent", "quote": "Cooper asked the AI what other underlying issues could possibly be, and it gave him a list, including IMHA, or immune-mediated hemolytic anemia." }, { "kind": "news report", "citation": "Chakravarti, A. / India Today (2023). ChatGPT saves life of dog, diagnoses problem that even vet could not identify. India Today, 28 March 2023.", "title": "ChatGPT saves life of dog, diagnoses problem that even vet could not identify", "url": "https://www.indiatoday.in/technology/news/story/chatgpt-saves-life-of-dog-diagnoses-problem-that-even-vet-could-not-identify-2352306-2023-03-28", "date": "2023-03-28", "written_by": "independent", "quote": "it suggested that Sassy's bloodwork and symptoms could indicate immune-mediated hemolytic anaemia (IMHA)." }, { "kind": "other", "citation": "Cooper (@peakcooper) (2023). #GPT4 saved my dog's life. Twitter thread, 25 March 2023.", "title": "#GPT4 saved my dog's life (Twitter thread, first post)", "url": "https://twitter.com/peakcooper/status/1639716822680236032", "date": "2023-03-25", "written_by": "beneficiary", "quote": "#GPT4 saved my dog's life. After my dog got diagnosed with a tick-borne disease, the vet started her on the proper treatment" } ], "replication": { "produced_by": [ "The owner had two sets of actual blood results plus the babesiosis diagnosis and treatment history. He entered them, with which tests had been run, into GPT-4 via ChatGPT Plus rather than GPT-3.5.", "The first vet had no working diagnosis and advised waiting, which left the owner looking for a second opinion.", "A second veterinarian was willing to test for the owner-raised hypothesis and confirmed it before treating." ], "must_hold": [ "A licensed clinician confirms the model's suggestion with tests before any treatment is started.", "The model's output is treated as a hypothesis to raise with a clinician, not as a diagnosis, consistent with its own disclaimer.", "The underlying data entered are real laboratory results, not a lay description of symptoms alone." ], "echoes": [] }, "paired_incident": null, "headline": "GPT-4 lists IMHA among causes of a dog's anemia; vet confirms", "metric": null, "comparator": { "against": "the first veterinarian, who found no cause after further tests and advised waiting", "before": "the vet had no clue what it could be", "after": "After more blood tests, \"the diagnosis was confirmed. GPT4 was right.\"", "basis": "assumed", "quote": "After running more tests to find co-infections related to tick-borne diseases, Cooper said the tests were negative and that \"the vet had no clue what it could be.\" The vet suggested waiting to see what happened, but Cooper said that \"wasn't an acceptable answer,\" and sought a second opinion from another clinic.", "url": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3" }, "later": { "status": "unknown", "date": null, "note": "Nothing after the March 2023 coverage was found; the thread itself already reported that Sassy had made almost a full recovery.", "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. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: every published account traces to the owner's own thread, no vet statement or record has surfaced, Business Insider got no reply from the owner, and the counterfactual is unproven. GPT-4 produced a list of candidate causes that included IMHA, not a single diagnosis. The event is dated to the thread (25 March 2023); the Monitor's ID date (27 March) is the New York Post report.", "source": "https://oecd.ai/en/incidents/2023-03-27-6c5c", "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 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 from the cited sources.", "source": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3", "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: three independent news reports and the beneficiary's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0138", "catalog_version": "1.0.0", "source_fingerprint": "9752a9cc019ef4baa1ceef1adda01678b7a31e52cc30030e0291373c54164957", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0138-source-0001", "AIBD-0138-source-0002", "AIBD-0138-source-0003", "AIBD-0138-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": "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": "From the owner's typed description and two sets of blood results, GPT-4 explained the trend in the bloodwork. When asked what else could underlie the anemia, it listed candidate causes including IMHA. The owner chose IMHA to put to a second veterinarian, who confirmed it after further tests and treated the dog. Whether that vet would have reached IMHA unprompted is unknown. The owner himself notes IMHA is a common complication of babesiosis, so the counterfactual is unproven. The owner reported that GPT-3.5 did not reach the diagnosis. He said the tool “should be a tool to be used by professionals”." } } }, "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": "From the owner's typed description and two sets of blood results, GPT-4 explained the trend in the bloodwork. When asked what else could underlie the anemia, it listed candidate causes including IMHA. The owner chose IMHA to put to a second veterinarian, who confirmed it after further tests and treated the dog. Whether that vet would have reached IMHA unprompted is unknown. The owner himself notes IMHA is a common complication of babesiosis, so the counterfactual is unproven. 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}, "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-0138" } }, "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": "The patient is a dog, and the owner's gain is its recovery after a candidate diagnosis the model surfaced." } }, "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": "A Twitter user, Cooper (@peakcooper), wrote on 25 March 2023 that his Border Collie, Sassy, had been treated for babesiosis but worsened. She had pale gums and more severe anemia. The first vet found no cause and advised waiting. He entered two sets of bloodwork into ChatGPT Plus running GPT-4. It said it was not a veterinarian, then listed possible underlying causes including immune-mediated hemolytic anemia (IMHA). At a second clinic he asked about IMHA. The vet agreed after further tests, treatment began, and Cooper reported an almost full recovery. The New York Post, India Today and Business Insider reported the thread (27 to 30 March). 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The model itself prefaced its answer by saying it is not a veterinarian." } }, "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": "GPT-4, served through ChatGPT, is a general-purpose conversational model built to answer questions and follow instructions. Answering a diagnostic question about a dog's bloodwork is an instance of that general use, not a designed veterinary function. The model itself prefaced its answer by saying it is not a veterinarian." } }, "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": "2023-03-25", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources. Filed as protective response with prevention unproven rather than the classifier's specific harm prevented: every published account traces to the owner's own thread, no vet statement or record has surfaced, Business Insider got no reply from the owner, and the counterfactual is unproven. GPT-4 produced a list of candidate causes that included IMHA, not a single diagnosis. The event is dated to the thread (25 March 2023); the Monitor's ID date (27 March) is the New York Post report.", "source": "https://oecd.ai/en/incidents/2023-03-27-6c5c", "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 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 from the cited sources.", "source": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3", "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 owner had two sets of actual blood results plus the babesiosis diagnosis and treatment history. He entered them, with which tests had been run, into GPT-4 via ChatGPT Plus rather than GPT-3.5.", "The first vet had no working diagnosis and advised waiting, which left the owner looking for a second opinion.", "A second veterinarian was willing to test for the owner-raised hypothesis and confirmed it before treating." ], "must_hold": [ "A licensed clinician confirms the model's suggestion with tests before any treatment is started.", "The model's output is treated as a hypothesis to raise with a clinician, not as a diagnosis, consistent with its own disclaimer.", "The underlying data entered are real laboratory results, not a lay description of symptoms alone." ], "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. 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ChatGPT saved my dog's life after vets couldn't figure out diagnosis. New York Post, 27 March 2023.", "title": "ChatGPT saved my dog's life after vets couldn't figure out diagnosis", "url": "https://nypost.com/2023/03/27/chatgpt-saved-my-dogs-life-after-vet-couldnt-diagnosis-it/", "date": "2023-03-27", "written_by": "independent", "quote": "Once at the second vet, Cooper asked if it could be IMHA, and after many tests, the doctor confirmed that ChatGPT4 was correct." }, { "kind": "news report", "citation": "Nguyen, B. / Business Insider (2023). Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics. Business Insider, 30 March 2023.", "title": "Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics", "url": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3", "date": "2023-03-30", "written_by": "independent", "quote": "Cooper asked the AI what other underlying issues could possibly be, and it gave him a list, including IMHA, or immune-mediated hemolytic anemia." }, { "kind": "news report", "citation": "Chakravarti, A. / India Today (2023). ChatGPT saves life of dog, diagnoses problem that even vet could not identify. India Today, 28 March 2023.", "title": "ChatGPT saves life of dog, diagnoses problem that even vet could not identify", "url": "https://www.indiatoday.in/technology/news/story/chatgpt-saves-life-of-dog-diagnoses-problem-that-even-vet-could-not-identify-2352306-2023-03-28", "date": "2023-03-28", "written_by": "independent", "quote": "it suggested that Sassy's bloodwork and symptoms could indicate immune-mediated hemolytic anaemia (IMHA)." }, { "kind": "other", "citation": "Cooper (@peakcooper) (2023). #GPT4 saved my dog's life. Twitter thread, 25 March 2023.", "title": "#GPT4 saved my dog's life (Twitter thread, first post)", "url": "https://twitter.com/peakcooper/status/1639716822680236032", "date": "2023-03-25", "written_by": "beneficiary", "quote": "#GPT4 saved my dog's life. After my dog got diagnosed with a tick-borne disease, the vet started her on the proper treatment" } ] } }, "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-0138-source-0001", "AIBD-0138-source-0002", "AIBD-0138-source-0003", "AIBD-0138-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": "Steinberg, B. / New York Post (2023). ChatGPT saved my dog's life after vets couldn't figure out diagnosis. New York Post, 27 March 2023.", "title": "ChatGPT saved my dog's life after vets couldn't figure out diagnosis", "url": "https://nypost.com/2023/03/27/chatgpt-saved-my-dogs-life-after-vet-couldnt-diagnosis-it/", "date": "2023-03-27", "written_by": "independent", "quote": "Once at the second vet, Cooper asked if it could be IMHA, and after many tests, the doctor confirmed that ChatGPT4 was correct." }, { "kind": "news report", "citation": "Nguyen, B. / Business Insider (2023). Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics. Business Insider, 30 March 2023.", "title": "Twitter User Claims GPT-4 Saved Dog's Life Through Diagnostics", "url": "https://www.businessinsider.com/twitter-user-claims-gpt4-saved-dogs-life-vet-couldnt-diagnose-2023-3", "date": "2023-03-30", "written_by": "independent", "quote": "Cooper asked the AI what other underlying issues could possibly be, and it gave him a list, including IMHA, or immune-mediated hemolytic anemia." }, { "kind": "news report", "citation": "Chakravarti, A. / India Today (2023). ChatGPT saves life of dog, diagnoses problem that even vet could not identify. India Today, 28 March 2023.", "title": "ChatGPT saves life of dog, diagnoses problem that even vet could not identify", "url": "https://www.indiatoday.in/technology/news/story/chatgpt-saves-life-of-dog-diagnoses-problem-that-even-vet-could-not-identify-2352306-2023-03-28", "date": "2023-03-28", "written_by": "independent", "quote": "it suggested that Sassy's bloodwork and symptoms could indicate immune-mediated hemolytic anaemia (IMHA)." }, { "kind": "other", "citation": "Cooper (@peakcooper) (2023). #GPT4 saved my dog's life. Twitter thread, 25 March 2023.", "title": "#GPT4 saved my dog's life (Twitter thread, first post)", "url": "https://twitter.com/peakcooper/status/1639716822680236032", "date": "2023-03-25", "written_by": "beneficiary", "quote": "#GPT4 saved my dog's life. After my dog got diagnosed with a tick-borne disease, the vet started her on the proper treatment" } ] } }, "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": "GPT-4 listed IMHA among causes of a dog's worsening anemia; a second vet confirmed and treated; the dog recovered, per the owner's thread.", "account": "A Twitter user, Cooper (@peakcooper), wrote on 25 March 2023 that his Border Collie, Sassy, had been treated for babesiosis but worsened. She had pale gums and more severe anemia. The first vet found no cause and advised waiting. He entered two sets of bloodwork into ChatGPT Plus running GPT-4. It said it was not a veterinarian, then listed possible underlying causes including immune-mediated hemolytic anemia (IMHA). At a second clinic he asked about IMHA. The vet agreed after further tests, treatment began, and Cooper reported an almost full recovery. The New York Post, India Today and Business Insider reported the thread (27 to 30 March). Cooper did not respond to Insider, and no veterinarian has spoken publicly." } }, "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": "GPT-4 interpreted blood-test results and suggested possible causes of the dog’s anemia." }, "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": "GPT-4." }, "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": "GPT-4" } }, "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": "ChatGPT Plus (GPT-4)" } }, "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": "GPT-4 lists immune-mediated hemolytic anemia among causes of a Border Collie's worsening anemia; a second vet confirms and treats", "headline": "GPT-4 lists IMHA among causes of a dog's anemia; vet confirms" } }, "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": [ "9752a9cc019ef4baa1ceef1adda01678b7a31e52cc30030e0291373c54164957" ], "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 Twitter user, Cooper (@peakcooper), wrote on 25 March 2023 that his Border Collie, Sassy, had been treated for babesiosis but worsened. She had pale gums and more severe anemia. The first vet found no cause and advised waiting. He entered two sets of bloodwork into ChatGPT Plus running GPT-4. It said it was not a veterinarian, then listed possible underlying causes including immune-mediated hemolytic anemia (IMHA). At a second clinic he asked about IMHA. The vet agreed after further tests, treatment began, and Cooper reported an almost full recovery. The New York Post, India Today and Business Insider reported the thread (27 to 30 March). Cooper did not respond to Insider, and no veterinarian has spoken publicly.", "replication": { "produced_by": [ "The owner had two sets of actual blood results plus the babesiosis diagnosis and treatment history. He entered them, with which tests had been run, into GPT-4 via ChatGPT Plus rather than GPT-3.5.", "The first vet had no working diagnosis and advised waiting, which left the owner looking for a second opinion.", "A second veterinarian was willing to test for the owner-raised hypothesis and confirmed it before treating." ], "must_hold": [ "A licensed clinician confirms the model's suggestion with tests before any treatment is started.", "The model's output is treated as a hypothesis to raise with a clinician, not as a diagnosis, consistent with its own disclaimer.", "The underlying data entered are real laboratory results, not a lay description of symptoms alone." ], "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": 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-0346", "case_url": "records/AIBD-0346.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "conventional damage assessment", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "label": "Before", "value": "weeks" }, { "label": "After", "value": "hours or minutes" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. 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." } ] } }, "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, the makers' report and the makers' preprint. 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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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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-0117", "case_url": "records/AIBD-0117.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The algorithm detected the mass on the real-time MRI data and raised the alert while the patient was still in the scanner. Physicians then read the images, judged the finding consistent with a diffuse glioma and admitted her. The diagnosis itself came from the pathology report after the stereotactic biopsy. What the AI contributed is detection and speed, not a different diagnosis or treatment. The counterfactual is unproven. The authors say radiology would typically take days to report and that further investigations are needed on the advantages of earlier treatment. They give no evidence of what delay this patient would otherwise have faced. The case is reported by the team that built and deployed the algorithm, and the Anadolu Agency coverage carries that team's account. No independent clinical review is cited." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0117", "title": "Gazi University's Turkish Brain Project algorithm flags a glioma on a 46-year-old woman's brain MRI while she is still in the scanner; physicians alerted at once, stereotactic biopsy the next day", "summary": "Real-time MRI algorithm at Gazi University Hospital alerted doctors to a 46-year-old woman's glioma mid-scan; biopsy next day, Oct 2022.", "date_observed": "2022-10-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In October 2022, a 46-year-old woman with a nonspecific headache had brain magnetic resonance imaging (MRI). This was at Gazi University Faculty of Medicine Hospital, Ankara. A region-based convolutional neural network algorithm, built within the Turkish Brain Project and running on the hospital network in real time, detected an intraparenchymal mass. It alerted the research team while she was still in the scanner. Physicians read the images, judged the finding consistent with a diffuse glioma and admitted her. A stereotactic biopsy next day confirmed an isocitrate dehydrogenase wild-type diffuse glioma; she was referred to oncology for immediate treatment. The case was published 8 May 2023 and reported by Anadolu Agency 22 May 2023.", "category": "protective response", "unexpected": false, "purpose": "The Turkish Brain Project algorithm was built to detect abnormalities on brain MRI as images are acquired and warn the responsible physicians at once. So the days the authors say radiology typically needs to report, and the longer wait before treatment, are cut to days. Anadolu Agency reports it was trained on stroke, hemorrhage and tumor cases. Flagging this patient's tumor mid-scan is that intended use.", "system": "Turkish Brain Project real-time brain MRI alert system at Gazi University: a region-based convolutional neural network object-detection algorithm integrated into the hospital network, which processes MRI images in real time; Anadolu Agency reports that it evaluates a brain MRI within 5-10 seconds while the patient is still in the scanner and sends the physicians' mobile phones a message with the abnormal regions segmented, through a GSM operator's picture-message service", "model_version": null, "developer": "Gazi University, Ankara (Department of Neurosurgery and Department of Computer Engineering), within the Turkish Brain Project funded by the Presidency of the Republic of Türkiye, with the Presidency's Digital Transformation Office credited for the model's development", "deployer": "Gazi University, Department of Neurosurgery, Ankara", "location": "Ankara, Turkey", "domain": "Healthcare - neurosurgery / brain MRI", "segment": "Hospital real-time MRI anomaly alerting (academic, non-commercial)", "benefit_type": "Improved Physical Health", "taxonomy_note": "The AI's part was turnaround, an alert mid-scan and a biopsy the next day where reporting typically takes days.", "registry_ids": { "oecd_aim": "2023-05-20-a638" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "One identifiable patient had a diffuse glioma found during her scan, was biopsied the next day and referred for immediate oncological treatment. That was instead of the days the authors say radiology typically needs and the longer wait before treatment. The paper reports no follow-up and no changed outcome. The “saved a life” wording is Anadolu Agency's headline, not the authors' claim." } ], "ai_role": { "status": "assessed", "statement": "The algorithm detected the mass on the real-time MRI data and raised the alert while the patient was still in the scanner. Physicians then read the images, judged the finding consistent with a diffuse glioma and admitted her. The diagnosis itself came from the pathology report after the stereotactic biopsy. What the AI contributed is detection and speed, not a different diagnosis or treatment. The counterfactual is unproven. The authors say radiology would typically take days to report and that further investigations are needed on the advantages of earlier treatment. They give no evidence of what delay this patient would otherwise have faced. The case is reported by the team that built and deployed the algorithm, and the Anadolu Agency coverage carries that team's account. No independent clinical review is cited." }, "evidence": [ { "kind": "case report", "citation": "Sozer A, Borcek AO, Sagiroglu S, Poshtkouh A, Demirtas Z, Karaaslan MM, Kuzucu P, Celtikci E (2023). The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case. Journal of Neurosurgery: Case Lessons 5(19): CASE22536, published 8 May 2023, doi:10.3171/CASE22536, PMID 37158388, PMCID PMC10550689, CC BY-NC-ND 4.0. All authors at Gazi University; their team built and deployed the algorithm, so marked as maker-authored.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://doi.org/10.3171/CASE22536", "date": "2023-05-08", "written_by": "makers", "quote": "detected by an AI algorithm running on real-time patient data while the patient was still in the MRI scanner" }, { "kind": "case report", "citation": "Sozer A et al. (2023). Journal of Neurosurgery: Case Lessons 5(19): CASE22536 (second quotation, on the operation; the case narrative dates the alert to October 2022, the biopsy to the following day, and the pathology to an isocitrate dehydrogenase wild-type diffuse glioma). Open-access copy at PubMed Central.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10550689/", "date": "2023-05-08", "written_by": "makers", "quote": "The day after MRI, a stereotactic biopsy was performed." }, { "kind": "news report", "citation": "TRT Haber, from Anadolu Agency (2023). Tıp tarihinde ilk: Yapay zeka hayat kurtardı [A first in medical history: artificial intelligence saved a life]. TRT Haber, Health, 22 May 2023, source credited as AA; quotes Assoc. Prof. Emrah Çeltikçi and Prof. Şeref Sağıroğlu of Gazi University on the 5-10 second evaluation, the phone alerts and the six-month training.", "title": "Tıp tarihinde ilk: Yapay zeka hayat kurtardı", "url": "https://www.trthaber.com/haber/saglik/tip-tarihinde-ilk-yapay-zeka-hayat-kurtardi-769140.html", "date": "2023-05-22", "written_by": "independent", "quote": "yapay zeka algoritmasının hasta daha MR cihazındayken 5-10 saniye arasında değişen sürelerde çekilen beyin MR'ında anormal bir durum içerip içermediğini değerlendirmeyi başardığını" }, { "kind": "news report", "citation": "Habertürk, from Anadolu Ajansı (2023). Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti [Patient whose life was saved by AI's early warning enters medical history]. Habertürk, Health, 23 May 2023, byline Anadolu Ajansı; identifies the patient as a 46-year-old woman, quotes Çeltikçi on the phone alert and Sağıroğlu on over 1,000 abnormal findings reported from the hospital's three MRI scanners and a pledge to install the system free in all public hospitals.", "title": "Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti", "url": "https://www.haberturk.com/yapay-zekanin-erken-uyariyla-hayatini-kurtardigi-hasta-3593925", "date": "2023-05-23", "written_by": "independent", "quote": "Telefona baktığımızda yapay zekanın hastamızın beyninin ciddi bir kısmında beyin ödemi ve tümör tespiti yaptığına dair mesaj gördük. Doktorlarımız hemen harekete geçti ve gerekli önlemleri aldı." } ], "replication": { "produced_by": [ "A region-based convolutional neural network object-detection algorithm trained on a brain MRI data set built within the Turkish Brain Project. Anadolu Agency: six months of training on stroke, hemorrhage and tumor cases.", "Integration into the hospital network so MRI images are processed as they are acquired. Anadolu Agency reports evaluation within 5–10 seconds on the hospital's three MRI scanners.", "An alert channel to the physicians' mobile phones, reported by Anadolu Agency as a GSM operator's picture-message service carrying the segmented abnormal regions", "A neurosurgery team that reads the alert at once and can admit and biopsy the patient within a day" ], "must_hold": [ "The lesion is of a kind the model was trained on (the team names stroke, hemorrhage and tumor)", "The alert reaches a physician who acts on it before the routine radiology report", "The hospital can schedule a biopsy or operation promptly", "False alerts stay low enough that physicians keep trusting the messages. Anadolu Agency quotes Sağıroğlu that the system had reported over 1,000 abnormal findings and physicians work closely with it because they trust it." ], "echoes": [] }, "paired_incident": null, "headline": "Turkish Brain Project algorithm flags a glioma mid-scan at Gazi", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "The May 2023 Turkish press coverage reported the published case; no later development in the patient's treatment or the algorithm's deployment 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.", "source": "https://oecd.ai/en/incidents/2023-05-20-a638", "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/Gazi_University; https://en.wikipedia.org/wiki/Gazi_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 Gazi University.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and two case reports from the makers. 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Flagging this patient's tumor mid-scan 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": "2022-10-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2023-05-20-a638", "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/Gazi_University; https://en.wikipedia.org/wiki/Gazi_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 Gazi 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 region-based convolutional neural network object-detection algorithm trained on a brain MRI data set built within the Turkish Brain Project. 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Anadolu Agency quotes Sağıroğlu that the system had reported over 1,000 abnormal findings and physicians work closely with it because they trust 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/2023-05-20-a638", "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/Gazi_University; https://en.wikipedia.org/wiki/Gazi_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 Gazi 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": "Healthcare - neurosurgery / brain MRI", "segment": "Hospital real-time MRI anomaly alerting (academic, non-commercial)" } }, "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": "Sozer A, Borcek AO, Sagiroglu S, Poshtkouh A, Demirtas Z, Karaaslan MM, Kuzucu P, Celtikci E (2023). The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case. Journal of Neurosurgery: Case Lessons 5(19): CASE22536, published 8 May 2023, doi:10.3171/CASE22536, PMID 37158388, PMCID PMC10550689, CC BY-NC-ND 4.0. All authors at Gazi University; their team built and deployed the algorithm, so marked as maker-authored.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://doi.org/10.3171/CASE22536", "date": "2023-05-08", "written_by": "makers", "quote": "detected by an AI algorithm running on real-time patient data while the patient was still in the MRI scanner" }, { "kind": "case report", "citation": "Sozer A et al. (2023). Journal of Neurosurgery: Case Lessons 5(19): CASE22536 (second quotation, on the operation; the case narrative dates the alert to October 2022, the biopsy to the following day, and the pathology to an isocitrate dehydrogenase wild-type diffuse glioma). Open-access copy at PubMed Central.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10550689/", "date": "2023-05-08", "written_by": "makers", "quote": "The day after MRI, a stereotactic biopsy was performed." }, { "kind": "news report", "citation": "TRT Haber, from Anadolu Agency (2023). Tıp tarihinde ilk: Yapay zeka hayat kurtardı [A first in medical history: artificial intelligence saved a life]. TRT Haber, Health, 22 May 2023, source credited as AA; quotes Assoc. Prof. Emrah Çeltikçi and Prof. Şeref Sağıroğlu of Gazi University on the 5-10 second evaluation, the phone alerts and the six-month training.", "title": "Tıp tarihinde ilk: Yapay zeka hayat kurtardı", "url": "https://www.trthaber.com/haber/saglik/tip-tarihinde-ilk-yapay-zeka-hayat-kurtardi-769140.html", "date": "2023-05-22", "written_by": "independent", "quote": "yapay zeka algoritmasının hasta daha MR cihazındayken 5-10 saniye arasında değişen sürelerde çekilen beyin MR'ında anormal bir durum içerip içermediğini değerlendirmeyi başardığını" }, { "kind": "news report", "citation": "Habertürk, from Anadolu Ajansı (2023). Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti [Patient whose life was saved by AI's early warning enters medical history]. Habertürk, Health, 23 May 2023, byline Anadolu Ajansı; identifies the patient as a 46-year-old woman, quotes Çeltikçi on the phone alert and Sağıroğlu on over 1,000 abnormal findings reported from the hospital's three MRI scanners and a pledge to install the system free in all public hospitals.", "title": "Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti", "url": "https://www.haberturk.com/yapay-zekanin-erken-uyariyla-hayatini-kurtardigi-hasta-3593925", "date": "2023-05-23", "written_by": "independent", "quote": "Telefona baktığımızda yapay zekanın hastamızın beyninin ciddi bir kısmında beyin ödemi ve tümör tespiti yaptığına dair mesaj gördük. Doktorlarımız hemen harekete geçti ve gerekli önlemleri aldı." } ] } }, "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-0117-source-0001", "AIBD-0117-source-0002", "AIBD-0117-source-0003", "AIBD-0117-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": "case report", "citation": "Sozer A, Borcek AO, Sagiroglu S, Poshtkouh A, Demirtas Z, Karaaslan MM, Kuzucu P, Celtikci E (2023). The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case. Journal of Neurosurgery: Case Lessons 5(19): CASE22536, published 8 May 2023, doi:10.3171/CASE22536, PMID 37158388, PMCID PMC10550689, CC BY-NC-ND 4.0. All authors at Gazi University; their team built and deployed the algorithm, so marked as maker-authored.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://doi.org/10.3171/CASE22536", "date": "2023-05-08", "written_by": "makers", "quote": "detected by an AI algorithm running on real-time patient data while the patient was still in the MRI scanner" }, { "kind": "case report", "citation": "Sozer A et al. (2023). Journal of Neurosurgery: Case Lessons 5(19): CASE22536 (second quotation, on the operation; the case narrative dates the alert to October 2022, the biopsy to the following day, and the pathology to an isocitrate dehydrogenase wild-type diffuse glioma). Open-access copy at PubMed Central.", "title": "The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10550689/", "date": "2023-05-08", "written_by": "makers", "quote": "The day after MRI, a stereotactic biopsy was performed." }, { "kind": "news report", "citation": "TRT Haber, from Anadolu Agency (2023). Tıp tarihinde ilk: Yapay zeka hayat kurtardı [A first in medical history: artificial intelligence saved a life]. TRT Haber, Health, 22 May 2023, source credited as AA; quotes Assoc. Prof. Emrah Çeltikçi and Prof. Şeref Sağıroğlu of Gazi University on the 5-10 second evaluation, the phone alerts and the six-month training.", "title": "Tıp tarihinde ilk: Yapay zeka hayat kurtardı", "url": "https://www.trthaber.com/haber/saglik/tip-tarihinde-ilk-yapay-zeka-hayat-kurtardi-769140.html", "date": "2023-05-22", "written_by": "independent", "quote": "yapay zeka algoritmasının hasta daha MR cihazındayken 5-10 saniye arasında değişen sürelerde çekilen beyin MR'ında anormal bir durum içerip içermediğini değerlendirmeyi başardığını" }, { "kind": "news report", "citation": "Habertürk, from Anadolu Ajansı (2023). Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti [Patient whose life was saved by AI's early warning enters medical history]. Habertürk, Health, 23 May 2023, byline Anadolu Ajansı; identifies the patient as a 46-year-old woman, quotes Çeltikçi on the phone alert and Sağıroğlu on over 1,000 abnormal findings reported from the hospital's three MRI scanners and a pledge to install the system free in all public hospitals.", "title": "Yapay zekanın erken uyarıyla hayatını kurtardığı hasta tıp tarihine geçti", "url": "https://www.haberturk.com/yapay-zekanin-erken-uyariyla-hayatini-kurtardigi-hasta-3593925", "date": "2023-05-23", "written_by": "independent", "quote": "Telefona baktığımızda yapay zekanın hastamızın beyninin ciddi bir kısmında beyin ödemi ve tümör tespiti yaptığına dair mesaj gördük. Doktorlarımız hemen harekete geçti ve gerekli önlemleri aldı." } ] } }, "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": "Real-time MRI algorithm at Gazi University Hospital alerted doctors to a 46-year-old woman's glioma mid-scan; biopsy next day, Oct 2022.", "account": "In October 2022, a 46-year-old woman with a nonspecific headache had brain magnetic resonance imaging (MRI). This was at Gazi University Faculty of Medicine Hospital, Ankara. A region-based convolutional neural network algorithm, built within the Turkish Brain Project and running on the hospital network in real time, detected an intraparenchymal mass. It alerted the research team while she was still in the scanner. Physicians read the images, judged the finding consistent with a diffuse glioma and admitted her. A stereotactic biopsy next day confirmed an isocitrate dehydrogenase wild-type diffuse glioma; she was referred to oncology for immediate treatment. The case was published 8 May 2023 and reported by Anadolu Agency 22 May 2023." } }, "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": "Turkish Brain Project real-time brain MRI alert system at Gazi University: a region-based convolutional neural network object-detection algorithm integrated into the hospital network, which processes MRI images in real time; Anadolu Agency reports that it evaluates a brain MRI within 5-10 seconds while the patient is still in the scanner and sends the physicians' mobile phones a message with the abnormal regions segmented, through a GSM operator's picture-message 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": "Gazi University, Ankara (Department of Neurosurgery and Department of Computer Engineering), within the Turkish Brain Project funded by the Presidency of the Republic of Türkiye, with the Presidency's Digital Transformation Office credited for the model's development" } }, "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": "Gazi University's Turkish Brain Project algorithm flags a glioma on a 46-year-old woman's brain MRI while she is still in the scanner; physicians alerted at once, stereotactic biopsy the next day", "headline": "Turkish Brain Project algorithm flags a glioma mid-scan at Gazi" } }, "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": [ "f2fd1ed0ddbe798b29eb836010ea3eb5a85f89930bbcca87cfd07756f879965d" ], "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 October 2022, a 46-year-old woman with a nonspecific headache had brain magnetic resonance imaging (MRI). This was at Gazi University Faculty of Medicine Hospital, Ankara. A region-based convolutional neural network algorithm, built within the Turkish Brain Project and running on the hospital network in real time, detected an intraparenchymal mass. It alerted the research team while she was still in the scanner. Physicians read the images, judged the finding consistent with a diffuse glioma and admitted her. A stereotactic biopsy next day confirmed an isocitrate dehydrogenase wild-type diffuse glioma; she was referred to oncology for immediate treatment. The case was published 8 May 2023 and reported by Anadolu Agency 22 May 2023.", "replication": { "produced_by": [ "A region-based convolutional neural network object-detection algorithm trained on a brain MRI data set built within the Turkish Brain Project. Anadolu Agency: six months of training on stroke, hemorrhage and tumor cases.", "Integration into the hospital network so MRI images are processed as they are acquired. Anadolu Agency reports evaluation within 5–10 seconds on the hospital's three MRI scanners.", "An alert channel to the physicians' mobile phones, reported by Anadolu Agency as a GSM operator's picture-message service carrying the segmented abnormal regions", "A neurosurgery team that reads the alert at once and can admit and biopsy the patient within a day" ], "must_hold": [ "The lesion is of a kind the model was trained on (the team names stroke, hemorrhage and tumor)", "The alert reaches a physician who acts on it before the routine radiology report", "The hospital can schedule a biopsy or operation promptly", "False alerts stay low enough that physicians keep trusting the messages. Anadolu Agency quotes Sağıroğlu that the system had reported over 1,000 abnormal findings and physicians work closely with it because they trust 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": 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-0174", "case_url": "records/AIBD-0174.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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The bank supplied the account. The USD 50 million figure assumes that 10% of the unsecured exposure would have been lost; it is not a measured saving." }, "provenance": { "kind": "source_review", "basis": "The identified originals and their locators are listed in the source assessment.", "record_fields": [], "source_refs": [ "AIBD-0174-source-0001", "AIBD-0174-source-0002" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "HKMA and Deloitte describe Bank F’s experience. The bank supplied the account. 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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-0364", "case_url": "records/AIBD-0364.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "By Van Hoesen's account to Het Laatste Nieuws, relayed by the Daily Star and DH.be, he turned the wheel to avoid the boar. The car then took over, straightening the course and working against his steering. On that account the system's contribution was stabilization after a human evasive input, not the initial avoidance. Le Figaro credits Autopilot with the swerve itself, and Electrek notes Autopilot had been thought incapable of such drastic maneuvers. Musk's reply endorses the Autopilot reading but adds no data. No Tesla telemetry, police report or engineering assessment is cited." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0364", "title": "Kenneth Van Hoesen's Tesla Model 3 straightens its course after he swerves around a wild boar at 4 am on the Kazernelaan in Houthalen-Helchteren; Elon Musk calls the system superhuman (30 May 2020)", "summary": "Baker Kenneth Van Hoesen swerved around a wild boar at 4 am on 30 May 2020 in Houthalen-Helchteren; he says his Model 3 then took over and straightened the car; Musk replied on Twitter.", "date_observed": "2020-05-30", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Kenneth Van Hoesen, a Flemish baker, was driving his Tesla Model 3 on the Kazernelaan in Houthalen-Helchteren, Belgium, at 4 am on 30 May 2020. A wild boar walked into his path (Daily Star, 9 June 2020). He told Het Laatste Nieuws he pulled the steering wheel himself and the car then took over and straightened its course. He felt the wheel working against him (Daily Star; DH.be, 11 June 2020). The Daily Star says the boar was barely touched, if at all. Electrek reads the footage as a possible strike on the car's side. No injury is reported. The video, shared on Twitter by Pranay Pathole, drew Musk's reply that Autopilot's “prime directive” is not to crash (Electrek, 8 June 2020). He added that its cameras, sonar and radar make it “superhuman”. Le Figaro (12 June 2020) credited the swerve to Autopilot rather than the driver's reflexes, contrary to his own account. No Tesla data is reported.", "category": "protective response", "unexpected": false, "purpose": "Autopilot is a driver-assistance system that steers, accelerates and brakes within a lane and includes collision-avoidance features. Keeping the car under control after an evasive swerve is within that intended use as the driver and Musk describe it.", "system": "Tesla Autopilot (with the active safety features it powers)", "model_version": "Tesla Model 3 per the Daily Star; Autopilot hardware and software version not reported", "developer": "Tesla", "deployer": null, "location": "Houthalen-Helchteren, Belgium", "domain": "Road vehicle crash avoidance", "segment": "Automotive", "benefit_type": "Increased Product Safety", "taxonomy_note": "The driver's account credits the car with steadying his own swerve, and whether the boar was struck is unclear.", "registry_ids": { "oecd_aim": "2020-06-10-258f" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "One driver kept control of his car after swerving around a wild boar at 4 am and arrived unhurt. By his account the car straightened the swerve he had begun. Whether he would have lost control or struck the animal without that correction is unknown. No avoided injury or damage is measured, so a removed risk is not established." } ], "ai_role": { "status": "assessed", "statement": "By Van Hoesen's account to Het Laatste Nieuws, relayed by the Daily Star and DH.be, he turned the wheel to avoid the boar. The car then took over, straightening the course and working against his steering. On that account the system's contribution was stabilization after a human evasive input, not the initial avoidance. Le Figaro credits Autopilot with the swerve itself, and Electrek notes Autopilot had been thought incapable of such drastic maneuvers. Musk's reply endorses the Autopilot reading but adds no data. No Tesla telemetry, police report or engineering assessment is cited." }, "evidence": [ { "kind": "news report", "citation": "Jackson S (2020). Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig. Daily Star, 9 June 2020.", "title": "Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig", "url": "https://www.dailystar.co.uk/news/world-news/elon-musk-says-tesla-car-22163770", "date": "2020-06-09", "written_by": "independent", "quote": "Kenneth was driving a Tesla Model 3 in the Kazernelaan in Houthalen-Helchteren on Saturday, May 30." }, { "kind": "news report", "citation": "Lambert F (2020). Watch Tesla Autopilot avoid running over a pig in the middle of the road. Electrek, 8 June 2020.", "title": "Watch Tesla Autopilot avoid running over a pig in the middle of the road", "url": "https://electrek.co/2020/06/08/tesla-autopilot-avoid-pig-video/", "date": "2020-06-08", "written_by": "independent", "quote": "Autopilot prime directive is: Don’t crash. What seems fast to humans is slow to a computer." }, { "kind": "news report", "citation": "La DH (2020). Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: 'Le système est surhumain'. DH.be, 11 June 2020.", "title": "Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: \"Le système est surhumain\"", "url": "https://www.dhnet.be/actu/belgique/un-boulanger-belge-fait-reagir-elon-musk-en-personne-suite-a-l-exploit-de-sa-tesla-le-systeme-est-surhumain-5ee2512d9978e21bd06f5947", "date": "2020-06-11", "written_by": "independent", "quote": "J'ai tourné le volant, mais la voiture a immédiatement repris le contrôle pour me remettre sur la route" }, { "kind": "news report", "citation": "L'Avenir (2020). VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne. Lavenir.net, 10 June 2020.", "title": "VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne", "url": "https://www.lavenir.net/cnt/dmf20200610_01481837/video-un-belge-evite-un-sanglier-grace-a-sa-tesla-et-fait-reagir-elon-musk-en-personne", "date": "2020-06-10", "written_by": "independent", "quote": "Alors que ce boulanger flamand se rendait au travail vers 4h du matin, les caméras intégrées dans sa voiture ont repéré un sanglier sur la route." }, { "kind": "news report", "citation": "Le Figaro (2020). Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot. Le Figaro, 12 June 2020.", "title": "Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot", "url": "https://www.lefigaro.fr/flash-actu/le-conducteur-d-une-tesla-evite-de-justesse-un-sanglier-grace-a-l-autopilot-20200612", "date": "2020-06-12", "written_by": "independent", "quote": "a pu faire un important écart non pas grâce aux réflexes du conducteur, mais grâce au système de pilotage automatique de la Tesla" }, { "kind": "registry label", "citation": "OECD.AI (2020). Tesla Autopilot Prevents Collision with Wild Boar in Belgium. OECD AI Incidents and Hazards Monitor, entry 2020-06-10-258f.", "title": "Tesla Autopilot Prevents Collision with Wild Boar in Belgium", "url": "https://oecd.ai/en/incidents/2020-06-10-258f", "date": null, "written_by": "independent", "quote": "A Belgian baker narrowly avoided a collision with a wild boar when his Tesla's Autopilot system intervened, taking control to steer the car away from the animal." } ], "replication": { "produced_by": [ "A Tesla with Autopilot engaged, by the driver's account, on an unlit road at about 4 am, with its cameras recording from four sides", "A sudden steering input by the driver to avoid an animal, after which the car's control systems straighten the course" ], "must_hold": [ "The road is otherwise empty, so the swerve and the correction stay within the carriageway", "The driver does not fight the correction. Van Hoesen said he felt the wheel working against him", "The animal is small enough, or far enough away, to be missed or only grazed" ], "echoes": [] }, "paired_incident": null, "headline": "Tesla Model 3 steadies a Belgian baker's swerve around a wild boar", "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/2020-06-10-258f", "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://en.wikipedia.org/wiki/Tesla,_Inc.", "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 a registry entry. 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Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig. Daily Star, 9 June 2020.", "title": "Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig", "url": "https://www.dailystar.co.uk/news/world-news/elon-musk-says-tesla-car-22163770", "date": "2020-06-09", "written_by": "independent", "quote": "Kenneth was driving a Tesla Model 3 in the Kazernelaan in Houthalen-Helchteren on Saturday, May 30." }, { "kind": "news report", "citation": "Lambert F (2020). Watch Tesla Autopilot avoid running over a pig in the middle of the road. Electrek, 8 June 2020.", "title": "Watch Tesla Autopilot avoid running over a pig in the middle of the road", "url": "https://electrek.co/2020/06/08/tesla-autopilot-avoid-pig-video/", "date": "2020-06-08", "written_by": "independent", "quote": "Autopilot prime directive is: Don’t crash. What seems fast to humans is slow to a computer." }, { "kind": "news report", "citation": "La DH (2020). Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: 'Le système est surhumain'. DH.be, 11 June 2020.", "title": "Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: \"Le système est surhumain\"", "url": "https://www.dhnet.be/actu/belgique/un-boulanger-belge-fait-reagir-elon-musk-en-personne-suite-a-l-exploit-de-sa-tesla-le-systeme-est-surhumain-5ee2512d9978e21bd06f5947", "date": "2020-06-11", "written_by": "independent", "quote": "J'ai tourné le volant, mais la voiture a immédiatement repris le contrôle pour me remettre sur la route" }, { "kind": "news report", "citation": "L'Avenir (2020). VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne. Lavenir.net, 10 June 2020.", "title": "VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne", "url": "https://www.lavenir.net/cnt/dmf20200610_01481837/video-un-belge-evite-un-sanglier-grace-a-sa-tesla-et-fait-reagir-elon-musk-en-personne", "date": "2020-06-10", "written_by": "independent", "quote": "Alors que ce boulanger flamand se rendait au travail vers 4h du matin, les caméras intégrées dans sa voiture ont repéré un sanglier sur la route." }, { "kind": "news report", "citation": "Le Figaro (2020). Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot. Le Figaro, 12 June 2020.", "title": "Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot", "url": "https://www.lefigaro.fr/flash-actu/le-conducteur-d-une-tesla-evite-de-justesse-un-sanglier-grace-a-l-autopilot-20200612", "date": "2020-06-12", "written_by": "independent", "quote": "a pu faire un important écart non pas grâce aux réflexes du conducteur, mais grâce au système de pilotage automatique de la Tesla" }, { "kind": "registry label", "citation": "OECD.AI (2020). Tesla Autopilot Prevents Collision with Wild Boar in Belgium. OECD AI Incidents and Hazards Monitor, entry 2020-06-10-258f.", "title": "Tesla Autopilot Prevents Collision with Wild Boar in Belgium", "url": "https://oecd.ai/en/incidents/2020-06-10-258f", "written_by": "independent", "quote": "A Belgian baker narrowly avoided a collision with a wild boar when his Tesla's Autopilot system intervened, taking control to steer the car away from the animal." } ] } }, "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-0364-source-0001", "AIBD-0364-source-0002", "AIBD-0364-source-0003", "AIBD-0364-source-0004", "AIBD-0364-source-0005", "AIBD-0364-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": "Jackson S (2020). Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig. Daily Star, 9 June 2020.", "title": "Elon Musk says Tesla car is 'superhuman' after it automatically swerves pig", "url": "https://www.dailystar.co.uk/news/world-news/elon-musk-says-tesla-car-22163770", "date": "2020-06-09", "written_by": "independent", "quote": "Kenneth was driving a Tesla Model 3 in the Kazernelaan in Houthalen-Helchteren on Saturday, May 30." }, { "kind": "news report", "citation": "Lambert F (2020). Watch Tesla Autopilot avoid running over a pig in the middle of the road. Electrek, 8 June 2020.", "title": "Watch Tesla Autopilot avoid running over a pig in the middle of the road", "url": "https://electrek.co/2020/06/08/tesla-autopilot-avoid-pig-video/", "date": "2020-06-08", "written_by": "independent", "quote": "Autopilot prime directive is: Don’t crash. What seems fast to humans is slow to a computer." }, { "kind": "news report", "citation": "La DH (2020). Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: 'Le système est surhumain'. DH.be, 11 June 2020.", "title": "Un boulanger belge fait réagir Elon Musk en personne suite à l'exploit de sa Tesla: \"Le système est surhumain\"", "url": "https://www.dhnet.be/actu/belgique/un-boulanger-belge-fait-reagir-elon-musk-en-personne-suite-a-l-exploit-de-sa-tesla-le-systeme-est-surhumain-5ee2512d9978e21bd06f5947", "date": "2020-06-11", "written_by": "independent", "quote": "J'ai tourné le volant, mais la voiture a immédiatement repris le contrôle pour me remettre sur la route" }, { "kind": "news report", "citation": "L'Avenir (2020). VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne. Lavenir.net, 10 June 2020.", "title": "VIDÉO | Un Belge évite un sanglier grâce à sa Tesla et fait réagir Elon Musk en personne", "url": "https://www.lavenir.net/cnt/dmf20200610_01481837/video-un-belge-evite-un-sanglier-grace-a-sa-tesla-et-fait-reagir-elon-musk-en-personne", "date": "2020-06-10", "written_by": "independent", "quote": "Alors que ce boulanger flamand se rendait au travail vers 4h du matin, les caméras intégrées dans sa voiture ont repéré un sanglier sur la route." }, { "kind": "news report", "citation": "Le Figaro (2020). Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot. Le Figaro, 12 June 2020.", "title": "Le conducteur d'une Tesla évite de justesse un sanglier grâce à l'Autopilot", "url": "https://www.lefigaro.fr/flash-actu/le-conducteur-d-une-tesla-evite-de-justesse-un-sanglier-grace-a-l-autopilot-20200612", "date": "2020-06-12", "written_by": "independent", "quote": "a pu faire un important écart non pas grâce aux réflexes du conducteur, mais grâce au système de pilotage automatique de la Tesla" }, { "kind": "registry label", "citation": "OECD.AI (2020). Tesla Autopilot Prevents Collision with Wild Boar in Belgium. OECD AI Incidents and Hazards Monitor, entry 2020-06-10-258f.", "title": "Tesla Autopilot Prevents Collision with Wild Boar in Belgium", "url": "https://oecd.ai/en/incidents/2020-06-10-258f", "written_by": "independent", "quote": "A Belgian baker narrowly avoided a collision with a wild boar when his Tesla's Autopilot system intervened, taking control to steer the car away from the animal." } ] } }, "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": "Baker Kenneth Van Hoesen swerved around a wild boar at 4 am on 30 May 2020 in Houthalen-Helchteren; he says his Model 3 then took over and straightened the car; Musk replied on Twitter.", "account": "Kenneth Van Hoesen, a Flemish baker, was driving his Tesla Model 3 on the Kazernelaan in Houthalen-Helchteren, Belgium, at 4 am on 30 May 2020. A wild boar walked into his path (Daily Star, 9 June 2020). He told Het Laatste Nieuws he pulled the steering wheel himself and the car then took over and straightened its course. He felt the wheel working against him (Daily Star; DH.be, 11 June 2020). The Daily Star says the boar was barely touched, if at all. Electrek reads the footage as a possible strike on the car's side. No injury is reported. The video, shared on Twitter by Pranay Pathole, drew Musk's reply that Autopilot's “prime directive” is not to crash (Electrek, 8 June 2020). He added that its cameras, sonar and radar make it “superhuman”. Le Figaro (12 June 2020) credited the swerve to Autopilot rather than the driver's reflexes, contrary to his own account. No Tesla data is 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": "value_present", "value": { "kind": "text", "text": "The driver reported that the system stabilized the car after his evasive steering." }, "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 report identified a Tesla Model 3, without an installed Autopilot release." }, "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": "Tesla Model 3 per the Daily Star; Autopilot hardware and software 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": "Tesla Autopilot (with the active safety features it powers)" } }, "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": "Tesla" } }, "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": "Kenneth Van Hoesen's Tesla Model 3 straightens its course after he swerves around a wild boar at 4 am on the Kazernelaan in Houthalen-Helchteren; Elon Musk calls the system superhuman (30 May 2020)", "headline": "Tesla Model 3 steadies a Belgian baker's swerve around a wild boar" } }, "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": [ "2bfa51f58b9122fd96cecb438d131bb83ee05d2aa9884d384e4470f0042b1c49" ], "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": "Kenneth Van Hoesen, a Flemish baker, was driving his Tesla Model 3 on the Kazernelaan in Houthalen-Helchteren, Belgium, at 4 am on 30 May 2020. A wild boar walked into his path (Daily Star, 9 June 2020). He told Het Laatste Nieuws he pulled the steering wheel himself and the car then took over and straightened its course. He felt the wheel working against him (Daily Star; DH.be, 11 June 2020). The Daily Star says the boar was barely touched, if at all. Electrek reads the footage as a possible strike on the car's side. No injury is reported. The video, shared on Twitter by Pranay Pathole, drew Musk's reply that Autopilot's “prime directive” is not to crash (Electrek, 8 June 2020). He added that its cameras, sonar and radar make it “superhuman”. Le Figaro (12 June 2020) credited the swerve to Autopilot rather than the driver's reflexes, contrary to his own account. No Tesla data is reported.", "replication": { "produced_by": [ "A Tesla with Autopilot engaged, by the driver's account, on an unlit road at about 4 am, with its cameras recording from four sides", "A sudden steering input by the driver to avoid an animal, after which the car's control systems straighten the course" ], "must_hold": [ "The road is otherwise empty, so the swerve and the correction stay within the carriageway", "The driver does not fight the correction. Van Hoesen said he felt the wheel working against him", "The animal is small enough, or far enough away, to be missed or only grazed" ], "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-0109", "case_url": "records/AIBD-0109.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "By Peters's account Autopilot braked and began a lane change to the right before he had seen the oncoming car. The TeslaCam footage, as described by Electrek and the Daily Mail, shows the Tesla moving right while the wrong-way car veers off-road. The claim rests on the driver's statements and the footage. Neither Tesla nor police is quoted, and the oncoming driver's own swerve means the system's action cannot be shown to have been decisive." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0109", "title": "Tesla Model S on Autopilot braked and steered right to avoid a wrong-way driver on Highway 49 in Cabarrus County, North Carolina, before driver Bret Peters had reacted", "summary": "Bret Peters's Model S on Autopilot braked and moved right as a wrong-way car came at him on Highway 49, NC; TeslaCam recorded the near miss.", "date_observed": "2020-02-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Before dawn on 27 February 2020, Bret Peters, a father and former state trooper, was driving his 2020 Tesla Model S on Autopilot. He was on Highway 49 near Cabarrus Arena in Concord, Cabarrus County, North Carolina. A car traveling the wrong way at about 60 mph came toward him in his lane. He wrote on Facebook that he did not see it until after Autopilot hit the brakes and started steering away. TeslaCam footage, aired by Fox 46 Charlotte and described by Electrek and the Daily Mail, shows the Tesla moving into the right lane. The oncoming car swerves onto the grassy median. There was no collision, and no police or Tesla statement is reported.", "category": "protective response", "unexpected": false, "purpose": "Autopilot is a driver-assistance system that steers, accelerates and brakes within a lane and includes collision-avoidance features. Braking and moving away from an oncoming vehicle is within that intended use as the driver describes it.", "system": "Tesla Autopilot on a 2020 Model S, with TeslaCam dashcam recording", "model_version": "2020 Tesla Model S per the Daily Mail; Autopilot software version not reported", "developer": "Tesla", "deployer": null, "location": "Concord, North Carolina, United States", "domain": "Road vehicle crash avoidance", "segment": "Automotive", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2020-03-07-752c" }, "ratings": [ { "group": "personal", "depth": 2, "reason": "By his account and the footage as described, one driver was moved out of a wrong-way car's path at about 60 mph before he reacted. The other driver also swerved onto the median, so whether a collision would have occurred is unknown. No avoided injury or damage can be measured." } ], "ai_role": { "status": "assessed", "statement": "By Peters's account Autopilot braked and began a lane change to the right before he had seen the oncoming car. The TeslaCam footage, as described by Electrek and the Daily Mail, shows the Tesla moving right while the wrong-way car veers off-road. The claim rests on the driver's statements and the footage. Neither Tesla nor police is quoted, and the oncoming driver's own swerve means the system's action cannot be shown to have been decisive." }, "evidence": [ { "kind": "news report", "citation": "Edmonds L, Pero J (2020). Incredible moment Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway. Daily Mail, 9 March 2020, quoting Peters's Facebook post and his interview with Fox 46 Charlotte.", "title": "Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway", "url": "https://www.dailymail.co.uk/news/article-8092899/Model-S-Tesla-swerves-Autopilot-avoid-wrong-way-driver-dark-North-Carolina-highway.html", "date": "2020-03-09", "written_by": "independent", "quote": "The car coming at me was masked by the cultural lighting and I didn't see them until AFTER Autopilot hit the brakes and started steering away from the oncoming car." }, { "kind": "news report", "citation": "Lambert F (2020). Tesla Autopilot automatically avoids head-on collision with car in wrong lane. Electrek, 9 March 2020, embedding the Fox 46 Charlotte report and placing the event 'last week'.", "title": "Tesla Autopilot automatically avoids head-on collision with car in wrong lane", "url": "https://electrek.co/2020/03/09/tesla-autopilot-automatically-avoids-head-on-collision-car-wrong-lane/", "date": "2020-03-09", "written_by": "independent", "quote": "it shows that the vehicle in the wrong lane started swerving off-road as it was getting closer to the Tesla, but the latter also moved toward the right lane" }, { "kind": "news report", "citation": "Crider J (2020). 'Dear Tesla & Elon Musk.. Because Of You I Won't Be Going To A Funeral'. CleanTechnica, 8 March 2020, relaying a since-deleted Reddit post and the Fox 46 Charlotte report.", "title": "'Dear Tesla & Elon Musk ... Because Of You I Won't Be Going To A Funeral'", "url": "https://cleantechnica.com/2020/03/08/dear-tesla-elon-musk-because-of-you-i-wont-be-going-to-a-funeral/", "date": "2020-03-08", "written_by": "independent", "quote": "Before I could put my foot on the brake, it had already started changing lanes" } ], "replication": { "produced_by": [ "Autopilot engaged on a divided highway at night, with an adjacent lane the system has registered as empty", "An oncoming vehicle entering the Tesla's lane within range of its forward cameras and radar", "Automatic braking and a lane change executed before the driver reacts" ], "must_hold": [ "A clear adjacent lane exists; Peters said the car knew nobody was to his right", "The oncoming vehicle is detected early enough at a closing speed of around 60 mph plus the Tesla's own", "The driver does not override the maneuver" ], "echoes": [] }, "paired_incident": null, "headline": "Tesla Autopilot brakes and steers clear of a wrong-way driver", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "No police, Tesla or follow-up report on the incident was found in the cited sources.", "quote": null, "url": null }, "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 OECD Monitor entry and the cited sources.", "source": "https://oecd.ai/en/incidents/2020-03-07-752c", "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://en.wikipedia.org/wiki/Tesla,_Inc.", "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-0109", "catalog_version": "1.0.0", "source_fingerprint": "7cf9b39d840930f0bfc162270db094f304c2a731033a0bf0a7b5e2df9de54356", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0109-source-0001", "AIBD-0109-source-0002", "AIBD-0109-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": "Tesla" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer" ], "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 Peters's account Autopilot braked and began a lane change to the right before he had seen the oncoming car. The TeslaCam footage, as described by Electrek and the Daily Mail, shows the Tesla moving right while the wrong-way car veers off-road. The claim rests on the driver's statements and the footage. Neither Tesla nor police is quoted, and the oncoming driver's own swerve means the system's action cannot be shown to have been decisive." } } }, "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": "By Peters's account Autopilot braked and began a lane change to the right before he had seen the oncoming car. The TeslaCam footage, as described by Electrek and the Daily Mail, shows the Tesla moving right while the wrong-way car veers off-road. The claim rests on the driver's statements and the footage. Neither Tesla nor police is quoted, and the oncoming driver's own swerve means the system's action cannot be shown to have been decisive." } } }, "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": "By his account and the footage as described, one driver was moved out of a wrong-way car's path at about 60 mph before he reacted. 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Incredible moment Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway. Daily Mail, 9 March 2020, quoting Peters's Facebook post and his interview with Fox 46 Charlotte.", "title": "Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway", "url": "https://www.dailymail.co.uk/news/article-8092899/Model-S-Tesla-swerves-Autopilot-avoid-wrong-way-driver-dark-North-Carolina-highway.html", "date": "2020-03-09", "written_by": "independent", "quote": "The car coming at me was masked by the cultural lighting and I didn't see them until AFTER Autopilot hit the brakes and started steering away from the oncoming car." }, { "kind": "news report", "citation": "Lambert F (2020). Tesla Autopilot automatically avoids head-on collision with car in wrong lane. Electrek, 9 March 2020, embedding the Fox 46 Charlotte report and placing the event 'last week'.", "title": "Tesla Autopilot automatically avoids head-on collision with car in wrong lane", "url": "https://electrek.co/2020/03/09/tesla-autopilot-automatically-avoids-head-on-collision-car-wrong-lane/", "date": "2020-03-09", "written_by": "independent", "quote": "it shows that the vehicle in the wrong lane started swerving off-road as it was getting closer to the Tesla, but the latter also moved toward the right lane" }, { "kind": "news report", "citation": "Crider J (2020). 'Dear Tesla & Elon Musk.. Because Of You I Won't Be Going To A Funeral'. CleanTechnica, 8 March 2020, relaying a since-deleted Reddit post and the Fox 46 Charlotte report.", "title": "'Dear Tesla & Elon Musk ... Because Of You I Won't Be Going To A Funeral'", "url": "https://cleantechnica.com/2020/03/08/dear-tesla-elon-musk-because-of-you-i-wont-be-going-to-a-funeral/", "date": "2020-03-08", "written_by": "independent", "quote": "Before I could put my foot on the brake, it had already started changing lanes" } ] } }, "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-0109-source-0001", "AIBD-0109-source-0002", "AIBD-0109-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": "Edmonds L, Pero J (2020). Incredible moment Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway. Daily Mail, 9 March 2020, quoting Peters's Facebook post and his interview with Fox 46 Charlotte.", "title": "Model S Tesla swerves on Autopilot to avoid wrong-way driver on a dark North Carolina highway", "url": "https://www.dailymail.co.uk/news/article-8092899/Model-S-Tesla-swerves-Autopilot-avoid-wrong-way-driver-dark-North-Carolina-highway.html", "date": "2020-03-09", "written_by": "independent", "quote": "The car coming at me was masked by the cultural lighting and I didn't see them until AFTER Autopilot hit the brakes and started steering away from the oncoming car." }, { "kind": "news report", "citation": "Lambert F (2020). Tesla Autopilot automatically avoids head-on collision with car in wrong lane. Electrek, 9 March 2020, embedding the Fox 46 Charlotte report and placing the event 'last week'.", "title": "Tesla Autopilot automatically avoids head-on collision with car in wrong lane", "url": "https://electrek.co/2020/03/09/tesla-autopilot-automatically-avoids-head-on-collision-car-wrong-lane/", "date": "2020-03-09", "written_by": "independent", "quote": "it shows that the vehicle in the wrong lane started swerving off-road as it was getting closer to the Tesla, but the latter also moved toward the right lane" }, { "kind": "news report", "citation": "Crider J (2020). 'Dear Tesla & Elon Musk.. Because Of You I Won't Be Going To A Funeral'. CleanTechnica, 8 March 2020, relaying a since-deleted Reddit post and the Fox 46 Charlotte report.", "title": "'Dear Tesla & Elon Musk ... Because Of You I Won't Be Going To A Funeral'", "url": "https://cleantechnica.com/2020/03/08/dear-tesla-elon-musk-because-of-you-i-wont-be-going-to-a-funeral/", "date": "2020-03-08", "written_by": "independent", "quote": "Before I could put my foot on the brake, it had already started changing lanes" } ] } }, "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": "Bret Peters's Model S on Autopilot braked and moved right as a wrong-way car came at him on Highway 49, NC; TeslaCam recorded the near miss.", "account": "Before dawn on 27 February 2020, Bret Peters, a father and former state trooper, was driving his 2020 Tesla Model S on Autopilot. He was on Highway 49 near Cabarrus Arena in Concord, Cabarrus County, North Carolina. A car traveling the wrong way at about 60 mph came toward him in his lane. He wrote on Facebook that he did not see it until after Autopilot hit the brakes and started steering away. TeslaCam footage, aired by Fox 46 Charlotte and described by Electrek and the Daily Mail, shows the Tesla moving into the right lane. The oncoming car swerves onto the grassy median. There was no collision, and no police or Tesla statement is 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": "value_present", "value": { "kind": "text", "text": "The driver reported that Autopilot braked and began changing lanes." }, "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 report identified a 2020 Tesla Model S, without an installed Autopilot release." }, "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": "2020 Tesla Model S per the Daily Mail; Autopilot software 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": "Tesla Autopilot on a 2020 Model S, with TeslaCam dashcam recording" } }, "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": "Tesla" } }, "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": "Tesla Model S on Autopilot braked and steered right to avoid a wrong-way driver on Highway 49 in Cabarrus County, North Carolina, before driver Bret Peters had reacted", "headline": "Tesla Autopilot brakes and steers clear of a wrong-way driver" } }, "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": [ "7cf9b39d840930f0bfc162270db094f304c2a731033a0bf0a7b5e2df9de54356" ], "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": "Before dawn on 27 February 2020, Bret Peters, a father and former state trooper, was driving his 2020 Tesla Model S on Autopilot. He was on Highway 49 near Cabarrus Arena in Concord, Cabarrus County, North Carolina. A car traveling the wrong way at about 60 mph came toward him in his lane. He wrote on Facebook that he did not see it until after Autopilot hit the brakes and started steering away. TeslaCam footage, aired by Fox 46 Charlotte and described by Electrek and the Daily Mail, shows the Tesla moving into the right lane. The oncoming car swerves onto the grassy median. There was no collision, and no police or Tesla statement is reported.", "replication": { "produced_by": [ "Autopilot engaged on a divided highway at night, with an adjacent lane the system has registered as empty", "An oncoming vehicle entering the Tesla's lane within range of its forward cameras and radar", "Automatic braking and a lane change executed before the driver reacts" ], "must_hold": [ "A clear adjacent lane exists; Peters said the car knew nobody was to his right", "The oncoming vehicle is detected early enough at a closing speed of around 60 mph plus the Tesla's own", "The driver does not override the maneuver" ], "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": 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-0223", "case_url": "records/AIBD-0223.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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-0331", "case_url": "records/AIBD-0331.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "Alice's role was detection. Per Inforchannel it pulls the estimated value from notice text, runs nine text analyses for competitiveness restrictions and 23 database cross-checks. The auditor's statement and the JOTA article attribute the discovery of the BR-153 notices to Alice. The cancellation was a decision of the TCU plenary (Acórdão 1116/2017). Both sources rest on TCU staff statements, the deployer describing its own in-house tool. The source counts as independent because TCU is a public body, not a vendor. The acórdão text was not obtained, so the two accounts' differing figures (R$39 million, two notices; R$20 million, one notice) cannot be reconciled." }, { "label": "Comparison", "value": "TCU auditors reading the roughly 200 federal tender notices published each day by hand", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, { "label": "Before", "value": "seria muito custoso porque são, em média, 200 editais por dia" }, { "label": "After", "value": "Os auditores do Tribunal de Contas da União recebem pontualmente às 19h um e-mail de Alice. São os resumos das centenas de contratações federais publicadas naquele dia." }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0331", "title": "TCU's Alice robot flags DNIT notices for BR-153 maintenance on stretches already under concession; TCU Acórdão 1116/2017 cancels the tender", "summary": "Alice flagged DNIT Goiás notices for BR-153 maintenance on concession stretches (R$39m per Inforchannel); TCU Acórdão 1116/2017 canceled the R$20m tender (JOTA).", "date_observed": "2017-09-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Alice (Análise de Licitações e Editais) is the Tribunal de Contas da União's robot. It reads every tender notice and auction record published on Comprasnet and the Diário Oficial da União. It had been running for about ten months when TCU presented it at a TCU–CGU data-analysis seminar on 25–26 September 2017. Inforchannel (27 September 2017), quoting TCU auditor Valéria Nakano, reported that in Goiás the court, via Alice, found DNIT's state unit had published two notices. They were worth R$39 million for maintenance of the BR-153 on stretches under concession, where maintenance falls to the concessionaire. Within two months an acórdão blocked both tenders. A JOTA article of 22 November 2017 states that Alice was decisive in Acórdão-TCU 1.116/2017 (rapporteur Minister Walton Alencar Rodrigues). It ordered cancellation of the BR-153 tender budgeted at R$20 million. The two sources give different numbers of notices and amounts. The acórdão itself was not available. The decision date is not reported.", "category": "harm prevented", "unexpected": false, "purpose": "Daily automated screening of federal tender notices for signs of irregularity so that auditors can act during the public phase of a tender. This use was its intended purpose.", "system": "Alice (Análise de Licitações e Editais), TCU/CGU text-analysis and data cross-checking robot", "model_version": null, "developer": "Tribunal de Contas da União (TCU) with the Ministério da Transparência e Controladoria-Geral da União (CGU), in-house", "deployer": "Tribunal de Contas da União (TCU), Goiás unit", "location": "Goiás, Brazil (BR-153 highway)", "domain": "Public administration: external audit and procurement control", "segment": "Federal court of accounts (anti-waste and anti-corruption control)", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified in either source." }, { "group": "organizational", "depth": 2, "reason": "Material to DNIT and TCU: both cited sources state that the tender(s) were blocked or canceled by acórdão after the Alice alert. The amount is given as R$39 million (two notices) by Inforchannel and R$20 million (one notice) by JOTA." }, { "group": "societal", "depth": 1, "reason": "A single highway tender among some 60,000 federal tenders a year, small at national scale." } ], "ai_role": { "status": "assessed", "statement": "Alice's role was detection. Per Inforchannel it pulls the estimated value from notice text, runs nine text analyses for competitiveness restrictions and 23 database cross-checks. The auditor's statement and the JOTA article attribute the discovery of the BR-153 notices to Alice. The cancellation was a decision of the TCU plenary (Acórdão 1116/2017). Both sources rest on TCU staff statements, the deployer describing its own in-house tool. The source counts as independent because TCU is a public body, not a vendor. The acórdão text was not obtained, so the two accounts' differing figures (R$39 million, two notices; R$20 million, one notice) cannot be reconciled." }, "evidence": [ { "kind": "news report", "citation": "Inforchannel (2017). TCU cria robô para varredura diária de irregularidades em editais. 27 Sep 2017, quoting TCU auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153" }, { "kind": "news report", "citation": "Inforchannel (2017). Same article, statement by auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais (Nakano statement)", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em dois meses, um Acórdão bloqueou essas duas licitações. Mas esse é só um exemplo." }, { "kind": "news report", "citation": "JOTA Info (2017). Alice e a plataforma digital de controle externo. Opinião e Análise, 22 Nov 2017 (quotes TCU secretary Wesley Vaz).", "title": "Alice e a plataforma digital de controle externo", "url": "https://www.jota.info/opiniao-e-analise/artigos/alice-e-a-plataforma-digital-de-controle-externo", "date": "2017-11-22", "written_by": "independent", "quote": "O Alice foi decisivo no Acórdão-TCU 1.116/2017 (relatoria do Ministro Walton Alencar Rodrigues), que determinou o cancelamento do edital da rodovia BR-153 orçado em R$ 20 milhões." }, { "kind": "news report", "citation": "Gomes, H. S. (2018). Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações. G1, 18 Mar 2018.", "title": "Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml", "date": "2018-03-18", "written_by": "independent", "quote": "Depois de dicas delas, licitações com falhas já foram canceladas e compras públicas enviesadas tiveram que ser refeitas." } ], "replication": { "produced_by": [ "Daily collection of all tender notices and auction records from Comprasnet and the Diário Oficial da União", "Rule-based text analysis (nine checks on competitiveness restrictions, reading of estimated value) and 23 database cross-checks of suppliers", "Auditors in the TCU Goiás unit reading the alerts during the public phase of the tender", "A TCU plenary decision (acórdão) within two months of the alert" ], "must_hold": [ "Notices published on Comprasnet in machine-readable text", "Alerts reaching auditors within the 15–30-day public phase", "Auditors with capacity to open a case or contact the contracting body", "Contracting bodies or the court willing to block or cancel flawed tenders" ], "echoes": [ { "date": "2018-03-18", "setting": "TCU statement to G1 that tenders with flaws had been canceled and biased purchases redone after the robots' tips (no case named)", "model_version": null, "outcome": "recurred", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, { "date": "2022-12-01", "setting": "TCE-MG audit of 2,297 state and municipal tenders of August 2022 using Alice: 60 irregularities, 53 answered, R$91.7 million of notices corrected", "model_version": null, "outcome": "recurred", "source": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457" } ] }, "paired_incident": null, "headline": "TCU's Alice flags DNIT's BR-153 notices; TCU cancels the tender", "metric": "ordered cancellation of the BR-153 tender budgeted at R$20 million", "comparator": { "against": "TCU auditors reading the roughly 200 federal tender notices published each day by hand", "before": "seria muito custoso porque são, em média, 200 editais por dia", "after": "Os auditores do Tribunal de Contas da União recebem pontualmente às 19h um e-mail de Alice. São os resumos das centenas de contratações federais publicadas naquele dia.", "basis": "assumed", "quote": "“Esse tipo de trabalho poderia ser feito por humanos, mas seria muito custoso porque são, em média, 200 editais por dia”, diz Wesley Vaz Silva, diretor da Secretaria de Fiscalização de Tecnologia da Informação do TCU, ao G1.", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, "later": { "status": "scaled", "date": "2018-03-18", "note": "By March 2018 G1 reported that Alice, Sofia and Monica were also in use at the CGU, the Federal Prosecution Service, the Federal Police and state courts of accounts, and that flawed tenders had been canceled after their tips.", "quote": "As três robôs já são usadas por servidores da Controladoria Geral da União, Ministério Público Federal, Polícia Federal e tribunais de contas dos Estados. Depois de dicas delas, licitações com falhas já foram canceladas e compras públicas enviesadas tiveram que ser refeitas.", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, "value": { "amount": "R$ 39 milhões", "what": "value of the two DNIT tender notices for BR-153 maintenance that a TCU acórdão blocked", "who": "DNIT's Goiás unit, which had published the tenders for stretches whose maintenance falls to the concessionaire", "estimate": false, "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153, mas em trechos que estão sob concessão – portanto com manutenção a cargo da concessionária.", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" }, "developer_country": "Brazil", "deployer_country": "Brazil", "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://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "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://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o", "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: 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-0331", "catalog_version": "1.0.0", "source_fingerprint": "7489aea69f34d2e6788d992db4d685b6d0e8fc49912fc42f499abe908e1e3d4f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0331-source-0001", "AIBD-0331-source-0002", "AIBD-0331-source-0003", "AIBD-0331-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-0331-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": "Tribunal de Contas da União (TCU) with the Ministério da Transparência e Controladoria-Geral da União (CGU), in-house", "deployer": "Tribunal de Contas da União (TCU), Goiás 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": "Alice's role was detection. Per Inforchannel it pulls the estimated value from notice text, runs nine text analyses for competitiveness restrictions and 23 database cross-checks. The auditor's statement and the JOTA article attribute the discovery of the BR-153 notices to Alice. The cancellation was a decision of the TCU plenary (Acórdão 1116/2017). Both sources rest on TCU staff statements, the deployer describing its own in-house tool. The source counts as independent because TCU is a public body, not a vendor. The acórdão text was not obtained, so the two accounts' differing figures (R$39 million, two notices; R$20 million, one notice) cannot be reconciled." } } }, "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": "Alice's role was detection. Per Inforchannel it pulls the estimated value from notice text, runs nine text analyses for competitiveness restrictions and 23 database cross-checks. The auditor's statement and the JOTA article attribute the discovery of the BR-153 notices to Alice. The cancellation was a decision of the TCU plenary (Acórdão 1116/2017). Both sources rest on TCU staff statements, the deployer describing its own in-house tool. The source counts as independent because TCU is a public body, not a vendor. The acórdão text was not obtained, so the two accounts' differing figures (R$39 million, two notices; R$20 million, one notice) cannot be reconciled." } } }, "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 in either source." }, { "group": "organizational", "depth": 2, "reason": "Material to DNIT and TCU: both cited sources state that the tender(s) were blocked or canceled by acórdão after the Alice alert. The amount is given as R$39 million (two notices) by Inforchannel and R$20 million (one notice) by JOTA." }, { "group": "societal", "depth": 1, "reason": "A single highway tender among some 60,000 federal tenders a year, small at national scale." } ] } }, "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 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 in either source." }, { "group": "organizational", "depth": 2, "reason": "Material to DNIT and TCU: both cited sources state that the tender(s) were blocked or canceled by acórdão after the Alice alert. The amount is given as R$39 million (two notices) by Inforchannel and R$20 million (one notice) by JOTA." }, { "group": "societal", "depth": 1, "reason": "A single highway tender among some 60,000 federal tenders a year, small at national scale." } ] } }, "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 either source." }, { "group": "organizational", "depth": 2, "reason": "Material to DNIT and TCU: both cited sources state that the tender(s) were blocked or canceled by acórdão after the Alice alert. The amount is given as R$39 million (two notices) by Inforchannel and R$20 million (one notice) by JOTA." }, { "group": "societal", "depth": 1, "reason": "A single highway tender among some 60,000 federal tenders a year, small at national scale." } ] } }, "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": "R$ 39 milhões", "what": "value of the two DNIT tender notices for BR-153 maintenance that a TCU acórdão blocked", "who": "DNIT's Goiás unit, which had published the tenders for stretches whose maintenance falls to the concessionaire", "estimate": false, "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153, mas em trechos que estão sob concessão – portanto com manutenção a cargo da concessionária.", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" } } }, "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": "R$ 39 milhões", "what": "value of the two DNIT tender notices for BR-153 maintenance that a TCU acórdão blocked", "who": "DNIT's Goiás unit, which had published the tenders for stretches whose maintenance falls to the concessionaire", "estimate": false, "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153, mas em trechos que estão sob concessão – portanto com manutenção a cargo da concessionária.", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" } } }, "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": "R$ 39 milhões", "what": "value of the two DNIT tender notices for BR-153 maintenance that a TCU acórdão blocked", "who": "DNIT's Goiás unit, which had published the tenders for stretches whose maintenance falls to the concessionaire", "estimate": false, "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153, mas em trechos que estão sob concessão – portanto com manutenção a cargo da concessionária.", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" } } }, "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": "ordered cancellation of the BR-153 tender budgeted at R$20 million", "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": "2018-03-18", "note": "By March 2018 G1 reported that Alice, Sofia and Monica were also in use at the CGU, the Federal Prosecution Service, the Federal Police and state courts of accounts, and that flawed tenders had been canceled after their tips.", "quote": "As três robôs já são usadas por servidores da Controladoria Geral da União, Ministério Público Federal, Polícia Federal e tribunais de contas dos Estados. Depois de dicas delas, licitações com falhas já foram canceladas e compras públicas enviesadas tiveram que ser refeitas.", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" } } }, "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-0331" } }, "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": "Alice (Análise de Licitações e Editais) is the Tribunal de Contas da União's robot. It reads every tender notice and auction record published on Comprasnet and the Diário Oficial da União. It had been running for about ten months when TCU presented it at a TCU–CGU data-analysis seminar on 25–26 September 2017. Inforchannel (27 September 2017), quoting TCU auditor Valéria Nakano, reported that in Goiás the court, via Alice, found DNIT's state unit had published two notices. They were worth R$39 million for maintenance of the BR-153 on stretches under concession, where maintenance falls to the concessionaire. Within two months an acórdão blocked both tenders. A JOTA article of 22 November 2017 states that Alice was decisive in Acórdão-TCU 1.116/2017 (rapporteur Minister Walton Alencar Rodrigues). It ordered cancellation of the BR-153 tender budgeted at R$20 million. The two sources give different numbers of notices and amounts. The acórdão itself was not available. The decision date is not reported.", "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": "No individual beneficiary is identified in either source." }, { "group": "organizational", "depth": 2, "reason": "Material to DNIT and TCU: both cited sources state that the tender(s) were blocked or canceled by acórdão after the Alice alert. The amount is given as R$39 million (two notices) by Inforchannel and R$20 million (one notice) by JOTA." }, { "group": "societal", "depth": 1, "reason": "A single highway tender among some 60,000 federal tenders a year, small at national scale." } ] } }, "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": "Daily automated screening of federal tender notices for signs of irregularity so that auditors can act during the public phase of a tender. This use was its 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": "Goiás, Brazil (BR-153 highway)", "developer_country": "Brazil", "deployer_country": "Brazil" } }, "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": "Daily automated screening of federal tender notices for signs of irregularity so that auditors can act during the public phase of a tender. This use was its 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": "2017-09-27", "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, the value as stated from the cited sources.", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "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://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o", "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": [ "Daily collection of all tender notices and auction records from Comprasnet and the Diário Oficial da União", "Rule-based text analysis (nine checks on competitiveness restrictions, reading of estimated value) and 23 database cross-checks of suppliers", "Auditors in the TCU Goiás unit reading the alerts during the public phase of the tender", "A TCU plenary decision (acórdão) within two months of the alert" ], "must_hold": [ "Notices published on Comprasnet in machine-readable text", "Alerts reaching auditors within the 15–30-day public phase", "Auditors with capacity to open a case or contact the contracting body", "Contracting bodies or the court willing to block or cancel flawed tenders" ], "echoes": [ { "date": "2018-03-18", "setting": "TCU statement to G1 that tenders with flaws had been canceled and biased purchases redone after the robots' tips (no case named)", "outcome": "recurred", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, { "date": "2022-12-01", "setting": "TCE-MG audit of 2,297 state and municipal tenders of August 2022 using Alice: 60 irregularities, 53 answered, R$91.7 million of notices corrected", "outcome": "recurred", "source": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457" } ] } } }, "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": [ "Daily collection of all tender notices and auction records from Comprasnet and the Diário Oficial da União", "Rule-based text analysis (nine checks on competitiveness restrictions, reading of estimated value) and 23 database cross-checks of suppliers", "Auditors in the TCU Goiás unit reading the alerts during the public phase of the tender", "A TCU plenary decision (acórdão) within two months of the alert" ], "must_hold": [ "Notices published on Comprasnet in machine-readable text", "Alerts reaching auditors within the 15–30-day public phase", "Auditors with capacity to open a case or contact the contracting body", "Contracting bodies or the court willing to block or cancel flawed tenders" ], "echoes": [ { "date": "2018-03-18", "setting": "TCU statement to G1 that tenders with flaws had been canceled and biased purchases redone after the robots' tips (no case named)", "outcome": "recurred", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, { "date": "2022-12-01", "setting": "TCE-MG audit of 2,297 state and municipal tenders of August 2022 using Alice: 60 irregularities, 53 answered, R$91.7 million of notices corrected", "outcome": "recurred", "source": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457" } ] } } }, "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, the value as stated from the cited sources.", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml; https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "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://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_da_Uni%C3%A3o", "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": "Public administration: external audit and procurement control", "segment": "Federal court of accounts (anti-waste and anti-corruption control)" } }, "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": "Inforchannel (2017). TCU cria robô para varredura diária de irregularidades em editais. 27 Sep 2017, quoting TCU auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153" }, { "kind": "news report", "citation": "Inforchannel (2017). Same article, statement by auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais (Nakano statement)", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em dois meses, um Acórdão bloqueou essas duas licitações. Mas esse é só um exemplo." }, { "kind": "news report", "citation": "JOTA Info (2017). Alice e a plataforma digital de controle externo. Opinião e Análise, 22 Nov 2017 (quotes TCU secretary Wesley Vaz).", "title": "Alice e a plataforma digital de controle externo", "url": "https://www.jota.info/opiniao-e-analise/artigos/alice-e-a-plataforma-digital-de-controle-externo", "date": "2017-11-22", "written_by": "independent", "quote": "O Alice foi decisivo no Acórdão-TCU 1.116/2017 (relatoria do Ministro Walton Alencar Rodrigues), que determinou o cancelamento do edital da rodovia BR-153 orçado em R$ 20 milhões." }, { "kind": "news report", "citation": "Gomes, H. S. (2018). Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações. G1, 18 Mar 2018.", "title": "Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml", "date": "2018-03-18", "written_by": "independent", "quote": "Depois de dicas delas, licitações com falhas já foram canceladas e compras públicas enviesadas tiveram que ser refeitas." } ] } }, "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-0331-source-0001", "AIBD-0331-source-0002", "AIBD-0331-source-0003", "AIBD-0331-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": "Inforchannel (2017). TCU cria robô para varredura diária de irregularidades em editais. 27 Sep 2017, quoting TCU auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em Goiás, o Tribunal de Contas, via Alice, descobriu que o DNIT estdual publicou dois editais, de R$ 39 milhões, para manutenção da BR 153" }, { "kind": "news report", "citation": "Inforchannel (2017). Same article, statement by auditor Valéria Nakano.", "title": "TCU cria robô para varredura diária de irregularidades em editais (Nakano statement)", "url": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/", "date": "2017-09-27", "written_by": "independent", "quote": "Em dois meses, um Acórdão bloqueou essas duas licitações. Mas esse é só um exemplo." }, { "kind": "news report", "citation": "JOTA Info (2017). Alice e a plataforma digital de controle externo. Opinião e Análise, 22 Nov 2017 (quotes TCU secretary Wesley Vaz).", "title": "Alice e a plataforma digital de controle externo", "url": "https://www.jota.info/opiniao-e-analise/artigos/alice-e-a-plataforma-digital-de-controle-externo", "date": "2017-11-22", "written_by": "independent", "quote": "O Alice foi decisivo no Acórdão-TCU 1.116/2017 (relatoria do Ministro Walton Alencar Rodrigues), que determinou o cancelamento do edital da rodovia BR-153 orçado em R$ 20 milhões." }, { "kind": "news report", "citation": "Gomes, H. S. (2018). Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações. G1, 18 Mar 2018.", "title": "Como as robôs Alice, Sofia e Monica ajudam o TCU a caçar irregularidades em licitações", "url": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml", "date": "2018-03-18", "written_by": "independent", "quote": "Depois de dicas delas, licitações com falhas já foram canceladas e compras públicas enviesadas tiveram que ser refeitas." } ] } }, "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": "Alice flagged DNIT Goiás notices for BR-153 maintenance on concession stretches (R$39m per Inforchannel); TCU Acórdão 1116/2017 canceled the R$20m tender (JOTA).", "account": "Alice (Análise de Licitações e Editais) is the Tribunal de Contas da União's robot. It reads every tender notice and auction record published on Comprasnet and the Diário Oficial da União. It had been running for about ten months when TCU presented it at a TCU–CGU data-analysis seminar on 25–26 September 2017. Inforchannel (27 September 2017), quoting TCU auditor Valéria Nakano, reported that in Goiás the court, via Alice, found DNIT's state unit had published two notices. They were worth R$39 million for maintenance of the BR-153 on stretches under concession, where maintenance falls to the concessionaire. Within two months an acórdão blocked both tenders. A JOTA article of 22 November 2017 states that Alice was decisive in Acórdão-TCU 1.116/2017 (rapporteur Minister Walton Alencar Rodrigues). It ordered cancellation of the BR-153 tender budgeted at R$20 million. The two sources give different numbers of notices and amounts. The acórdão itself was not available. The decision date 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": "Alice (Análise de Licitações e Editais), TCU/CGU text-analysis and data cross-checking 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": "Tribunal de Contas da União (TCU) with the Ministério da Transparência e Controladoria-Geral da União (CGU), in-house" } }, "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": "TCU's Alice robot flags DNIT notices for BR-153 maintenance on stretches already under concession; TCU Acórdão 1116/2017 cancels the tender", "headline": "TCU's Alice flags DNIT's BR-153 notices; TCU cancels the tender" } }, "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": [ "7489aea69f34d2e6788d992db4d685b6d0e8fc49912fc42f499abe908e1e3d4f" ], "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": "Alice (Análise de Licitações e Editais) is the Tribunal de Contas da União's robot. It reads every tender notice and auction record published on Comprasnet and the Diário Oficial da União. It had been running for about ten months when TCU presented it at a TCU–CGU data-analysis seminar on 25–26 September 2017. Inforchannel (27 September 2017), quoting TCU auditor Valéria Nakano, reported that in Goiás the court, via Alice, found DNIT's state unit had published two notices. They were worth R$39 million for maintenance of the BR-153 on stretches under concession, where maintenance falls to the concessionaire. Within two months an acórdão blocked both tenders. A JOTA article of 22 November 2017 states that Alice was decisive in Acórdão-TCU 1.116/2017 (rapporteur Minister Walton Alencar Rodrigues). It ordered cancellation of the BR-153 tender budgeted at R$20 million. The two sources give different numbers of notices and amounts. The acórdão itself was not available. The decision date is not reported.", "replication": { "produced_by": [ "Daily collection of all tender notices and auction records from Comprasnet and the Diário Oficial da União", "Rule-based text analysis (nine checks on competitiveness restrictions, reading of estimated value) and 23 database cross-checks of suppliers", "Auditors in the TCU Goiás unit reading the alerts during the public phase of the tender", "A TCU plenary decision (acórdão) within two months of the alert" ], "must_hold": [ "Notices published on Comprasnet in machine-readable text", "Alerts reaching auditors within the 15–30-day public phase", "Auditors with capacity to open a case or contact the contracting body", "Contracting bodies or the court willing to block or cancel flawed tenders" ], "echoes": [ { "date": "2018-03-18", "setting": "TCU statement to G1 that tenders with flaws had been canceled and biased purchases redone after the robots' tips (no case named)", "outcome": "recurred", "source": "https://g1.globo.com/economia/tecnologia/noticia/como-as-robos-alice-sofia-e-monica-ajudam-o-tcu-a-cacar-irregularidades-em-licitacoes.ghtml" }, { "date": "2022-12-01", "setting": "TCE-MG audit of 2,297 state and municipal tenders of August 2022 using Alice: 60 irregularities, 53 answered, R$91.7 million of notices corrected", "outcome": "recurred", "source": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457" } ] } } }, "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-0359", "case_url": "records/AIBD-0359.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "AI analysis of thermal camera feeds triggered the warning signs. Virginia Tech reports a 3 mph average slowdown as drivers approached activated signs, but it gives no result on collisions prevented. The results come from the evaluating institution's own report. WCYB's separate interview with VDOT describes deployment and adds no independent test of the measured results." }, { "label": "Comparison", "value": "comparison baseline not specified in the institutional report", "url": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html" }, { "label": "After", "value": "an average speed reduction of 3 mph as vehicles approached signs that had been activated by wildlife detections" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0359", "title": "VTTI/VDOT thermal-camera AI wildlife detection pilot on Route 460 near Grundy, Virginia: 97% detection accuracy and a 3 mph average speed drop at activated signs", "summary": "Drivers slowed by an average of 3 mph near warning signs linked to Crosstek’s AI in Virginia’s ADDWS pilot.", "date_observed": "2026-10-20", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Gwen Sheldon reported for Virginia Tech on 20 October 2026 on a one-year Virginia Tech Transportation Institute (VTTI) pilot that ended in September 2026. It ran on Route 460 near Grundy in Southwest Virginia, sponsored by the Virginia Department of Transportation (VDOT) as part of the Corridor Q expansion. Eight portable solar-powered trailers with thermal cameras were installed on a 750-meter section of a 14-mile corridor that is home to about 100 elk. AI software from Crosstek analyzed the camera feeds. When wildlife was detected, dynamic message signs told motorists that wildlife had been detected ahead and to slow down. T-Mobile provided 5G and satellite links. The system achieved 97 percent overall accuracy in identifying medium and large wildlife. The initial analysis found an average speed reduction of 3 mph as vehicles approached activated signs. The report gives detection accuracy and driver speeds but no result on collisions or fatalities prevented. WCYB had reported the test on 19 November 2025. Its separate interview with Michelle Earl of VDOT describes animal detection and communication with the message boards. The measured results come from VTTI's own report. WCYB's earlier interview establishes deployment context, not independent verification of those results. VDOT names the pilot the Animal Detection and Driver Warning System (ADDWS). Its project page identifies VTTI as the research partner. Virginia Tech’s results report identifies Crosstek as the camera and AI-software supplier.", "category": "protective response", "unexpected": false, "purpose": "Detect large wildlife near the roadway and warn approaching drivers in real time to reduce wildlife-vehicle collisions. The measured driver slowdown was the intended response.", "system": "Animal Detection and Driver Warning System (ADDWS), using Crosstek thermal-camera AI detection", "model_version": null, "developer": "Crosstek (cameras and detection software); system integration and evaluation by Virginia Tech Transportation Institute", "deployer": "Virginia Department of Transportation with Virginia Tech Transportation Institute", "location": "Route 460 (Corridor Q) near Grundy, Buchanan County, Virginia, USA", "domain": "Road safety and wildlife-vehicle collision prevention", "segment": "State transportation agency research pilot", "benefit_type": "Increased Product Safety", "taxonomy_note": "The documented effect is slower driving at warning signs; collisions avoided are not established.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual driver or crash outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "VDOT obtained a measured driver response (3 mph average slowdown) and 97 percent detection accuracy from a pilot, but no crash reduction." }, { "group": "societal", "depth": 1, "reason": "One 750-meter test section. The institutional report gives no result on collisions or fatalities prevented." } ], "ai_role": { "status": "assessed", "statement": "AI analysis of thermal camera feeds triggered the warning signs. Virginia Tech reports a 3 mph average slowdown as drivers approached activated signs, but it gives no result on collisions prevented. The results come from the evaluating institution's own report. WCYB's separate interview with VDOT describes deployment and adds no independent test of the measured results." }, "evidence": [ { "kind": "press release", "citation": "Sheldon, G. (2026). Elk crossing: Real-time driver warning system helps prevent animal collisions. Virginia Tech, 20 October 2026.", "title": "Elk crossing: Real-time driver warning system helps prevent animal collisions", "url": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "date": "2026-10-20", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "Allen, S. (2025). VDOT tests elk-detection system to prevent collisions in Southwest Virginia. WCYB, 19 November 2025.", "title": "VDOT tests elk-detection system to prevent collisions in Southwest Virginia", "url": "https://wcyb.com/news/local/vdot-tests-elk-detection-system-to-prevent-collisions-in-southwest-virginia", "date": "2025-11-19", "written_by": "independent", "quote": "It identifies a large animal on the road — an elk, a bear, or even a large deer — it will communicate with our message boards." }, { "kind": "report", "citation": "Donaldson, Bridget M., Elizabeth M. Hillard, Jacalyn P. Rosenberger, and Renee Callahan. An Evaluation of Wildlife Crossing Design, Placement, Costs, and Funding Opportunities for Corridor Q. VTRC 24-R8. Virginia Transportation Research Council, September 2023, 20–21, 41–42, table 12 and footnotes a and c.", "title": "An Evaluation of Wildlife Crossing Design, Placement, Costs, and Funding Opportunities for Corridor Q", "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "date": null, "written_by": "deployer", "quote": null }, { "kind": "report", "citation": "Virginia Department of Transportation. “Corridor Q driver safety and maintaining habitat connectivity,” § “Current Research - Pilot Project.”", "title": "Corridor Q driver safety and maintaining habitat connectivity", "url": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "date": null, "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Portable solar trailers with thermal cameras on a known elk corridor", "AI detection software linked to dynamic message signs", "5G and satellite connectivity", "State DOT sponsorship within a highway expansion project" ], "must_hold": [ "Detection accuracy high enough to keep driver trust in the signs", "Sign placement giving drivers time to slow", "Power and connectivity at remote sites", "Crash data collection to test the unproven safety effect" ], "echoes": [ { "date": "2026-02-09", "setting": "LAARMA AI roadside alerts in the Kuranda cassowary trial, Queensland (AIBD-0104): fatal-crash reduction reported", "model_version": null, "outcome": "recurred", "source": "AIBD-0104" }, { "date": "2026-07-18", "setting": "NSW Monaro Highway PAWS trial launched with the same detect-and-warn design; no results yet", "model_version": null, "outcome": "unknown", "source": "https://aboutregional.com.au/ai-cameras-to-be-extra-set-of-eyes-on-roadside-wildlife-in-monaro-highway-trial/510964/" } ] }, "paired_incident": null, "headline": "Drivers slowed down when signs warned of wildlife ahead", "metric": "an average speed reduction of 3 mph as vehicles approached activated signs", "comparator": { "against": "comparison baseline not specified in the institutional report", "before": null, "after": "an average speed reduction of 3 mph as vehicles approached signs that had been activated by wildlife detections", "basis": "measured", "quote": "reducing their speeds by an average of 3 mph when approaching activated signs.", "url": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html" }, "later": { "status": "unknown", "date": null, "note": "The institutional report states that the pilot ended in September 2026 and that data analysis was continuing. No later result is cited.", "quote": null, "url": null }, "value": { "amount": "$80,771.49 per elk crash", "what": "Estimated comprehensive societal cost of an elk-vehicle crash in 2020 U.S. dollars, including economic loss and monetized pain and suffering. Context only: no AI-attributed savings or crashes prevented are established.", "who": "Road users and wider society", "estimate": true, "quote": "the calculated costs for an elk crash and a deer crash were $80,771.49 and $41,338.28, respectively.", "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf" }, "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, the value as stated from the cited sources.", "source": "https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/; https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.crosstekco.com/contact-us; https://en.wikipedia.org/wiki/Virginia_Department_of_Transportation", "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": "2026-10-24", "change": "Replaced three excerpts from the Bioengineer.org reproduction with Virginia Tech's original 20 October 2026 report by Gwen Sheldon. The reproduction adds no original reporting. Retained Samantha Allen's 19 November 2025 WCYB interview with VDOT for separate deployment evidence, not corroboration of the measured results. The original report gives no collision-prevention result or explicit comparison baseline. Removed the reproduced limitation wording as a quotation and retained that evidentiary limit in the account. Excluded reproduction: https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the elk-crash cost through the 2026 Virginia Tech article and its cited 2024 feasibility study to Donaldson et al. (2023), pages 41–42, table 12. Replaced the rounded amount with $80,771.49 per elk crash. The estimate includes economic costs and monetized pain and suffering, so it is contextual societal valuation rather than an AI saving or cash loss. Recorded the input references, California severity assumption, and unresolved price year.", "source": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered the cited VDOT crash-cost memorandum in a preserved copy. Its tables 1–2 and page 3 establish the 2020 price basis. The elk estimate reproduces as 0.862 × $13,743 + 0.136 × $308,895 + 0.002 × $13,457,654 = $80,771.494, rounded to $80,771.49. This is a contextual societal valuation, not an AI saving.", "source": "https://loudouncoalition.org/wp-content/uploads/2022/06/37-VDOT-Crash-Costs-Memo_acc050222-1.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, two reports from the deployer 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-0359", "catalog_version": "1.0.0", "source_fingerprint": "1b5658b0b4e4d534db95642c8c0b4ab3bec761d8b7e2156861af3d0ec07dffbe", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-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-0359-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": "Crosstek (cameras and detection software); system integration and evaluation by Virginia Tech Transportation Institute", "deployer": "Virginia Department of Transportation with Virginia Tech Transportation Institute" } }, "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": "AI analysis of thermal camera feeds triggered the warning signs. Virginia Tech reports a 3 mph average slowdown as drivers approached activated signs, but it gives no result on collisions prevented. The results come from the evaluating institution's own report. WCYB's separate interview with VDOT describes deployment and adds no independent test of the measured results." } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "ai_role" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "AI detected animals in thermal images and activated roadside warning signs. Virginia Tech reports that approaching drivers slowed by an average of 3 mph. It reports no measured reduction in collisions.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "AI analysis of thermal camera feeds triggered the warning signs. Virginia Tech reports a 3 mph average slowdown as drivers approached activated signs, but it gives no result on collisions prevented. The results come from the evaluating institution's own report. WCYB's separate interview with VDOT describes deployment and adds no independent test of the measured results." } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "category", "ai_role" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-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", "reason": "No individual driver or crash outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "VDOT obtained a measured driver response (3 mph average slowdown) and 97 percent detection accuracy from a pilot, but no crash reduction." }, { "group": "societal", "depth": 1, "reason": "One 750-meter test section. The institutional report gives no result on collisions or fatalities prevented." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002" ], "reviewed_on": "2026-10-24" }, "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, two reports from the deployer and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "The display category follows the existing rule. Source lineage and claim-specific limitations are stated here; a publication count is not a count of independent confirmations." } }, "provenance": { "kind": "source_review", "basis": "Recomputed the existing display rule after adding the reviewed sources. Source independence remains claim-specific.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No individual driver or crash outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "VDOT obtained a measured driver response (3 mph average slowdown) and 97 percent detection accuracy from a pilot, but no crash reduction." }, { "group": "societal", "depth": 1, "reason": "One 750-meter test section. The institutional report gives no result on collisions or fatalities prevented." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002" ], "reviewed_on": "2026-10-24" }, "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 driver or crash outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "VDOT obtained a measured driver response (3 mph average slowdown) and 97 percent detection accuracy from a pilot, but no crash reduction." }, { "group": "societal", "depth": 1, "reason": "One 750-meter test section. The institutional report gives no result on collisions or fatalities prevented." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002" ], "reviewed_on": "2026-10-24" }, "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": "$80,771.49 per elk crash", "what": "Estimated comprehensive societal cost of an elk-vehicle crash in 2020 U.S. dollars, including economic loss and monetized pain and suffering. Context only: no AI-attributed savings or crashes prevented are established.", "who": "Road users and wider society", "estimate": true, "quote": "the calculated costs for an elk crash and a deer crash were $80,771.49 and $41,338.28, respectively.", "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf" } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source or monetary context after tracing the cited cost estimate to the original analysis, its table and footnotes. Other mapped content and review states remain unchanged.", "record_fields": [ "value" ], "source_refs": [ "AIBD-0359-source-0003" ], "reviewed_on": "2026-10-24" }, "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": "$80,771.49 per elk crash", "what": "Estimated comprehensive societal cost of an elk-vehicle crash in 2020 U.S. dollars, including economic loss and monetized pain and suffering. Context only: no AI-attributed savings or crashes prevented are established.", "who": "Road users and wider society", "estimate": true, "quote": "the calculated costs for an elk crash and a deer crash were $80,771.49 and $41,338.28, respectively.", "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf" } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source or monetary context after tracing the cited cost estimate to the original analysis, its table and footnotes. Other mapped content and review states remain unchanged.", "record_fields": [ "value" ], "source_refs": [ "AIBD-0359-source-0003" ], "reviewed_on": "2026-10-24" }, "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": "$80,771.49 per elk crash", "what": "Estimated comprehensive societal cost of an elk-vehicle crash in 2020 U.S. dollars, including economic loss and monetized pain and suffering. Context only: no AI-attributed savings or crashes prevented are established.", "who": "Road users and wider society", "estimate": true, "quote": "the calculated costs for an elk crash and a deer crash were $80,771.49 and $41,338.28, respectively.", "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf" } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source or monetary context after tracing the cited cost estimate to the original analysis, its table and footnotes. Other mapped content and review states remain unchanged.", "record_fields": [ "value" ], "source_refs": [ "AIBD-0359-source-0003" ], "reviewed_on": "2026-10-24" }, "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": "an average speed reduction of 3 mph as vehicles approached activated signs", "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": "unknown", "note": "The institutional report states that the pilot ended in September 2026 and that data analysis was continuing. No later result is cited." } } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "later" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-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-0359" } }, "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": "The documented effect is slower driving at warning signs; collisions avoided are 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": "Gwen Sheldon reported for Virginia Tech on 20 October 2026 on a one-year Virginia Tech Transportation Institute (VTTI) pilot that ended in September 2026. It ran on Route 460 near Grundy in Southwest Virginia, sponsored by the Virginia Department of Transportation (VDOT) as part of the Corridor Q expansion. Eight portable solar-powered trailers with thermal cameras were installed on a 750-meter section of a 14-mile corridor that is home to about 100 elk. AI software from Crosstek analyzed the camera feeds. When wildlife was detected, dynamic message signs told motorists that wildlife had been detected ahead and to slow down. T-Mobile provided 5G and satellite links. The system achieved 97 percent overall accuracy in identifying medium and large wildlife. The initial analysis found an average speed reduction of 3 mph as vehicles approached activated signs. The report gives detection accuracy and driver speeds but no result on collisions or fatalities prevented. WCYB had reported the test on 19 November 2025. Its separate interview with Michelle Earl of VDOT describes animal detection and communication with the message boards. The measured results come from VTTI's own report. WCYB's earlier interview establishes deployment context, not independent verification of those results. VDOT names the pilot the Animal Detection and Driver Warning System (ADDWS). Its project page identifies VTTI as the research partner. Virginia Tech’s results report identifies Crosstek as the camera and AI-software supplier.", "benefit_type": "Increased Product Safety" } }, "provenance": { "kind": "source_review", "basis": "Updated the fields affected by the system-identification review. Existing benefit-depth ratings and unrelated assessments are preserved.", "record_fields": [ "account", "benefit_type" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Drivers slowed by an average of 3 mph as they approached activated warning signs.", "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", "reason": "No individual driver or crash outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "VDOT obtained a measured driver response (3 mph average slowdown) and 97 percent detection accuracy from a pilot, but no crash reduction." }, { "group": "societal", "depth": 1, "reason": "One 750-meter test section. The institutional report gives no result on collisions or fatalities prevented." } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the affected source references, dates, or account after reading the original sources. Other mapped content and its review status remain unchanged.", "record_fields": [ "ratings" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002" ], "reviewed_on": "2026-10-24" }, "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 large wildlife near the roadway and warn approaching drivers in real time to reduce wildlife-vehicle collisions. The measured driver slowdown was the intended response." } }, "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": "Route 460 (Corridor Q) near Grundy, Buchanan County, Virginia, USA", "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": "Detect large wildlife near the roadway and warn approaching drivers in real time to reduce wildlife-vehicle collisions. The measured driver slowdown was the intended response." } }, "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": "2026-10-20", "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": "Added what the benefit is measured against, the value as stated from the cited sources.", "source": "https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/; https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.crosstekco.com/contact-us; https://en.wikipedia.org/wiki/Virginia_Department_of_Transportation", "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" }, { "date": "2026-10-24", "change": "Replaced three excerpts from the Bioengineer.org reproduction with Virginia Tech's original 20 October 2026 report by Gwen Sheldon. The reproduction adds no original reporting. Retained Samantha Allen's 19 November 2025 WCYB interview with VDOT for separate deployment evidence, not corroboration of the measured results. The original report gives no collision-prevention result or explicit comparison baseline. Removed the reproduced limitation wording as a quotation and retained that evidentiary limit in the account. Excluded reproduction: https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the elk-crash cost through the 2026 Virginia Tech article and its cited 2024 feasibility study to Donaldson et al. (2023), pages 41–42, table 12. Replaced the rounded amount with $80,771.49 per elk crash. The estimate includes economic costs and monetized pain and suffering, so it is contextual societal valuation rather than an AI saving or cash loss. Recorded the input references, California severity assumption, and unresolved price year.", "source": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered the cited VDOT crash-cost memorandum in a preserved copy. Its tables 1–2 and page 3 establish the 2020 price basis. The elk estimate reproduces as 0.862 × $13,743 + 0.136 × $308,895 + 0.002 × $13,457,654 = $80,771.494, rounded to $80,771.49. This is a contextual societal valuation, not an AI saving.", "source": "https://loudouncoalition.org/wp-content/uploads/2022/06/37-VDOT-Crash-Costs-Memo_acc050222-1.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "date_observed", "revisions", "date_basis", "date_precision" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Portable solar trailers with thermal cameras on a known elk corridor", "AI detection software linked to dynamic message signs", "5G and satellite connectivity", "State DOT sponsorship within a highway expansion project" ], "must_hold": [ "Detection accuracy high enough to keep driver trust in the signs", "Sign placement giving drivers time to slow", "Power and connectivity at remote sites", "Crash data collection to test the unproven safety effect" ], "echoes": [ { "date": "2026-02-09", "setting": "LAARMA AI roadside alerts in the Kuranda cassowary trial, Queensland (AIBD-0104): fatal-crash reduction reported", "outcome": "recurred", "source": "AIBD-0104" }, { "date": "2026-07-18", "setting": "NSW Monaro Highway PAWS trial launched with the same detect-and-warn design; no results yet", "outcome": "unknown", "source": "https://aboutregional.com.au/ai-cameras-to-be-extra-set-of-eyes-on-roadside-wildlife-in-monaro-highway-trial/510964/" } ] } } }, "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": [ "Portable solar trailers with thermal cameras on a known elk corridor", "AI detection software linked to dynamic message signs", "5G and satellite connectivity", "State DOT sponsorship within a highway expansion project" ], "must_hold": [ "Detection accuracy high enough to keep driver trust in the signs", "Sign placement giving drivers time to slow", "Power and connectivity at remote sites", "Crash data collection to test the unproven safety effect" ], "echoes": [ { "date": "2026-02-09", "setting": "LAARMA AI roadside alerts in the Kuranda cassowary trial, Queensland (AIBD-0104): fatal-crash reduction reported", "outcome": "recurred", "source": "AIBD-0104" }, { "date": "2026-07-18", "setting": "NSW Monaro Highway PAWS trial launched with the same detect-and-warn design; 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The reproduction adds no original reporting. Retained Samantha Allen's 19 November 2025 WCYB interview with VDOT for separate deployment evidence, not corroboration of the measured results. The original report gives no collision-prevention result or explicit comparison baseline. Removed the reproduced limitation wording as a quotation and retained that evidentiary limit in the account. Excluded reproduction: https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the elk-crash cost through the 2026 Virginia Tech article and its cited 2024 feasibility study to Donaldson et al. (2023), pages 41–42, table 12. Replaced the rounded amount with $80,771.49 per elk crash. The estimate includes economic costs and monetized pain and suffering, so it is contextual societal valuation rather than an AI saving or cash loss. Recorded the input references, California severity assumption, and unresolved price year.", "source": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Recovered the cited VDOT crash-cost memorandum in a preserved copy. Its tables 1–2 and page 3 establish the 2020 price basis. The elk estimate reproduces as 0.862 × $13,743 + 0.136 × $308,895 + 0.002 × $13,457,654 = $80,771.494, rounded to $80,771.49. This is a contextual societal valuation, not an AI saving.", "source": "https://loudouncoalition.org/wp-content/uploads/2022/06/37-VDOT-Crash-Costs-Memo_acc050222-1.pdf", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Rewrote the short title and summary in plain language, preserving the reported benefit and material limits. Added the named publication or institution to the reviewed lead source for homepage display.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Identified the AI system or feature in the supporting summary, or stated that the source does not name the software. Preserved the reported benefit and material qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Traced the system identity beyond the original case report. Added product names and supporting original sources, preserving historical-version limits and any inference in the source notes.", "source": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Shortened the homepage supporting statement to one sentence naming the system and its contribution; retained detailed outcomes and qualifications in the case account and source notes.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Revised the title and subtitle for natural phrasing, with named researchers, shorter dependencies, and less compressed terminology; preserved the system identities and evidence qualifications.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" }, { "date": "2026-10-24", "change": "Assessed model identity, provider, and deployment configuration from the original case sources. Distinguished the named product and known hardware roles from the unreported model and release.", "source": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "editor": "AIBD" } ] } }, "provenance": { "kind": "source_review", "basis": "Updated the mapped values after reviewing system identity and distinguishing product, hardware, and model versions.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Road safety and wildlife-vehicle collision prevention", "segment": "State transportation agency research pilot" } }, "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": "2026-10-24", "versions_and_access": [ { "source_url": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "status": "Original source inspected.", "locator": "Current Research - Pilot Project.", "reviewed_on": "2026-10-24" } ], "limitations": [ "This access review covers the newly added source. Earlier source-access details remain in the source lineage." ] } }, "provenance": { "kind": "source_review", "basis": "Inspected the added originals and retained prior access limitations for other sources.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Virginia Tech reports its own pilot results. WCYB’s interview with VDOT describes the deployment but provides no separate test of the measured slowdown.", "editor_note": "" } }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "Sheldon, G. (2026). Elk crossing: Real-time driver warning system helps prevent animal collisions. 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An Evaluation of Wildlife Crossing Design, Placement, Costs, and Funding Opportunities for Corridor Q. VTRC 24-R8. 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Hillard, Jacalyn P. Rosenberger, and Renee Callahan. An Evaluation of Wildlife Crossing Design, Placement, Costs, and Funding Opportunities for Corridor Q. VTRC 24-R8. 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Existing benefit-depth ratings and unrelated assessments are preserved.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-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-0359-source-0001" ], "url": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "publisher": "Virginia Tech", "author": "Gwen Sheldon", "published_on": "2026-10-20", "relationship": "Original institutional report of VTTI's own pilot results, including the 3 mph slowdown and 97 percent accuracy. This is not an independent evaluation.", "display_name": "Virginia Tech" }, { "source_refs": [ "AIBD-0359-source-0002" ], "url": "https://wcyb.com/news/local/vdot-tests-elk-detection-system-to-prevent-collisions-in-southwest-virginia", "publisher": "WCYB", "author": "Samantha Allen", "published_on": "2025-11-19", "relationship": "Separate reporting with a VDOT interview about deployment. It predates the measured results and does not corroborate them." }, { "source_refs": [ "AIBD-0359-source-0003" ], "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "publisher": "Virginia Transportation Research Council", "authors": [ "Bridget M. Donaldson", "Elizabeth M. Hillard", "Jacalyn P. Rosenberger", "Renee Callahan" ], "published_on": "2023-09", "date_precision": "month", "relationship": "Original calculation of the contextual societal cost per elk crash. It supplies additional valuation evidence and does not assess the later AI pilot.", "locators": [ "20–21", "41–42, table 12", "42, table 12, footnotes a and c" ] }, { "url": "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/", "contribution": "Establishes the deployed pilot’s name and VTTI partnership.", "locator": "Current Research - Pilot Project.", "relationship": "Deployer’s project page. It names the system but does not independently verify the results.", "source_refs": [ "AIBD-0359-source-0004" ], "display_name": "Virginia Department of Transportation" } ], "excluded_republications": [ { "url": "https://bioengineer.org/virginia-pilot-links-wildlife-alerts-to-3-mph-speed-reduction/", "published_on": "2026-10-20", "relationship": "Reproduction of the institutional results, without added original reporting." } ], "independent_evaluations_of_reported_results": 0, "lead_source_ref": "AIBD-0359-source-0001", "retained_source_order": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "ordering_basis": "The first retained source is the original publication that supports the main reported benefit. Any additional retained source follows it and supplies the separate evidence described in its relationship.", "claim_lineage": [ { "claim": "Estimated comprehensive societal cost of $80,771.49 per elk crash", "source_ref": "AIBD-0359-source-0003", "source_chain": [ { "url": "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "locator": "Paragraph linking the project’s feasibility study", "role": "Reports a rounded cost above $80,000 and links to the feasibility study." }, { "url": "https://hdl.handle.net/10919/118445", "full_text_url": "https://vtechworks.lib.vt.edu/bitstreams/32738405-75f5-4fd2-8612-ce591e462729/download", "citation": "Bell, Stephen, Tarah Crowder, and Andrew S. Alden. Feasibility Study for Animal Detection Driver Warning Systems on Corridor Q/Route 460. Report 121024, March 2024.", "locator": "2", "role": "Cites Donaldson et al. (2023) for $80,771 and separately cites Ament et al. (2022) for a different estimate." }, { "url": "https://www.vdot.virginia.gov/media/vdotvirginiagov/projects/bristol/corridor-q-driver-safety-and-maintaining-habitat/VTRC-Evaluation-of-Wildlife-Crossing-Design-for-Corridor-Q-Final-Report_acc09032025_PM.pdf", "locator": "41–42, table 12", "role": "Original weighted calculation of $80,771.49 per elk crash." } ], "method": { "description": "Combines the proportions of crash severity from 3,421 elk crash records in five states with Virginia comprehensive costs for those severity categories.", "locator": "20–21 and 41–42, table 12", "input_records": [ { "state": "California", "period": "May 2015–November 2020", "crashes": 171 }, { "state": "Colorado", "period": "2016–2020", "crashes": 1874 }, { "state": "New Mexico", "period": "2016–2020", "crashes": 1311 }, { "state": "North Carolina", "period": "2012–2021", "crashes": 40 }, { "state": "Tennessee", "period": "2009–2021", "crashes": 25 } ], "input_references": [ { "citation": "Virginia Department of Transportation. Memorandum: Virginia Traffic Crash Costs—Version 1. March 2, 2022.", "reference_label": "VDOT (2022b)", "url": "https://www.vdot.virginia.gov/business/resources/vhsip/VDOT-Crash-Costs-Memo_acc050222.pdf", "locator_in_original": "21; 42, table 12, footnote c; 67", "role": "Virginia-specific comprehensive costs by crash severity.", "access_status": "Full three-page memorandum read and table 1 visually checked in the preserved copy. The original VDOT URL returned an access error.", "access_url": "https://loudouncoalition.org/wp-content/uploads/2022/06/37-VDOT-Crash-Costs-Memo_acc050222-1.pdf", "locator_in_input": "1, tables 1–2; 3, table 6 and adjustment method" }, { "citation": "Harmon et al. Crash Costs for Highway Safety Analysis. 2018.", "locator_in_original": "21; 42, table 12, footnote c", "role": "Underlying method for the Virginia comprehensive crash costs.", "access_status": "Identified from the original report’s method citation; not independently assessed in this review." } ] }, "interpretation": "A comprehensive societal valuation, including tangible economic impacts and monetized pain and suffering through quality-adjusted life years. It is not an observed cash loss, payment, or saving produced by the AI pilot.", "limitations": [ "California supplied 171 crashes, but only 67 had severity information. Table 12, footnote a treats the remaining 104 as property-damage-only crashes.", "The authors apply crash severity proportions from other states; this does not establish how many elk crashes would occur or be prevented on Corridor Q.", "No number of crashes avoided by the AI pilot is established, so this unit valuation cannot be multiplied into an AI benefit." ], "price_basis": { "year": 2020, "currency": "USD", "basis": "VDOT memorandum tables 1–2 label Virginia crash costs as 2020 values. Page 3 states that the adjustment uses 2020 economic indicators.", "calculation_check": "0.862 × 13,743 + 0.136 × 308,895 + 0.002 × 13,457,654 = 80,771.494, rounding to the original report’s $80,771.49.", "source_url": "https://loudouncoalition.org/wp-content/uploads/2022/06/37-VDOT-Crash-Costs-Memo_acc050222-1.pdf" } } ], "reviewed_on": "2026-10-24", "claim_chains": [ { "claim": "System identity", "path": [ "https://news.vt.edu/articles/2026/10/vtti-elk-live-driver-warning-detection-virginia.html", "https://www.vdot.virginia.gov/projects/bristol-district/corridor-q-driver-safety-and-maintaining-habitat-connectivity/" ], "finding": "Animal Detection and Driver Warning System (ADDWS), using Crosstek thermal-camera AI detection" } ] } }, "provenance": { "kind": "source_review", "basis": "Traced the specific system through original developer, deployer, funder, or clinician accounts. Limits are recorded alongside each identification.", "record_fields": [ "evidence", "system" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Virginia Tech reports its own pilot results, while WCYB interviews VDOT about deployment. A separate 2023 transportation report estimates the cost of an elk collision but does not evaluate the later AI pilot.", "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": "Drivers slowed by an average of 3 mph near warning signs linked to Crosstek’s AI in Virginia’s ADDWS pilot.", "account": "Gwen Sheldon reported for Virginia Tech on 20 October 2026 on a one-year Virginia Tech Transportation Institute (VTTI) pilot that ended in September 2026. It ran on Route 460 near Grundy in Southwest Virginia, sponsored by the Virginia Department of Transportation (VDOT) as part of the Corridor Q expansion. Eight portable solar-powered trailers with thermal cameras were installed on a 750-meter section of a 14-mile corridor that is home to about 100 elk. AI software from Crosstek analyzed the camera feeds. When wildlife was detected, dynamic message signs told motorists that wildlife had been detected ahead and to slow down. T-Mobile provided 5G and satellite links. The system achieved 97 percent overall accuracy in identifying medium and large wildlife. The initial analysis found an average speed reduction of 3 mph as vehicles approached activated signs. The report gives detection accuracy and driver speeds but no result on collisions or fatalities prevented. WCYB had reported the test on 19 November 2025. Its separate interview with Michelle Earl of VDOT describes animal detection and communication with the message boards. The measured results come from VTTI's own report. WCYB's earlier interview establishes deployment context, not independent verification of those results. VDOT names the pilot the Animal Detection and Driver Warning System (ADDWS). Its project page identifies VTTI as the research partner. Virginia Tech’s results report identifies Crosstek as the camera and AI-software supplier." } }, "provenance": { "kind": "source_review", "basis": "Updated the fields affected by the system-identification review. Existing benefit-depth ratings and unrelated assessments are preserved.", "record_fields": [ "summary", "account" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "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": "value_present", "value": { "kind": "structured", "data": { "inputs": "Thermal-camera images near the Route 460 wildlife crossing.", "outputs": "Roadside warnings when animals approach.", "communications": "5G and satellite connectivity support remote monitoring.", "unreported_details": [ "Underlying model and release", "Installed software version", "Specific camera model" ] } }, "provenance": { "kind": "source_review", "basis": "Recorded the deployment components that the original case accounts describe.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Thermal cameras watched for animals near Route 460. Detections activated warning signs, while 5G and satellite connections supported remote monitoring.", "editor_note": "" } }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Virginia Tech and VDOT identify ADDWS and Crosstek’s AI detection, but do not supply a named model, release, or checkpoint for the pilot.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "reason": "Virginia Tech and VDOT identify ADDWS and Crosstek’s AI detection, but do not supply a named model, release, or checkpoint for the pilot.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Crosstek’s detector for animals in thermal images. The pilot account gives no separate model name.", "editor_note": "" } }, "systems[].model_version": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "Virginia Tech and VDOT identify ADDWS and Crosstek’s AI detection, but do not supply a named model, release, or checkpoint for the pilot.", "record_fields": [ "system", "model_version" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "reason": "Virginia Tech and VDOT identify ADDWS and Crosstek’s AI detection, but do not supply a named model, release, or checkpoint for the pilot.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The pilot’s model and software releases are not disclosed.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Animal Detection and Driver Warning System (ADDWS), using Crosstek thermal-camera AI detection" } }, "provenance": { "kind": "source_review", "basis": "System identification follows the cited source chain and its stated historical or patient-matching qualifications.", "record_fields": [ "system" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Animal Detection and Driver Warning System (ADDWS), with detection software from Crosstek.", "editor_note": "" } }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Crosstek", "underlying_model_provider": "No separate model provider is identified." } }, "provenance": { "kind": "source_review", "basis": "The original operator or research-institution report identifies the application developer.", "record_fields": [ "developer", "system" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "VTTI/VDOT thermal-camera AI wildlife detection pilot on Route 460 near Grundy, Virginia: 97% detection accuracy and a 3 mph average speed drop at activated signs", "headline": "Drivers slowed down when signs warned of wildlife 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": [ "1b5658b0b4e4d534db95642c8c0b4ab3bec761d8b7e2156861af3d0ec07dffbe" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "derivation", "basis": "Recomputed the fingerprint after the system-identity and model-version review.", "record_fields": [ "revisions" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Gwen Sheldon reported for Virginia Tech on 20 October 2026 on a one-year Virginia Tech Transportation Institute (VTTI) pilot that ended in September 2026. It ran on Route 460 near Grundy in Southwest Virginia, sponsored by the Virginia Department of Transportation (VDOT) as part of the Corridor Q expansion. Eight portable solar-powered trailers with thermal cameras were installed on a 750-meter section of a 14-mile corridor that is home to about 100 elk. AI software from Crosstek analyzed the camera feeds. When wildlife was detected, dynamic message signs told motorists that wildlife had been detected ahead and to slow down. T-Mobile provided 5G and satellite links. The system achieved 97 percent overall accuracy in identifying medium and large wildlife. The initial analysis found an average speed reduction of 3 mph as vehicles approached activated signs. The report gives detection accuracy and driver speeds but no result on collisions or fatalities prevented. WCYB had reported the test on 19 November 2025. Its separate interview with Michelle Earl of VDOT describes animal detection and communication with the message boards. The measured results come from VTTI's own report. WCYB's earlier interview establishes deployment context, not independent verification of those results. VDOT names the pilot the Animal Detection and Driver Warning System (ADDWS). Its project page identifies VTTI as the research partner. Virginia Tech’s results report identifies Crosstek as the camera and AI-software supplier.", "replication": { "produced_by": [ "Portable solar trailers with thermal cameras on a known elk corridor", "AI detection software linked to dynamic message signs", "5G and satellite connectivity", "State DOT sponsorship within a highway expansion project" ], "must_hold": [ "Detection accuracy high enough to keep driver trust in the signs", "Sign placement giving drivers time to slow", "Power and connectivity at remote sites", "Crash data collection to test the unproven safety effect" ], "echoes": [ { "date": "2026-02-09", "setting": "LAARMA AI roadside alerts in the Kuranda cassowary trial, Queensland (AIBD-0104): fatal-crash reduction reported", "outcome": "recurred", "source": "AIBD-0104" }, { "date": "2026-07-18", "setting": "NSW Monaro Highway PAWS trial launched with the same detect-and-warn design; no results yet", "outcome": "unknown", "source": "https://aboutregional.com.au/ai-cameras-to-be-extra-set-of-eyes-on-roadside-wildlife-in-monaro-highway-trial/510964/" } ] } } }, "provenance": { "kind": "source_review", "basis": "Updated the fields affected by the system-identification review. Existing benefit-depth ratings and unrelated assessments are preserved.", "record_fields": [ "account", "replication" ], "source_refs": [ "AIBD-0359-source-0001", "AIBD-0359-source-0002", "AIBD-0359-source-0003", "AIBD-0359-source-0004" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "partial", "display": { "answer": "Solar-powered trailers carried thermal cameras beside an elk corridor. Crosstek’s software detected animals and activated warning signs. VDOT and Virginia Tech monitored the pilot through 5G and satellite connections.", "editor_note": "" } } }, "summary": { "total": 91, "addressed": 91, "known": 35, "reviewed": 19, "record_mapped": 18, "not_assessed": 54, "complete": false, "by_state": { "value_present": 35, "not_reported": 2, "unresolved": 0, "not_assessed": 54, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } } }, { "case_id": "AIBD-0240", "case_url": "records/AIBD-0240.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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). 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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. 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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-0253", "case_url": "records/AIBD-0253.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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. 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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." } ] } }, "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-0253-source-0001", "AIBD-0253-source-0002", "AIBD-0253-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": "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." } ] } }, "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": "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...", "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." } }, "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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "feeling the quake with no warning", "url": "https://www.haberler.com/guncel/karadeniz-teknik-universitesi-ogrencileri-tbmm-de-depremi-hissetti-18634729-haberi" }, { "label": "After", "value": "Bu deprem olmadan 30 saniye öncesinde telefonlarımıza bildirim geldi" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "the arrival of the seismic shaking (no warning)", "url": "https://www.newsbomb.gr/technologia/story/1649410/seismos-tora-pos-na-energopoiisete-ti-nea-efarmogi-tis-google-pou-enimeronei-gia-seismoys" }, { "label": "After", "value": "λίγα δευτερόλεπτα πριν σημειωθεί η σεισμική δόνηση" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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-0318", "case_url": "records/AIBD-0318.html", "reason": "Protective response, prevention unproven.", "details": [ { "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." }, { "label": "Comparison", "value": "camera traps without real-time notification", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10580963/" }, { "label": "Before", "value": "camera traps have lacked the real-time notification capabilities and practicality necessary for prevention of poaching" }, { "label": "After", "value": "notifications of tiger presence via email or push notification between 30 and 42 seconds after detections" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "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. 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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. 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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" } } }, "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-0318" } }, "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 paper documents detection and identification of armed poachers for investigation, with arrests claimed only in releases, and the protected asset is wildlife." } }, "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": "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.", "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": 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" }, "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 tigers and people on trails with embedded AI and send real-time images to forest officials. 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Detecting and transmitting the poachers' images 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": "2023-09-14", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "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.", "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.", "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": [ "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": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "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": [ "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": "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.", "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.", "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": "Anti-poaching 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": "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. 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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). 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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). 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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. 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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. 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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. 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-0358", "case_url": "records/AIBD-0358.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Embedded AI on the camera selected and transmitted images of tigers and humans within 30 to 42 seconds. That is the mechanism in the developer-affiliated BioScience paper. Whether any alert changed what villagers or officials did is not documented." }, { "label": "Comparison", "value": "conventional camera traps tripped by passing boars or falling leaves", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276" }, { "label": "After", "value": "almost wiped out false alarms" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0358", "title": "TrailGuard AI cameras in the Kanha-Pench corridor transmit tiger alerts to researchers and forest officials 30 to 42 seconds after detection in a 2022 pilot", "summary": "BioScience 2023: 12 TrailGuard AI cameras ran in the Kanha-Pench corridor, Madhya Pradesh, mid-May to mid-July 2022 (591 trap nights); tiger notifications arrived 30 to 42 seconds after detection.", "date_observed": "2023-09-14", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Dertien et al. reported in BioScience (published 14 September 2023) that 12 TrailGuard AI camera-alert systems ran in the Kanha-Pench corridor, Madhya Pradesh. They ran from mid-May to mid-July 2022 (591 trap nights). The cameras run embedded AI on an Intel Myriad chip to detect tigers and humans and transmit only images of interest. Researchers and forest department officials received tiger notifications 30 to 42 seconds after detection. AFP (Taipei Times, 6 October 2023) reported the study's claim. The cameras were “immediately effective, picking up a tiger just 300 meters from a village”. The Hindu (16 September 2023) reported the Madhya Pradesh deployment. It quoted H.S. Negi of the Global Tiger Forum on plans to incorporate the system in other tiger reserves. The paper's second site, Dudhwa Tiger Reserve in Uttar Pradesh, where a unit captured armed poachers, is recorded separately. No forest department document on the pilot is among the sources.", "category": "protective response", "unexpected": false, "purpose": "Real-time detection of tigers and humans for human-wildlife conflict early warning and anti-poaching. The reported detections and follow-up were the intended use.", "system": "TrailGuard AI camera-alert system with embedded AI (Intel Myriad processor; detectors trained with CVEDIA synthetic renderings)", "model_version": "2022 pilot units", "developer": "RESOLVE (design) with CVEDIA Ltd (AI training); units manufactured by Nightjar", "deployer": "Madhya Pradesh and Uttar Pradesh forest departments with Clemson University, Wildlife Institute of India and Global Tiger Forum", "location": "Kanha-Pench corridor, Madhya Pradesh, India", "domain": "Wildlife protection and human-wildlife conflict", "segment": "State forest departments and conservation research partners", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identified individual beneficiary. Villagers near three units are mentioned only as sources of daily notifications." }, { "group": "organizational", "depth": 1, "reason": "Researchers and forest department officials received tiger alerts 30 to 42 seconds after detection during the two-month pilot. No action taken on an alert is documented." }, { "group": "societal", "depth": null, "reason": "No measured effect on human-wildlife conflict in the two-month pilot of 12 cameras." } ], "ai_role": { "status": "assessed", "statement": "Embedded AI on the camera selected and transmitted images of tigers and humans within 30 to 42 seconds. That is the mechanism in the developer-affiliated BioScience paper. Whether any alert changed what villagers or officials did is not documented." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Dertien, J. S., Negi, H., Dinerstein, E., Krishnamurthy, R., Negi, H. S., Gopal, R., Gulick, S., Pathak, S. K., Kapoor, M., Yadav, P., Benitez, M., Ferreira, M., Wijnveen, A. J., Lee, A. T. L., Wright, B., Baldwin, R. F. (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10), 748-756. doi:10.1093/biosci/biad076", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://doi.org/10.1093/biosci/biad076", "date": "2023-09-14", "written_by": "makers", "quote": "We successfully captured and transmitted the first images of tigers using cameras with embedded AI and detected poachers." }, { "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." } ], "replication": { "produced_by": [ "Camera units with on-device detectors for tigers and humans placed on trails", "Cellular or satellite transmission to officials' phones and email", "Forest department staff willing to act on transmitted images", "Research consortium funding (Tigers United, Clemson, Khosla grant)" ], "must_hold": [ "Transmission path within cell range or via the slower protocol", "Detector accuracy high enough to avoid alert fatigue", "Investigative follow-up by forest and police authorities", "Battery life and concealment of units" ], "echoes": [ { "date": "2025-02-17", "setting": "Similipal Tiger Reserve, Odisha: TrailGuard AI cameras linked to multiple poacher arrests (recorded separately as AIBD-0102)", "model_version": null, "outcome": "recurred", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-fit-cameras-in-similipal-tiger-reserve-send-poaching-plummeting/article69225924.ece" }, { "date": null, "setting": "Plans to incorporate the system in other tiger reserves, per H.S. Negi of the Global Tiger Forum in The Hindu", "model_version": null, "outcome": "unknown", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" } ] }, "paired_incident": null, "headline": "TrailGuard AI sends tiger alerts within 30 to 42 seconds", "metric": "tiger notifications arrived 30 to 42 seconds after detection", "comparator": { "against": "conventional camera traps tripped by passing boars or falling leaves", "before": null, "after": "almost wiped out false alarms", "basis": "assumed", "quote": "They say their system was the first AI camera to identify and transmit a picture of a tiger, and it has almost wiped out false alarms — when traps are tripped by passing boars or falling leaves.", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276" }, "later": { "status": "scaled", "date": "2023-10-06", "note": "AFP reported that RESOLVE's Eric Dinerstein was widening the TrailGuard rollout to larger animals such as elephants; the BioScience article also notes continued transmission from Kanha-Pench and other reserves.", "quote": "Those uncertainties aside, Dinerstein is widening the rollout of TrailGuard — this time with even bigger animals in his sights.", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276" }, "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": "Narrowed to the Kanha-Pench pilot; the Dudhwa poacher detection from the same paper is recorded separately as AIBD-0318.", "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.taipeitimes.com/News/feat/archives/2023/10/06/2003807276; https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "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/Madhya_Pradesh", "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 and the makers' peer-reviewed study. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0358", "catalog_version": "1.0.0", "source_fingerprint": "b8c957579f35e7fc9fae4447086ddc6204eb2f610b0e4b44bc2b988b71a6e26c", "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" } ], "source_ids": [ "AIBD-0358-source-0001", "AIBD-0358-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": "RESOLVE (design) with CVEDIA Ltd (AI training); units manufactured by Nightjar", "deployer": "Madhya Pradesh and Uttar Pradesh forest departments with Clemson University, Wildlife Institute of India and Global Tiger Forum" } }, "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": "Embedded AI on the camera selected and transmitted images of tigers and humans within 30 to 42 seconds. That is the mechanism in the developer-affiliated BioScience paper. Whether any alert changed what villagers or officials did is not documented." } } }, "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": "Embedded AI on the camera selected and transmitted images of tigers and humans within 30 to 42 seconds. That is the mechanism in the developer-affiliated BioScience paper. Whether any alert changed what villagers or officials did is not documented." } } }, "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 identified individual beneficiary. Villagers near three units are mentioned only as sources of daily notifications." }, { "group": "organizational", "depth": 1, "reason": "Researchers and forest department officials received tiger alerts 30 to 42 seconds after detection during the two-month pilot. No action taken on an alert is documented." }, { "group": "societal", "reason": "No measured effect on human-wildlife conflict in the two-month pilot of 12 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[].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' peer-reviewed study. 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 identified individual beneficiary. Villagers near three units are mentioned only as sources of daily notifications." }, { "group": "organizational", "depth": 1, "reason": "Researchers and forest department officials received tiger alerts 30 to 42 seconds after detection during the two-month pilot. No action taken on an alert is documented." }, { "group": "societal", "reason": "No measured effect on human-wildlife conflict in the two-month pilot of 12 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 identified individual beneficiary. 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{ "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": "tiger notifications arrived 30 to 42 seconds after detection", "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": "2023-10-06", "note": "AFP reported that RESOLVE's Eric Dinerstein was widening the TrailGuard rollout to larger animals such as elephants; the BioScience article also notes continued transmission from Kanha-Pench and other reserves.", "quote": "Those uncertainties aside, Dinerstein is widening the rollout of TrailGuard — this time with even bigger animals in his sights.", "url": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276" } } }, "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-0358" } }, "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": "Dertien et al. reported in BioScience (published 14 September 2023) that 12 TrailGuard AI camera-alert systems ran in the Kanha-Pench corridor, Madhya Pradesh. They ran from mid-May to mid-July 2022 (591 trap nights). The cameras run embedded AI on an Intel Myriad chip to detect tigers and humans and transmit only images of interest. Researchers and forest department officials received tiger notifications 30 to 42 seconds after detection. AFP (Taipei Times, 6 October 2023) reported the study's claim. The cameras were “immediately effective, picking up a tiger just 300 meters from a village”. The Hindu (16 September 2023) reported the Madhya Pradesh deployment. It quoted H.S. Negi of the Global Tiger Forum on plans to incorporate the system in other tiger reserves. The paper's second site, Dudhwa Tiger Reserve in Uttar Pradesh, where a unit captured armed poachers, is recorded separately. No forest department document on the pilot is among the sources.", "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 identified individual beneficiary. Villagers near three units are mentioned only as sources of daily notifications." }, { "group": "organizational", "depth": 1, "reason": "Researchers and forest department officials received tiger alerts 30 to 42 seconds after detection during the two-month pilot. No action taken on an alert is documented." }, { "group": "societal", "reason": "No measured effect on human-wildlife conflict in the two-month pilot of 12 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": "Real-time detection of tigers and humans for human-wildlife conflict early warning and anti-poaching. The reported detections and follow-up 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": "Kanha-Pench corridor, Madhya Pradesh, India", "developer_country": "United States", "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": "Real-time detection of tigers and humans for human-wildlife conflict early warning and anti-poaching. The reported detections and follow-up 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": "2023-09-14", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Narrowed to the Kanha-Pench pilot; the Dudhwa poacher detection from the same paper is recorded separately as AIBD-0318.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276; https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "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/Madhya_Pradesh", "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": [ "Camera units with on-device detectors for tigers and humans placed on trails", "Cellular or satellite transmission to officials' phones and email", "Forest department staff willing to act on transmitted images", "Research consortium funding (Tigers United, Clemson, Khosla grant)" ], "must_hold": [ "Transmission path within cell range or via the slower protocol", "Detector accuracy high enough to avoid alert fatigue", "Investigative follow-up by forest and police authorities", "Battery life and concealment of units" ], "echoes": [ { "date": "2025-02-17", "setting": "Similipal Tiger Reserve, Odisha: TrailGuard AI cameras linked to multiple poacher arrests (recorded separately as AIBD-0102)", "outcome": "recurred", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-fit-cameras-in-similipal-tiger-reserve-send-poaching-plummeting/article69225924.ece" }, { "setting": "Plans to incorporate the system in other tiger reserves, per H.S. Negi of the Global Tiger Forum in The Hindu", "outcome": "unknown", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" } ] } } }, "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": [ "Camera units with on-device detectors for tigers and humans placed on trails", "Cellular or satellite transmission to officials' phones and email", "Forest department staff willing to act on transmitted images", "Research consortium funding (Tigers United, Clemson, Khosla grant)" ], "must_hold": [ "Transmission path within cell range or via the slower protocol", "Detector accuracy high enough to avoid alert fatigue", "Investigative follow-up by forest and police authorities", "Battery life and concealment of units" ], "echoes": [ { "date": "2025-02-17", "setting": "Similipal Tiger Reserve, Odisha: TrailGuard AI cameras linked to multiple poacher arrests (recorded separately as AIBD-0102)", "outcome": "recurred", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-fit-cameras-in-similipal-tiger-reserve-send-poaching-plummeting/article69225924.ece" }, { "setting": "Plans to incorporate the system in other tiger reserves, per H.S. Negi of the Global Tiger Forum in The Hindu", "outcome": "unknown", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" } ] } } }, "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": "Narrowed to the Kanha-Pench pilot; the Dudhwa poacher detection from the same paper is recorded separately as AIBD-0318.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276; https://www.taipeitimes.com/News/feat/archives/2023/10/06/2003807276", "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/Madhya_Pradesh", "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": "Wildlife protection and human-wildlife conflict", "segment": "State forest departments and conservation research 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": "peer-reviewed study", "citation": "Dertien, J. S., Negi, H., Dinerstein, E., Krishnamurthy, R., Negi, H. S., Gopal, R., Gulick, S., Pathak, S. K., Kapoor, M., Yadav, P., Benitez, M., Ferreira, M., Wijnveen, A. J., Lee, A. T. L., Wright, B., Baldwin, R. F. (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10), 748-756. doi:10.1093/biosci/biad076", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://doi.org/10.1093/biosci/biad076", "date": "2023-09-14", "written_by": "makers", "quote": "We successfully captured and transmitted the first images of tigers using cameras with embedded AI and detected poachers." }, { "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." } ] } }, "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-0358-source-0001", "AIBD-0358-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": "peer-reviewed study", "citation": "Dertien, J. S., Negi, H., Dinerstein, E., Krishnamurthy, R., Negi, H. S., Gopal, R., Gulick, S., Pathak, S. K., Kapoor, M., Yadav, P., Benitez, M., Ferreira, M., Wijnveen, A. J., Lee, A. T. L., Wright, B., Baldwin, R. F. (2023). Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. BioScience 73(10), 748-756. doi:10.1093/biosci/biad076", "title": "Mitigating human-wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system", "url": "https://doi.org/10.1093/biosci/biad076", "date": "2023-09-14", "written_by": "makers", "quote": "We successfully captured and transmitted the first images of tigers using cameras with embedded AI and detected poachers." }, { "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." } ] } }, "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": "BioScience 2023: 12 TrailGuard AI cameras ran in the Kanha-Pench corridor, Madhya Pradesh, mid-May to mid-July 2022 (591 trap nights); tiger notifications arrived 30 to 42 seconds after detection.", "account": "Dertien et al. reported in BioScience (published 14 September 2023) that 12 TrailGuard AI camera-alert systems ran in the Kanha-Pench corridor, Madhya Pradesh. They ran from mid-May to mid-July 2022 (591 trap nights). The cameras run embedded AI on an Intel Myriad chip to detect tigers and humans and transmit only images of interest. Researchers and forest department officials received tiger notifications 30 to 42 seconds after detection. AFP (Taipei Times, 6 October 2023) reported the study's claim. The cameras were “immediately effective, picking up a tiger just 300 meters from a village”. The Hindu (16 September 2023) reported the Madhya Pradesh deployment. It quoted H.S. Negi of the Global Tiger Forum on plans to incorporate the system in other tiger reserves. The paper's second site, Dudhwa Tiger Reserve in Uttar Pradesh, where a unit captured armed poachers, is recorded separately. No forest department document on the pilot 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": "value_present", "value": { "kind": "text", "text": "The camera’s models detected tigers and people and transmitted selected images." }, "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 the 2022 pilot units." }, "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": "2022 pilot units" } }, "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": "TrailGuard AI camera-alert system with embedded AI (Intel Myriad processor; detectors trained with CVEDIA synthetic renderings)" } }, "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": "RESOLVE (design) with CVEDIA Ltd (AI training); units manufactured by Nightjar" } }, "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 cameras in the Kanha-Pench corridor transmit tiger alerts to researchers and forest officials 30 to 42 seconds after detection in a 2022 pilot", "headline": "TrailGuard AI sends tiger alerts within 30 to 42 seconds" } }, "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": [ "b8c957579f35e7fc9fae4447086ddc6204eb2f610b0e4b44bc2b988b71a6e26c" ], "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 et al. reported in BioScience (published 14 September 2023) that 12 TrailGuard AI camera-alert systems ran in the Kanha-Pench corridor, Madhya Pradesh. They ran from mid-May to mid-July 2022 (591 trap nights). The cameras run embedded AI on an Intel Myriad chip to detect tigers and humans and transmit only images of interest. Researchers and forest department officials received tiger notifications 30 to 42 seconds after detection. AFP (Taipei Times, 6 October 2023) reported the study's claim. The cameras were “immediately effective, picking up a tiger just 300 meters from a village”. The Hindu (16 September 2023) reported the Madhya Pradesh deployment. It quoted H.S. Negi of the Global Tiger Forum on plans to incorporate the system in other tiger reserves. The paper's second site, Dudhwa Tiger Reserve in Uttar Pradesh, where a unit captured armed poachers, is recorded separately. No forest department document on the pilot is among the sources.", "replication": { "produced_by": [ "Camera units with on-device detectors for tigers and humans placed on trails", "Cellular or satellite transmission to officials' phones and email", "Forest department staff willing to act on transmitted images", "Research consortium funding (Tigers United, Clemson, Khosla grant)" ], "must_hold": [ "Transmission path within cell range or via the slower protocol", "Detector accuracy high enough to avoid alert fatigue", "Investigative follow-up by forest and police authorities", "Battery life and concealment of units" ], "echoes": [ { "date": "2025-02-17", "setting": "Similipal Tiger Reserve, Odisha: TrailGuard AI cameras linked to multiple poacher arrests (recorded separately as AIBD-0102)", "outcome": "recurred", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-fit-cameras-in-similipal-tiger-reserve-send-poaching-plummeting/article69225924.ece" }, { "setting": "Plans to incorporate the system in other tiger reserves, per H.S. Negi of the Global Tiger Forum in The Hindu", "outcome": "unknown", "source": "https://www.thehindu.com/sci-tech/energy-and-environment/ai-cameras-to-curb-poaching/article67315766.ece" } ] } } }, "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-0113", "case_url": "records/AIBD-0113.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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”). 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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", "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. 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"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": "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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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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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. 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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. 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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. 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": [ "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/2022-01-18-136b", "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": [ "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": "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", "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[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0111-source-0001", "AIBD-0111-source-0002", "AIBD-0111-source-0003", "AIBD-0111-source-0004", "AIBD-0111-source-0005", "AIBD-0111-source-0006", "AIBD-0111-source-0007", "AIBD-0111-source-0008", "AIBD-0111-source-0009", "AIBD-0111-source-0010", "AIBD-0111-source-0011", "AIBD-0111-source-0012", "AIBD-0111-source-0013", "AIBD-0111-source-0014" ], "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": "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", "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-0330", "case_url": "records/AIBD-0330.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Qualcomm attributes the fault that caused the collapse to a communication glitch in the prototype. The claimed benefit is that the robot's safety software then executed a designed “safe-collapse” to its knees. That claim comes only from Qualcomm's statement to Creative Bloq, and no engineering account exists. NDTV, Asianet, TechSpot and Creative Bloq describe a face-first or backward fall followed by staff dropping the robot. Whether any safety routine ran, and whether it protected anything, is unsupported. The AI platform's role in the event is the malfunction itself." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0330", "title": "Qualcomm's Dragonwing IQ10-powered humanoid may have run a “safe-collapse” sequence to protect its surroundings when it fell during a Computex 2026 demo in Taipei", "summary": "Vendor statement via Creative Bloq (27 July 2026): “The safety systems worked as intended.” NDTV, Asianet and TechSpot describe a face-first or backward collapse; nobody is reported at risk.", "date_observed": "2026-06-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The OECD AI Incidents entry files the event as an AI hazard. A humanoid robot on Qualcomm's Dragonwing IQ10 AI platform collapsed on stage during a live demo at Computex 2026 in Taipei after a communication glitch. The entry says it “executed a safe shutdown sequence, preventing harm”. The protective claim is Qualcomm's alone. Creative Bloq (27 July 2026, updated with a Qualcomm statement) quotes the company: “Live demos involve risk. At Computex in Taipei, a brief communication glitch triggered a fault in the prototype humanoid and initiated a controlled shutdown. The robot performed its designed safe-collapse sequence, lowering itself to its knees to protect the environment around it. The safety systems worked as intended.” NDTV (30 Jul), NDTV Profit (30 Jul, via The Indian Express) and Deccan Chronicle (30 Jul, “according to a news report”) relay the same statement. So do Asianet (31 Jul) and TechSpot (31 Jul). Gadget Review (28 Jul) says that the explanation “has not been independently confirmed”. It says Qualcomm had issued no official public statement at its time of publication. No statement was found on Qualcomm's or Neura Robotics' newsrooms. Qualcomm's release page rendered empty here, and Neura's news page lists no such item. The independent descriptions of the footage do not match a controlled kneel. NDTV says the robot “tumbled face-first onto the stage”, Asianet “crashed face-first”, and Creative Bloq “a crashing heap of plastic and metal”. TechSpot says it “collapsed like it had been shot”, “folding backwards on itself like a gymnast”, and was “dropped onto the floor” while being carried off. No person or object is reported at risk, and no beneficiary is identifiable. TechSpot identifies the robot as Neura Robotics' 4NE1 Gen 3.5 on Qualcomm's Dragonwing IQ10 robotics reference design. That design is listed at up to 700 TOPS, 6–8 hours' runtime, 100 kg payload, 5 ft 9 in. Gadget Review instead places the 4NE1 Gen 3.5's debut in Munich and calls the Taipei unit “Qualcomm's Dragonwing IQ10 humanoid”. The outlets say the fall happened during Qualcomm's keynote presentation. The Computex schedule lists Cristiano Amon's keynote on 1 June 2026 at TaiNEX 2, and the show ran 2–5 June. The sources do not give the exact day of the keynote. The video surfaced on social media on 22 July 2026 (a post embedded by Deccan Chronicle) and spread from 27–30 July. The Monitor's 28 July date is the coverage date. Four of the Monitor's nine listed sources (Fox News, Newsweek, Global Times, Tech Times) do not mention the incident. TechSpot's embedded November 2025 post is the Moscow AIdol clip cited for comparison.", "category": "protective response", "unexpected": false, "purpose": "Qualcomm describes the “safe-collapse” sequence as designed behavior that lowers the robot to its knees to protect its surroundings after a fault. The Dragonwing IQ10 is a reference design for humanoid and industrial robots.", "system": "Prototype humanoid on Qualcomm's Dragonwing IQ10 Robotics Reference Design (up to 700 TOPS on-device AI); identified by TechSpot as Neura Robotics' 4NE1 Gen 3.5, which Gadget Review instead places at a Munich debut", "model_version": "Dragonwing IQ10 RRD prototype; 4NE1 Gen 3.5 per TechSpot", "developer": "Qualcomm (Dragonwing IQ10 platform); robot hardware attributed to Neura Robotics by TechSpot and Gadget Review", "deployer": "Qualcomm, at its Computex 2026 showcase in Taipei", "location": "Taipei, Taiwan (Computex 2026, TaiNEX 2)", "domain": "Robotics: humanoid robot demonstrations", "segment": "Safety fallback behavior in humanoid robots", "benefit_type": "Increased Product Safety", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2026-07-28-5a37" }, "ratings": [ { "group": "personal", "depth": null, "reason": "No person on stage is reported at risk from the fall. The only account of a protective kneel is Qualcomm's, contradicted by descriptions of a face-first crash." }, { "group": "organizational", "depth": null, "reason": "Qualcomm says that the incident “provided valuable real-world data”. No measure of damage avoided or reliability gained is given." } ], "ai_role": { "status": "assessed", "statement": "Qualcomm attributes the fault that caused the collapse to a communication glitch in the prototype. The claimed benefit is that the robot's safety software then executed a designed “safe-collapse” to its knees. That claim comes only from Qualcomm's statement to Creative Bloq, and no engineering account exists. NDTV, Asianet, TechSpot and Creative Bloq describe a face-first or backward fall followed by staff dropping the robot. Whether any safety routine ran, and whether it protected anything, is unsupported. The AI platform's role in the event is the malfunction itself." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2026-07-28-5a37, 'Humanoid Robot Malfunctions and Collapses During Live AI Demo at Computex 2026' (AI-generated summary; filed as an AI hazard).", "title": "Humanoid Robot Malfunctions and Collapses During Live AI Demo at Computex 2026", "url": "https://oecd.ai/en/incidents/2026-07-28-5a37", "date": "2026-07-28", "written_by": "independent", "quote": "The robot executed a safe shutdown sequence, preventing harm, but the incident highlighted potential risks of AI system malfunctions." }, { "kind": "news report", "citation": "Creative Bloq, 27 July 2026 (updated to include a statement from Qualcomm), 'This video of an AI robot collapsing mid-presentation gets funnier every time I watch it'.", "title": "This video of an AI robot collapsing mid-presentation gets funnier every time I watch it", "url": "https://www.creativebloq.com/design/product-design/this-video-of-an-ai-robot-collapsing-mid-presentation-gets-funnier-every-time-i-watch-it", "date": "2026-07-27", "written_by": "independent", "quote": "The robot performed its designed ‘safe-collapse’ sequence, lowering itself to its knees to protect the environment around it. The safety systems worked as intended." }, { "kind": "news report", "citation": "NDTV (ed. Abhinav Singh), 30 July 2026, 'Watch: AI Humanoid Robot Collapses In Taiwan During Live Demo, Video Viral'.", "title": "Watch: AI Humanoid Robot Collapses In Taiwan During Live Demo, Video Viral", "url": "https://www.ndtv.com/offbeat/watch-ai-humanoid-robot-collapses-in-taiwan-during-live-demo-video-viral-11845190", "date": "2026-07-30", "written_by": "independent", "quote": "A humanoid robot in Taiwan tumbled face-first onto the stage, leaving the developers red-faced as the video of the incident went viral on social media." }, { "kind": "news report", "citation": "NDTV Profit (Shreya Sur), 30 July 2026, 'Humanoid Robot Running On Qualcomm AI Chip 'Dies' On Stage At Computex 2026'.", "title": "Humanoid Robot Running On Qualcomm AI Chip 'Dies' On Stage At Computex 2026", "url": "https://www.ndtvprofit.com/technology/humanoid-robot-running-on-qualcomm-ai-chip-dies-on-stage-at-computex-2026-11844305", "date": "2026-07-30", "written_by": "independent", "quote": "According to The Indian Express, Qualcomm later clarified that the incident was caused by a brief communication glitch that triggered a fault in the prototype." }, { "kind": "news report", "citation": "TechSpot, 31 July 2026, 'Qualcomm's humanoid robot demo went hilariously wrong when the machine just dropped dead on stage'.", "title": "Qualcomm's humanoid robot demo went hilariously wrong when the machine just dropped dead on stage", "url": "https://www.techspot.com/news/113313-qualcomm-powered-humanoid-robot-collapses-stage-like-shot.html", "date": "2026-07-31", "written_by": "independent", "quote": "It collapsed spectacularly, folding backwards on itself like a gymnast." }, { "kind": "news report", "citation": "Deccan Chronicle (DC Web Desk), 30 July 2026, 'Humanoid Robot Powered by Qualcomm Collapses on Stage at Computex 2026' (embeds a 22 July 2026 post of the video).", "title": "Humanoid Robot Powered by Qualcomm Collapses on Stage at Computex 2026", "url": "https://www.deccanchronicle.com/technology/humanoid-robot-powered-by-qualcomm-collapses-on-stage-at-computex-2026-1975284", "date": "2026-07-30", "written_by": "independent", "quote": "According to a news report, Qualcomm said the collapse was caused by a brief communication glitch that triggered a fault in the prototype." }, { "kind": "news report", "citation": "Asianet Newsable (Shweta Kumari), 31 July 2026, 'Taiwan: AI Humanoid Robot Crashes Face-First Onstage During Live Demo At Computex 2026 (WATCH)'.", "title": "Taiwan: AI Humanoid Robot Crashes Face-First Onstage During Live Demo At Computex 2026 (WATCH)", "url": "https://newsable.asianetnews.com/world/taiwan-ai-humanoid-robot-crashes-onstage-during-live-demo-at-computex-2026-articleshow-bcr0em1", "date": "2026-07-31", "written_by": "independent", "quote": "the machine dramatically crashed face-first onto the stage, leaving its developers scrambling" }, { "kind": "news report", "citation": "Gadget Review (Al Landes), 28 July 2026, 'A Humanoid Robot 'Dies' On Stage at Computex 2026, Gets Carried Off'.", "title": "A Humanoid Robot “Dies” On Stage at Computex 2026, Gets Carried Off", "url": "https://www.gadgetreview.com/a-humanoid-robot-dies-on-stage-at-computex-2026-gets-carried-off", "date": "2026-07-28", "written_by": "independent", "quote": "That explanation has not been independently confirmed — Qualcomm had not issued an official public statement on the incident at time of publication." }, { "kind": "other", "citation": "COMPUTEX Keynote & Forum 2026 programme (events.computextaipei.com.tw), accessed by the earlier verifier on 20 October 2026: Qualcomm CEO Cristiano Amon keynote on 1 June 2026 at TaiNEX 2.", "title": "COMPUTEX Keynote & Forum 2026", "url": "https://events.computextaipei.com.tw/en/", "date": null, "written_by": "independent", "quote": "Keynote June 1 - 3, 2026 TaiNEX 2 & TWTC, Taipei Taiwan" }, { "kind": "press release", "citation": "Neura Robotics, product page 'Humanoid Robot 4NE1 for Work and Life', accessed 20 October 2026 (current 4NE1 specifications; no Computex statement).", "title": "Humanoid Robot 4NE1 for Work and Life | NEURA Robotics", "url": "https://neura-robotics.com/products/4ne1", "date": null, "written_by": "makers", "quote": "4NE1 brings safe, intelligent automation to the real world – wherever humans are." } ], "replication": { "produced_by": [ "A controlled-shutdown routine triggered when a communication glitch caused a fault, in Qualcomm's account", "A prototype humanoid on the Dragonwing IQ10 reference design demonstrated on a stage" ], "must_hold": [ "Independent engineering evidence that the robot knelt rather than fell. The footage descriptions say it fell", "A person or object within reach that the behavior protected. None is reported" ], "echoes": [ { "date": null, "setting": "Moscow, Russia, November 2025: the AIdol humanoid fell face-first at its debut (TechSpot and NDTV cite it for comparison); its developer called it a “real-time training” session and no safe-collapse claim was made", "model_version": "AIdol (unrelated system)", "outcome": "unknown", "source": "https://www.techspot.com/news/113313-qualcomm-powered-humanoid-robot-collapses-stage-like-shot.html" } ] }, "paired_incident": null, "headline": "Qualcomm's humanoid may have collapsed safely at Computex", "metric": null, "comparator": null, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the late-July 2026 coverage was found; Qualcomm's safe-collapse statement is the benefit claim itself, and Gadget Review noted it had not been independently confirmed.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": "only Qualcomm's account of a safe collapse, and nobody at risk", "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: only Qualcomm's account of a safe collapse, and nobody at risk.", "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": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Qualcomm; https://en.wikipedia.org/wiki/Qualcomm", "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, one other independent source 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-0330", "catalog_version": "1.0.0", "source_fingerprint": "43b1c0ac3b6faf3c18cf0331e4190700350e8389cf0f15809b75ffa042feb2ea", "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" } ], "source_ids": [ "AIBD-0330-source-0001", "AIBD-0330-source-0002", "AIBD-0330-source-0003", "AIBD-0330-source-0004", "AIBD-0330-source-0005", "AIBD-0330-source-0006", "AIBD-0330-source-0007", "AIBD-0330-source-0008", "AIBD-0330-source-0009", "AIBD-0330-source-0010" ], "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": "Qualcomm (Dragonwing IQ10 platform); robot hardware attributed to Neura Robotics by TechSpot and Gadget Review", "deployer": "Qualcomm, at its Computex 2026 showcase in Taipei" } }, "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": "Qualcomm attributes the fault that caused the collapse to a communication glitch in the prototype. The claimed benefit is that the robot's safety software then executed a designed “safe-collapse” to its knees. That claim comes only from Qualcomm's statement to Creative Bloq, and no engineering account exists. NDTV, Asianet, TechSpot and Creative Bloq describe a face-first or backward fall followed by staff dropping the robot. Whether any safety routine ran, and whether it protected anything, is unsupported. 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Whether any safety routine ran, and whether it protected anything, is unsupported. 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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": "2026-07-28-5a37" } } }, "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": "Nothing dated after the late-July 2026 coverage was found; Qualcomm's safe-collapse statement is the benefit claim itself, and Gadget Review noted it had not been independently confirmed." } } }, "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-0330" } }, "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": "The OECD AI Incidents entry files the event as an AI hazard. A humanoid robot on Qualcomm's Dragonwing IQ10 AI platform collapsed on stage during a live demo at Computex 2026 in Taipei after a communication glitch. The entry says it “executed a safe shutdown sequence, preventing harm”. The protective claim is Qualcomm's alone. Creative Bloq (27 July 2026, updated with a Qualcomm statement) quotes the company: “Live demos involve risk. At Computex in Taipei, a brief communication glitch triggered a fault in the prototype humanoid and initiated a controlled shutdown. The robot performed its designed safe-collapse sequence, lowering itself to its knees to protect the environment around it. The safety systems worked as intended.” NDTV (30 Jul), NDTV Profit (30 Jul, via The Indian Express) and Deccan Chronicle (30 Jul, “according to a news report”) relay the same statement. So do Asianet (31 Jul) and TechSpot (31 Jul). Gadget Review (28 Jul) says that the explanation “has not been independently confirmed”. It says Qualcomm had issued no official public statement at its time of publication. No statement was found on Qualcomm's or Neura Robotics' newsrooms. Qualcomm's release page rendered empty here, and Neura's news page lists no such item. The independent descriptions of the footage do not match a controlled kneel. NDTV says the robot “tumbled face-first onto the stage”, Asianet “crashed face-first”, and Creative Bloq “a crashing heap of plastic and metal”. TechSpot says it “collapsed like it had been shot”, “folding backwards on itself like a gymnast”, and was “dropped onto the floor” while being carried off. No person or object is reported at risk, and no beneficiary is identifiable. TechSpot identifies the robot as Neura Robotics' 4NE1 Gen 3.5 on Qualcomm's Dragonwing IQ10 robotics reference design. That design is listed at up to 700 TOPS, 6–8 hours' runtime, 100 kg payload, 5 ft 9 in. Gadget Review instead places the 4NE1 Gen 3.5's debut in Munich and calls the Taipei unit “Qualcomm's Dragonwing IQ10 humanoid”. The outlets say the fall happened during Qualcomm's keynote presentation. The Computex schedule lists Cristiano Amon's keynote on 1 June 2026 at TaiNEX 2, and the show ran 2–5 June. The sources do not give the exact day of the keynote. The video surfaced on social media on 22 July 2026 (a post embedded by Deccan Chronicle) and spread from 27–30 July. The Monitor's 28 July date is the coverage date. Four of the Monitor's nine listed sources (Fox News, Newsweek, Global Times, Tech Times) do not mention the incident. TechSpot's embedded November 2025 post is the Moscow AIdol clip cited for comparison.", "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 person on stage is reported at risk from the fall. The only account of a protective kneel is Qualcomm's, contradicted by descriptions of a face-first crash." }, { "group": "organizational", "reason": "Qualcomm says that the incident “provided valuable real-world data”. No measure of damage avoided or reliability gained 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": "Qualcomm describes the “safe-collapse” sequence as designed behavior that lowers the robot to its knees to protect its surroundings after a fault. The Dragonwing IQ10 is a reference design for humanoid and industrial robots." } }, "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": "Taipei, Taiwan (Computex 2026, TaiNEX 2)", "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": "Qualcomm describes the “safe-collapse” sequence as designed behavior that lowers the robot to its knees to protect its surroundings after a fault. 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The safety systems worked as intended." }, { "kind": "news report", "citation": "NDTV (ed. 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Abhinav Singh), 30 July 2026, 'Watch: AI Humanoid Robot Collapses In Taiwan During Live Demo, Video Viral'.", "title": "Watch: AI Humanoid Robot Collapses In Taiwan During Live Demo, Video Viral", "url": "https://www.ndtv.com/offbeat/watch-ai-humanoid-robot-collapses-in-taiwan-during-live-demo-video-viral-11845190", "date": "2026-07-30", "written_by": "independent", "quote": "A humanoid robot in Taiwan tumbled face-first onto the stage, leaving the developers red-faced as the video of the incident went viral on social media." }, { "kind": "news report", "citation": "NDTV Profit (Shreya Sur), 30 July 2026, 'Humanoid Robot Running On Qualcomm AI Chip 'Dies' On Stage At Computex 2026'.", "title": "Humanoid Robot Running On Qualcomm AI Chip 'Dies' On Stage At Computex 2026", "url": "https://www.ndtvprofit.com/technology/humanoid-robot-running-on-qualcomm-ai-chip-dies-on-stage-at-computex-2026-11844305", "date": "2026-07-30", "written_by": "independent", "quote": "According to The Indian Express, Qualcomm later clarified that the incident was caused by a brief communication glitch that triggered a fault in the prototype." }, { "kind": "news report", "citation": "TechSpot, 31 July 2026, 'Qualcomm's humanoid robot demo went hilariously wrong when the machine just dropped dead on stage'.", "title": "Qualcomm's humanoid robot demo went hilariously wrong when the machine just dropped dead on stage", "url": "https://www.techspot.com/news/113313-qualcomm-powered-humanoid-robot-collapses-stage-like-shot.html", "date": "2026-07-31", "written_by": "independent", "quote": "It collapsed spectacularly, folding backwards on itself like a gymnast." }, { "kind": "news report", "citation": "Deccan Chronicle (DC Web Desk), 30 July 2026, 'Humanoid Robot Powered by Qualcomm Collapses on Stage at Computex 2026' (embeds a 22 July 2026 post of the video).", "title": "Humanoid Robot Powered by Qualcomm Collapses on Stage at Computex 2026", "url": "https://www.deccanchronicle.com/technology/humanoid-robot-powered-by-qualcomm-collapses-on-stage-at-computex-2026-1975284", "date": "2026-07-30", "written_by": "independent", "quote": "According to a news report, Qualcomm said the collapse was caused by a brief communication glitch that triggered a fault in the prototype." }, { "kind": "news report", "citation": "Asianet Newsable (Shweta Kumari), 31 July 2026, 'Taiwan: AI Humanoid Robot Crashes Face-First Onstage During Live Demo At Computex 2026 (WATCH)'.", "title": "Taiwan: AI Humanoid Robot Crashes Face-First Onstage During Live Demo At Computex 2026 (WATCH)", "url": "https://newsable.asianetnews.com/world/taiwan-ai-humanoid-robot-crashes-onstage-during-live-demo-at-computex-2026-articleshow-bcr0em1", "date": "2026-07-31", "written_by": "independent", "quote": "the machine dramatically crashed face-first onto the stage, leaving its developers scrambling" }, { "kind": "news report", "citation": "Gadget Review (Al Landes), 28 July 2026, 'A Humanoid Robot 'Dies' On Stage at Computex 2026, Gets Carried Off'.", "title": "A Humanoid Robot “Dies” On Stage at Computex 2026, Gets Carried Off", "url": "https://www.gadgetreview.com/a-humanoid-robot-dies-on-stage-at-computex-2026-gets-carried-off", "date": "2026-07-28", "written_by": "independent", "quote": "That explanation has not been independently confirmed — Qualcomm had not issued an official public statement on the incident at time of publication." }, { "kind": "other", "citation": "COMPUTEX Keynote & Forum 2026 programme (events.computextaipei.com.tw), accessed by the earlier verifier on 20 October 2026: Qualcomm CEO Cristiano Amon keynote on 1 June 2026 at TaiNEX 2.", "title": "COMPUTEX Keynote & Forum 2026", "url": "https://events.computextaipei.com.tw/en/", "written_by": "independent", "quote": "Keynote June 1 - 3, 2026 TaiNEX 2 & TWTC, Taipei Taiwan" }, { "kind": "press release", "citation": "Neura Robotics, product page 'Humanoid Robot 4NE1 for Work and Life', accessed 20 October 2026 (current 4NE1 specifications; no Computex statement).", "title": "Humanoid Robot 4NE1 for Work and Life | NEURA Robotics", "url": "https://neura-robotics.com/products/4ne1", "written_by": "makers", "quote": "4NE1 brings safe, intelligent automation to the real world – wherever humans are." } ] } }, "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 statement via Creative Bloq (27 July 2026): “The safety systems worked as intended.” NDTV, Asianet and TechSpot describe a face-first or backward collapse; nobody is reported at risk.", "account": "The OECD AI Incidents entry files the event as an AI hazard. A humanoid robot on Qualcomm's Dragonwing IQ10 AI platform collapsed on stage during a live demo at Computex 2026 in Taipei after a communication glitch. The entry says it “executed a safe shutdown sequence, preventing harm”. The protective claim is Qualcomm's alone. Creative Bloq (27 July 2026, updated with a Qualcomm statement) quotes the company: “Live demos involve risk. At Computex in Taipei, a brief communication glitch triggered a fault in the prototype humanoid and initiated a controlled shutdown. The robot performed its designed safe-collapse sequence, lowering itself to its knees to protect the environment around it. The safety systems worked as intended.” NDTV (30 Jul), NDTV Profit (30 Jul, via The Indian Express) and Deccan Chronicle (30 Jul, “according to a news report”) relay the same statement. So do Asianet (31 Jul) and TechSpot (31 Jul). Gadget Review (28 Jul) says that the explanation “has not been independently confirmed”. It says Qualcomm had issued no official public statement at its time of publication. No statement was found on Qualcomm's or Neura Robotics' newsrooms. Qualcomm's release page rendered empty here, and Neura's news page lists no such item. The independent descriptions of the footage do not match a controlled kneel. NDTV says the robot “tumbled face-first onto the stage”, Asianet “crashed face-first”, and Creative Bloq “a crashing heap of plastic and metal”. TechSpot says it “collapsed like it had been shot”, “folding backwards on itself like a gymnast”, and was “dropped onto the floor” while being carried off. No person or object is reported at risk, and no beneficiary is identifiable. TechSpot identifies the robot as Neura Robotics' 4NE1 Gen 3.5 on Qualcomm's Dragonwing IQ10 robotics reference design. That design is listed at up to 700 TOPS, 6–8 hours' runtime, 100 kg payload, 5 ft 9 in. Gadget Review instead places the 4NE1 Gen 3.5's debut in Munich and calls the Taipei unit “Qualcomm's Dragonwing IQ10 humanoid”. The outlets say the fall happened during Qualcomm's keynote presentation. The Computex schedule lists Cristiano Amon's keynote on 1 June 2026 at TaiNEX 2, and the show ran 2–5 June. The sources do not give the exact day of the keynote. The video surfaced on social media on 22 July 2026 (a post embedded by Deccan Chronicle) and spread from 27–30 July. The Monitor's 28 July date is the coverage date. Four of the Monitor's nine listed sources (Fox News, Newsweek, Global Times, Tech Times) do not mention the incident. TechSpot's embedded November 2025 post is the Moscow AIdol clip cited for comparison." } }, "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": "value_present", "value": { "kind": "text", "text": "The prototype used Dragonwing IQ10 RRD. TechSpot identified the robot as 4NE1 Gen 3.5." }, "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": "Dragonwing IQ10 RRD prototype; 4NE1 Gen 3.5 per TechSpot" } }, "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": "Prototype humanoid on Qualcomm's Dragonwing IQ10 Robotics Reference Design (up to 700 TOPS on-device AI); identified by TechSpot as Neura Robotics' 4NE1 Gen 3.5, which Gadget Review instead places at a Munich debut" } }, "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": "Qualcomm (Dragonwing IQ10 platform); robot hardware attributed to Neura Robotics by TechSpot and Gadget Review" } }, "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": "Qualcomm's Dragonwing IQ10-powered humanoid may have run a “safe-collapse” sequence to protect its surroundings when it fell during a Computex 2026 demo in Taipei", "headline": "Qualcomm's humanoid may have collapsed safely at Computex" } }, "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": [ "43b1c0ac3b6faf3c18cf0331e4190700350e8389cf0f15809b75ffa042feb2ea" ], "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 files the event as an AI hazard. A humanoid robot on Qualcomm's Dragonwing IQ10 AI platform collapsed on stage during a live demo at Computex 2026 in Taipei after a communication glitch. The entry says it “executed a safe shutdown sequence, preventing harm”. The protective claim is Qualcomm's alone. Creative Bloq (27 July 2026, updated with a Qualcomm statement) quotes the company: “Live demos involve risk. At Computex in Taipei, a brief communication glitch triggered a fault in the prototype humanoid and initiated a controlled shutdown. The robot performed its designed safe-collapse sequence, lowering itself to its knees to protect the environment around it. The safety systems worked as intended.” NDTV (30 Jul), NDTV Profit (30 Jul, via The Indian Express) and Deccan Chronicle (30 Jul, “according to a news report”) relay the same statement. So do Asianet (31 Jul) and TechSpot (31 Jul). Gadget Review (28 Jul) says that the explanation “has not been independently confirmed”. It says Qualcomm had issued no official public statement at its time of publication. No statement was found on Qualcomm's or Neura Robotics' newsrooms. Qualcomm's release page rendered empty here, and Neura's news page lists no such item. The independent descriptions of the footage do not match a controlled kneel. NDTV says the robot “tumbled face-first onto the stage”, Asianet “crashed face-first”, and Creative Bloq “a crashing heap of plastic and metal”. TechSpot says it “collapsed like it had been shot”, “folding backwards on itself like a gymnast”, and was “dropped onto the floor” while being carried off. No person or object is reported at risk, and no beneficiary is identifiable. TechSpot identifies the robot as Neura Robotics' 4NE1 Gen 3.5 on Qualcomm's Dragonwing IQ10 robotics reference design. That design is listed at up to 700 TOPS, 6–8 hours' runtime, 100 kg payload, 5 ft 9 in. Gadget Review instead places the 4NE1 Gen 3.5's debut in Munich and calls the Taipei unit “Qualcomm's Dragonwing IQ10 humanoid”. The outlets say the fall happened during Qualcomm's keynote presentation. The Computex schedule lists Cristiano Amon's keynote on 1 June 2026 at TaiNEX 2, and the show ran 2–5 June. The sources do not give the exact day of the keynote. The video surfaced on social media on 22 July 2026 (a post embedded by Deccan Chronicle) and spread from 27–30 July. The Monitor's 28 July date is the coverage date. Four of the Monitor's nine listed sources (Fox News, Newsweek, Global Times, Tech Times) do not mention the incident. TechSpot's embedded November 2025 post is the Moscow AIdol clip cited for comparison.", "replication": { "produced_by": [ "A controlled-shutdown routine triggered when a communication glitch caused a fault, in Qualcomm's account", "A prototype humanoid on the Dragonwing IQ10 reference design demonstrated on a stage" ], "must_hold": [ "Independent engineering evidence that the robot knelt rather than fell. The footage descriptions say it fell", "A person or object within reach that the behavior protected. None is reported" ], "echoes": [ { "setting": "Moscow, Russia, November 2025: the AIdol humanoid fell face-first at its debut (TechSpot and NDTV cite it for comparison); its developer called it a “real-time training” session and no safe-collapse claim was made", "model_version": "AIdol (unrelated system)", "outcome": "unknown", "source": "https://www.techspot.com/news/113313-qualcomm-powered-humanoid-robot-collapses-stage-like-shot.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": 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-0327", "case_url": "records/AIBD-0327.html", "reason": "Specific harm prevented.", "details": [ { "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." }, { "label": "Comparison", "value": "Iron Dome interceptor missiles", "url": "http://theconversation.com/israels-iron-beam-why-laser-weapons-are-no-longer-science-fiction-277390" }, { "label": "Before", "value": "A single Iron Dome interceptor missile costs about US$50,000" }, { "label": "After", "value": "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" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "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. 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" }, "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": "one drone intercepted near Shlomi", "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": "2026-02-28-d62f" } } }, "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-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. 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" }, "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 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": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Defense: air defense", "segment": "Directed-energy interception of rockets, mortars and drones" } }, "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 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[].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-0325", "case_url": "records/AIBD-0325.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "ChatGPT's output (advice to give paraffin oil, then nine bottles in turn with breathing exercises) is documented only by the cousin's own post. The chat transcripts were not published (“too many to publish all”, per Masrawy's summary of the post). Dean Alaa Attia contradicts its effect: he says that the patient had no clinical signs of phosphine poisoning and denied swallowing the pill. Dr Nermin Hamdi's report that Assiut University denied the case involved the grain pill also contradicts it. Dr Mahmoud Amr says that oil is a recognized first aid but not a cure. Whether the hospital followed any of the AI's instructions is unsupported by any source other than the post." } ], "filters": { "category": "Specific harm prevented", "comparison": "undocumented" }, "record": { "id": "AIBD-0325", "title": "ChatGPT's paraffin-oil advice may have saved Islam Adel's 17-year-old cousin after an aluminum phosphide “grain pill” ingestion in Assiut", "summary": "A Facebook post of 24 May 2025 credits ChatGPT with first aid. Assiut Faculty of Medicine dean Alaa Attia says that the youth had no phosphine symptoms and said he threw the pill into water.", "date_observed": "2025-05-24", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "El Balad (24 May 2025, 12:39) reproduces a Facebook post by Islam Adel, a commercial-control inspector. He wrote it at 1 a.m. after “24 hours” had passed since his cousin Mohamed Yousry (17) swallowed part of a “grain pill” (aluminum phosphide). The pill came from samples Adel had seized and kept at the family's old house. The cousin took it after a quarrel with his father. Adel writes that the Abnoub central hospital refused the case. He writes that within minutes he asked ChatGPT for the best course. It advised paraffin oil to coat the pill and limit phosphine release. The youth drank a bottle from a pharmacy before they went to Assiut University Hospital's poison reception. Doctors there said there was no treatment protocol and the case would die. He then gave instructions from “an advanced AI model and a limited-use trial version of GPT-5”. Nine bottles of paraffin oil were given in turn with breathing exercises. The cousin stayed conscious, vomited for hours and was discharged stable. Dostor (24 May 2025, 22:40) reports the post and the reply of Dr Alaa Attia, dean of the Faculty of Medicine at Assiut University. He said precise medical examinations showed no signs or symptoms of swallowing the pill. The youth denied taking it and admitted throwing it into a water basin. The dean added that the hospital is researching paraffin oil with acetylcysteine as an adjunct. The protocol is experimental and not officially adopted. Masrawy (25 May 2025) quotes the dean that the youth, about 17, showed no clinical symptoms of phosphine poisoning. The team questioned him and he categorically denied swallowing the pill. He cited a family dispute over travel to Italy. Dostor (25 May 2025) adds toxicologist Dr Mahmoud Amr, founder of the National Poison Center. He says oils are indeed a necessary first aid in grain-pill poisoning. Al Jazeera Mubasher (26 May 2025) quotes Amr that the claim that paraffin oil rescues such a case “has no scientific basis”. It quotes Cairo University toxicologist Dr Nermin Hamdi that Assiut University denied the case involved the grain pill. Hamdi adds that giving oil is first aid, not treatment. The Monitor lists 13 syndications. Al Arabiya's syndication was not available. The ingestion date is not fixed by any source.", "category": "harm prevented", "unexpected": true, "purpose": "ChatGPT is a general-purpose assistant. The cousin used it for emergency first-aid advice and, by his account, for instructions during hospital care. The claimed benefit (a survival) is outside any stated purpose of the system.", "system": "ChatGPT (OpenAI), described in the post as 'an advanced AI model and a limited-use trial version of GPT-5'", "model_version": "GPT-5 trial version, per the cousin's post; unverified", "developer": "OpenAI", "deployer": "Islam Adel, the patient's cousin, as a consumer user", "location": "Abnoub and Assiut University Hospital, Assiut Governorate, Egypt", "domain": "Healthcare: emergency toxicology", "segment": "Consumer chatbot used for first-aid advice", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": { "oecd_aim": "2025-05-24-415b" }, "ratings": [ { "group": "personal", "depth": null, "reason": "The survival claim rests on the cousin's post. The dean says that there was no poisoning to survive. No outcome attributable to the advice is therefore documented." }, { "group": "societal", "depth": null, "reason": "Al Jazeera Mubasher and Dostor report experts warning that the “trend” could kill. No wider effect is documented." } ], "ai_role": { "status": "assessed", "statement": "ChatGPT's output (advice to give paraffin oil, then nine bottles in turn with breathing exercises) is documented only by the cousin's own post. The chat transcripts were not published (“too many to publish all”, per Masrawy's summary of the post). Dean Alaa Attia contradicts its effect: he says that the patient had no clinical signs of phosphine poisoning and denied swallowing the pill. Dr Nermin Hamdi's report that Assiut University denied the case involved the grain pill also contradicts it. Dr Mahmoud Amr says that oil is a recognized first aid but not a cure. Whether the hospital followed any of the AI's instructions is unsupported by any source other than the post." }, "evidence": [ { "kind": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-05-24-415b, 'AI Intervention Saves Teen from Poisoning' (AI-generated summary).", "title": "AI Intervention Saves Teen from Poisoning", "url": "https://oecd.ai/en/incidents/2025-05-24-415b", "date": "2025-05-24", "written_by": "independent", "quote": "The AI recommended immediately coating the pill with paraffin oil to mitigate poisoning effects, helping counteract doctors’ initial pessimism and contributing to the teen’s survival." }, { "kind": "news report", "citation": "Sada El Balad (Sara Abdallah), 24 May 2025, 'عاد من الموت. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة' (reproduces Islam Adel's Facebook post).", "title": "عاد من الموت.. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة", "url": "https://www.elbalad.news/6583785", "date": "2025-05-24", "written_by": "independent", "quote": "خلال دقائق قررت أسأل شات جي بي تي عن التصرف الأمثل فنصحني فورًا باستخدام زيت البرافين" }, { "kind": "news report", "citation": "Al-Dostor (Asmaa Ibrahim), 24 May 2025, 'الذكاء الاصطناعي السبب. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة'.", "title": "الذكاء الاصطناعي السبب.. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة", "url": "https://www.dostor.org/5081951", "date": "2025-05-24", "written_by": "independent", "quote": "كما أكد أن الشاب نفى تناوله للقرص وأقر بأنه ألقاه في حوض ماء" }, { "kind": "news report", "citation": "Masrawy (Mahmoud Agami, Assiut), 25 May 2025, 'عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"'.", "title": "عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"", "url": "https://www.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "date": "2025-05-25", "written_by": "independent", "quote": "لم تظهر عليه أي أعراض سريرية تشير إلى تعرضه لتسمم بمادة الفوسفين الناتجة عن قرص الغلة" }, { "kind": "news report", "citation": "Al-Dostor, 25 May 2025, 'دراما إنقاذ \"قرص الغلة\" عبر الذكاء الاصطناعي. وخبراء يحذرون من كارثة محتملة' (quotes Dr Mahmoud Amr, founder of the National Poison Centre).", "title": "دراما إنقاذ “قرص الغلة” عبر الذكاء الاصطناعي.. وخبراء يحذرون من كارثة محتملة", "url": "https://www.dostor.org/5083066", "date": "2025-05-25", "written_by": "independent", "quote": "تناول الزيوت بالفعل يُعد من الإسعافات الأولية الضرورية في حالات التسمم بحبوب الغلة" }, { "kind": "news report", "citation": "Al Jazeera Mubasher (Sameh Gad), 26 May 2025, '\"قرص الغلة\" والمنشور الذي حيّر المصريين. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من \"الترند القاتل\"' (quotes Dr Nermin Hamdi and Dr Mahmoud Mohamed Amr).", "title": "“قرص الغلة” والمنشور الذي حيّر المصريين.. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من “الترند القاتل”", "url": "https://www.aljazeeramubasher.net/news/reports/2025/5/26/%d9%82%d8%b1%d8%b5-%d8%a7%d9%84%d8%ba%d9%84%d8%a9-%d9%88%d8%a7%d9%84%d9%85%d9%86%d8%b4%d9%88%d8%b1-%d8%a7%d9%84%d8%b0%d9%8a-%d8%ad%d9%8a%d8%b1", "date": "2025-05-26", "written_by": "independent", "quote": "إعطاء المصاب بالتسمم أي نوع من الزيوت هو بمثابة إسعاف أولي وليس علاجا" } ], "replication": { "produced_by": [ "A layperson prompting ChatGPT for first aid during a suspected aluminum phosphide ingestion", "Paraffin oil bought from a pharmacy and given before and, by the cousin's account, during hospital care" ], "must_hold": [ "That the pill was swallowed at all, which the treating dean says the patient denied", "Clinical confirmation of phosphine poisoning and of any effect of the oil. Assiut's paraffin-oil-plus-acetylcysteine protocol is described by the dean as experimental and not officially adopted." ], "echoes": [] }, "paired_incident": null, "headline": "ChatGPT's first-aid advice may have saved a teen in Assiut", "metric": null, "comparator": null, "later": { "status": "reversed", "date": "2025-05-25", "note": "The next day the dean of Assiut University's Faculty of Medicine told Masrawy that the youth showed no clinical signs of phosphine poisoning and had categorically denied swallowing the pill, and on 26 May a toxicologist told Al Jazeera Mubasher that the claim that paraffin oil rescues such a case has no scientific basis.", "quote": "فى المقابل، أوضح عميد كلية طب أسيوط، لـ\"مصراوي\" أن الشاب، البالغ من العمر نحو 17 عامًا، لم تظهر عليه أي أعراض سريرية تشير إلى تعرضه لتسمم بمادة الفوسفين الناتجة عن قرص الغلة، وهي أعراض معروفة وواضحة في مثل هذه الحالات. وأضاف أن الفريق الطبي تابع الحالة بدقة، ووجّه للشاب عدة استفسارات حول تناوله للقرص، إلا أنه نفى ذلك بشكل قاطع، مؤكدًا أنه ألقى القرص في حوض ماء ولم يبتلعه.", "url": "https://www.masrawy.com/news/news_regions/details/2025/5/25/2793185/" }, "value": null, "developer_country": "United States", "deployer_country": null, "unrated_because": "the dean denies that there was a poisoning", "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 dean denies that there was a poisoning.", "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.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "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": "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, 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: five 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-0325", "catalog_version": "1.0.0", "source_fingerprint": "c89c1581d594839bae821d714dc3afa69acacd79a4a7a7f16669b13a0928a51a", "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" } ], "source_ids": [ "AIBD-0325-source-0001", "AIBD-0325-source-0002", "AIBD-0325-source-0003", "AIBD-0325-source-0004", "AIBD-0325-source-0005", "AIBD-0325-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": "OpenAI", "deployer": "Islam Adel, the patient's cousin, as a consumer user" } }, "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": "ChatGPT's output (advice to give paraffin oil, then nine bottles in turn with breathing exercises) is documented only by the cousin's own post. The chat transcripts were not published (“too many to publish all”, per Masrawy's summary of the post). Dean Alaa Attia contradicts its effect: he says that the patient had no clinical signs of phosphine poisoning and denied swallowing the pill. Dr Nermin Hamdi's report that Assiut University denied the case involved the grain pill also contradicts it. Dr Mahmoud Amr says that oil is a recognized first aid but not a cure. Whether the hospital followed any of the AI's instructions is unsupported by any source other than the post." } } }, "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": "ChatGPT's output (advice to give paraffin oil, then nine bottles in turn with breathing exercises) is documented only by the cousin's own post. The chat transcripts were not published (“too many to publish all”, per Masrawy's summary of the post). Dean Alaa Attia contradicts its effect: he says that the patient had no clinical signs of phosphine poisoning and denied swallowing the pill. Dr Nermin Hamdi's report that Assiut University denied the case involved the grain pill also contradicts it. Dr Mahmoud Amr says that oil is a recognized first aid but not a cure. Whether the hospital followed any of the AI's instructions is unsupported by any source other than the post." } } }, "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: five 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", "reason": "The survival claim rests on the cousin's post. The dean says that there was no poisoning to survive. No outcome attributable to the advice is therefore documented." }, { "group": "societal", "reason": "Al Jazeera Mubasher and Dostor report experts warning that the “trend” could kill. No wider effect 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": "The survival claim rests on the cousin's post. The dean says that there was no poisoning to survive. No outcome attributable to the advice is therefore documented." }, { "group": "societal", "reason": "Al Jazeera Mubasher and Dostor report experts warning that the “trend” could kill. No wider effect 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": 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"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": 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"review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2025-05-24-415b" } } }, "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": "2025-05-25", "note": "The next day the dean of Assiut University's Faculty of Medicine told Masrawy that the youth showed no clinical signs of phosphine poisoning and had categorically denied swallowing the pill, and on 26 May a toxicologist told Al Jazeera Mubasher that the claim that paraffin oil rescues such a case has no scientific basis.", "quote": "فى المقابل، أوضح عميد كلية طب أسيوط، لـ\"مصراوي\" أن الشاب، البالغ من العمر نحو 17 عامًا، لم تظهر عليه أي أعراض سريرية تشير إلى تعرضه لتسمم بمادة الفوسفين الناتجة عن قرص الغلة، وهي أعراض معروفة وواضحة في مثل هذه الحالات. وأضاف أن الفريق الطبي تابع الحالة بدقة، ووجّه للشاب عدة استفسارات حول تناوله للقرص، إلا أنه نفى ذلك بشكل قاطع، مؤكدًا أنه ألقى القرص في حوض ماء ولم يبتلعه.", "url": "https://www.masrawy.com/news/news_regions/details/2025/5/25/2793185/" } } }, "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-0325" } }, "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" } }, "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": "El Balad (24 May 2025, 12:39) reproduces a Facebook post by Islam Adel, a commercial-control inspector. He wrote it at 1 a.m. after “24 hours” had passed since his cousin Mohamed Yousry (17) swallowed part of a “grain pill” (aluminum phosphide). The pill came from samples Adel had seized and kept at the family's old house. The cousin took it after a quarrel with his father. Adel writes that the Abnoub central hospital refused the case. He writes that within minutes he asked ChatGPT for the best course. It advised paraffin oil to coat the pill and limit phosphine release. The youth drank a bottle from a pharmacy before they went to Assiut University Hospital's poison reception. Doctors there said there was no treatment protocol and the case would die. He then gave instructions from “an advanced AI model and a limited-use trial version of GPT-5”. Nine bottles of paraffin oil were given in turn with breathing exercises. The cousin stayed conscious, vomited for hours and was discharged stable. Dostor (24 May 2025, 22:40) reports the post and the reply of Dr Alaa Attia, dean of the Faculty of Medicine at Assiut University. He said precise medical examinations showed no signs or symptoms of swallowing the pill. The youth denied taking it and admitted throwing it into a water basin. The dean added that the hospital is researching paraffin oil with acetylcysteine as an adjunct. The protocol is experimental and not officially adopted. Masrawy (25 May 2025) quotes the dean that the youth, about 17, showed no clinical symptoms of phosphine poisoning. The team questioned him and he categorically denied swallowing the pill. He cited a family dispute over travel to Italy. Dostor (25 May 2025) adds toxicologist Dr Mahmoud Amr, founder of the National Poison Center. He says oils are indeed a necessary first aid in grain-pill poisoning. Al Jazeera Mubasher (26 May 2025) quotes Amr that the claim that paraffin oil rescues such a case “has no scientific basis”. It quotes Cairo University toxicologist Dr Nermin Hamdi that Assiut University denied the case involved the grain pill. Hamdi adds that giving oil is first aid, not treatment. The Monitor lists 13 syndications. Al Arabiya's syndication was not available. The ingestion date is not fixed by any source.", "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", "reason": "The survival claim rests on the cousin's post. The dean says that there was no poisoning to survive. No outcome attributable to the advice is therefore documented." }, { "group": "societal", "reason": "Al Jazeera Mubasher and Dostor report experts warning that the “trend” could kill. No wider effect 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": true, "purpose": "ChatGPT is a general-purpose assistant. The cousin used it for emergency first-aid advice and, by his account, for instructions during hospital care. The claimed benefit (a survival) is outside any stated purpose of the system." } }, "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": "Abnoub and Assiut University Hospital, Assiut Governorate, Egypt", "developer_country": "United States" } }, "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": "ChatGPT is a general-purpose assistant. The cousin used it for emergency first-aid advice and, by his account, for instructions during hospital care. The claimed benefit (a survival) is outside any stated purpose of the system." } }, "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": "2025-05-24", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Left not rated: the dean denies that there was a poisoning.", "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.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "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": "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, 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 layperson prompting ChatGPT for first aid during a suspected aluminum phosphide ingestion", "Paraffin oil bought from a pharmacy and given before and, by the cousin's account, during hospital care" ], "must_hold": [ "That the pill was swallowed at all, which the treating dean says the patient denied", "Clinical confirmation of phosphine poisoning and of any effect of the oil. Assiut's paraffin-oil-plus-acetylcysteine protocol is described by the dean as experimental and not officially adopted." ], "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: the dean denies that there was a poisoning.", "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.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "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": "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, 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": "Healthcare: emergency toxicology", "segment": "Consumer chatbot used for first-aid advice" } }, "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 2025-05-24-415b, 'AI Intervention Saves Teen from Poisoning' (AI-generated summary).", "title": "AI Intervention Saves Teen from Poisoning", "url": "https://oecd.ai/en/incidents/2025-05-24-415b", "date": "2025-05-24", "written_by": "independent", "quote": "The AI recommended immediately coating the pill with paraffin oil to mitigate poisoning effects, helping counteract doctors’ initial pessimism and contributing to the teen’s survival." }, { "kind": "news report", "citation": "Sada El Balad (Sara Abdallah), 24 May 2025, 'عاد من الموت. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة' (reproduces Islam Adel's Facebook post).", "title": "عاد من الموت.. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة", "url": "https://www.elbalad.news/6583785", "date": "2025-05-24", "written_by": "independent", "quote": "خلال دقائق قررت أسأل شات جي بي تي عن التصرف الأمثل فنصحني فورًا باستخدام زيت البرافين" }, { "kind": "news report", "citation": "Al-Dostor (Asmaa Ibrahim), 24 May 2025, 'الذكاء الاصطناعي السبب. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة'.", "title": "الذكاء الاصطناعي السبب.. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة", "url": "https://www.dostor.org/5081951", "date": "2025-05-24", "written_by": "independent", "quote": "كما أكد أن الشاب نفى تناوله للقرص وأقر بأنه ألقاه في حوض ماء" }, { "kind": "news report", "citation": "Masrawy (Mahmoud Agami, Assiut), 25 May 2025, 'عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"'.", "title": "عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"", "url": "https://www.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "date": "2025-05-25", "written_by": "independent", "quote": "لم تظهر عليه أي أعراض سريرية تشير إلى تعرضه لتسمم بمادة الفوسفين الناتجة عن قرص الغلة" }, { "kind": "news report", "citation": "Al-Dostor, 25 May 2025, 'دراما إنقاذ \"قرص الغلة\" عبر الذكاء الاصطناعي. وخبراء يحذرون من كارثة محتملة' (quotes Dr Mahmoud Amr, founder of the National Poison Centre).", "title": "دراما إنقاذ “قرص الغلة” عبر الذكاء الاصطناعي.. وخبراء يحذرون من كارثة محتملة", "url": "https://www.dostor.org/5083066", "date": "2025-05-25", "written_by": "independent", "quote": "تناول الزيوت بالفعل يُعد من الإسعافات الأولية الضرورية في حالات التسمم بحبوب الغلة" }, { "kind": "news report", "citation": "Al Jazeera Mubasher (Sameh Gad), 26 May 2025, '\"قرص الغلة\" والمنشور الذي حيّر المصريين. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من \"الترند القاتل\"' (quotes Dr Nermin Hamdi and Dr Mahmoud Mohamed Amr).", "title": "“قرص الغلة” والمنشور الذي حيّر المصريين.. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من “الترند القاتل”", "url": "https://www.aljazeeramubasher.net/news/reports/2025/5/26/%d9%82%d8%b1%d8%b5-%d8%a7%d9%84%d8%ba%d9%84%d8%a9-%d9%88%d8%a7%d9%84%d9%85%d9%86%d8%b4%d9%88%d8%b1-%d8%a7%d9%84%d8%b0%d9%8a-%d8%ad%d9%8a%d8%b1", "date": "2025-05-26", "written_by": "independent", "quote": "إعطاء المصاب بالتسمم أي نوع من الزيوت هو بمثابة إسعاف أولي وليس علاجا" } ] } }, "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-0325-source-0001", "AIBD-0325-source-0002", "AIBD-0325-source-0003", "AIBD-0325-source-0004", "AIBD-0325-source-0005", "AIBD-0325-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": "registry label", "citation": "OECD AI Incidents and Hazards Monitor, record 2025-05-24-415b, 'AI Intervention Saves Teen from Poisoning' (AI-generated summary).", "title": "AI Intervention Saves Teen from Poisoning", "url": "https://oecd.ai/en/incidents/2025-05-24-415b", "date": "2025-05-24", "written_by": "independent", "quote": "The AI recommended immediately coating the pill with paraffin oil to mitigate poisoning effects, helping counteract doctors’ initial pessimism and contributing to the teen’s survival." }, { "kind": "news report", "citation": "Sada El Balad (Sara Abdallah), 24 May 2025, 'عاد من الموت. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة' (reproduces Islam Adel's Facebook post).", "title": "عاد من الموت.. شات جي بي تي ينقذ شابا بعد تناول حبة الغلة", "url": "https://www.elbalad.news/6583785", "date": "2025-05-24", "written_by": "independent", "quote": "خلال دقائق قررت أسأل شات جي بي تي عن التصرف الأمثل فنصحني فورًا باستخدام زيت البرافين" }, { "kind": "news report", "citation": "Al-Dostor (Asmaa Ibrahim), 24 May 2025, 'الذكاء الاصطناعي السبب. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة'.", "title": "الذكاء الاصطناعي السبب.. شاب ينجو من تسمم قرص الغلة وعميد طب أسيوط ينفي الحادثة", "url": "https://www.dostor.org/5081951", "date": "2025-05-24", "written_by": "independent", "quote": "كما أكد أن الشاب نفى تناوله للقرص وأقر بأنه ألقاه في حوض ماء" }, { "kind": "news report", "citation": "Masrawy (Mahmoud Agami, Assiut), 25 May 2025, 'عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"'.", "title": "عميد طب أسيوط يكشف حقيقة منشور \"شات جي بي تي وحبة الغلال\"", "url": "https://www.masrawy.com/news/news_regions/details/2025/5/25/2793185/", "date": "2025-05-25", "written_by": "independent", "quote": "لم تظهر عليه أي أعراض سريرية تشير إلى تعرضه لتسمم بمادة الفوسفين الناتجة عن قرص الغلة" }, { "kind": "news report", "citation": "Al-Dostor, 25 May 2025, 'دراما إنقاذ \"قرص الغلة\" عبر الذكاء الاصطناعي. وخبراء يحذرون من كارثة محتملة' (quotes Dr Mahmoud Amr, founder of the National Poison Centre).", "title": "دراما إنقاذ “قرص الغلة” عبر الذكاء الاصطناعي.. وخبراء يحذرون من كارثة محتملة", "url": "https://www.dostor.org/5083066", "date": "2025-05-25", "written_by": "independent", "quote": "تناول الزيوت بالفعل يُعد من الإسعافات الأولية الضرورية في حالات التسمم بحبوب الغلة" }, { "kind": "news report", "citation": "Al Jazeera Mubasher (Sameh Gad), 26 May 2025, '\"قرص الغلة\" والمنشور الذي حيّر المصريين. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من \"الترند القاتل\"' (quotes Dr Nermin Hamdi and Dr Mahmoud Mohamed Amr).", "title": "“قرص الغلة” والمنشور الذي حيّر المصريين.. وصفة غريبة لإنقاذ المنتحرين وتحذيرات من “الترند القاتل”", "url": "https://www.aljazeeramubasher.net/news/reports/2025/5/26/%d9%82%d8%b1%d8%b5-%d8%a7%d9%84%d8%ba%d9%84%d8%a9-%d9%88%d8%a7%d9%84%d9%85%d9%86%d8%b4%d9%88%d8%b1-%d8%a7%d9%84%d8%b0%d9%8a-%d8%ad%d9%8a%d8%b1", "date": "2025-05-26", "written_by": "independent", "quote": "إعطاء المصاب بالتسمم أي نوع من الزيوت هو بمثابة إسعاف أولي وليس علاجا" } ] } }, "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 Facebook post of 24 May 2025 credits ChatGPT with first aid. Assiut Faculty of Medicine dean Alaa Attia says that the youth had no phosphine symptoms and said he threw the pill into water.", "account": "El Balad (24 May 2025, 12:39) reproduces a Facebook post by Islam Adel, a commercial-control inspector. He wrote it at 1 a.m. after “24 hours” had passed since his cousin Mohamed Yousry (17) swallowed part of a “grain pill” (aluminum phosphide). The pill came from samples Adel had seized and kept at the family's old house. The cousin took it after a quarrel with his father. Adel writes that the Abnoub central hospital refused the case. He writes that within minutes he asked ChatGPT for the best course. It advised paraffin oil to coat the pill and limit phosphine release. The youth drank a bottle from a pharmacy before they went to Assiut University Hospital's poison reception. Doctors there said there was no treatment protocol and the case would die. He then gave instructions from “an advanced AI model and a limited-use trial version of GPT-5”. Nine bottles of paraffin oil were given in turn with breathing exercises. The cousin stayed conscious, vomited for hours and was discharged stable. Dostor (24 May 2025, 22:40) reports the post and the reply of Dr Alaa Attia, dean of the Faculty of Medicine at Assiut University. He said precise medical examinations showed no signs or symptoms of swallowing the pill. The youth denied taking it and admitted throwing it into a water basin. The dean added that the hospital is researching paraffin oil with acetylcysteine as an adjunct. The protocol is experimental and not officially adopted. Masrawy (25 May 2025) quotes the dean that the youth, about 17, showed no clinical symptoms of phosphine poisoning. The team questioned him and he categorically denied swallowing the pill. He cited a family dispute over travel to Italy. Dostor (25 May 2025) adds toxicologist Dr Mahmoud Amr, founder of the National Poison Center. He says oils are indeed a necessary first aid in grain-pill poisoning. Al Jazeera Mubasher (26 May 2025) quotes Amr that the claim that paraffin oil rescues such a case “has no scientific basis”. It quotes Cairo University toxicologist Dr Nermin Hamdi that Assiut University denied the case involved the grain pill. Hamdi adds that giving oil is first aid, not treatment. The Monitor lists 13 syndications. Al Arabiya's syndication was not available. The ingestion date is not fixed by any source." } }, "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 chatbot supplied treatment suggestions, according to the patient’s cousin." }, "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": "A trial version of GPT-5, according to the cousin’s post." }, "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": "A trial version of GPT-5, according to the cousin’s post.", "editor_note": "The identification comes from the cousin’s post. The existing sources provide no model output or vendor confirmation." } }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "GPT-5 trial version, per the cousin's post; unverified" } }, "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": "ChatGPT (OpenAI), described in the post as 'an advanced AI model and a limited-use trial version of GPT-5'" } }, "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's paraffin-oil advice may have saved Islam Adel's 17-year-old cousin after an aluminum phosphide “grain pill” ingestion in Assiut", "headline": "ChatGPT's first-aid advice may have saved a teen in Assiut" } }, "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": [ "c89c1581d594839bae821d714dc3afa69acacd79a4a7a7f16669b13a0928a51a" ], "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": "El Balad (24 May 2025, 12:39) reproduces a Facebook post by Islam Adel, a commercial-control inspector. He wrote it at 1 a.m. after “24 hours” had passed since his cousin Mohamed Yousry (17) swallowed part of a “grain pill” (aluminum phosphide). The pill came from samples Adel had seized and kept at the family's old house. The cousin took it after a quarrel with his father. Adel writes that the Abnoub central hospital refused the case. He writes that within minutes he asked ChatGPT for the best course. It advised paraffin oil to coat the pill and limit phosphine release. The youth drank a bottle from a pharmacy before they went to Assiut University Hospital's poison reception. Doctors there said there was no treatment protocol and the case would die. He then gave instructions from “an advanced AI model and a limited-use trial version of GPT-5”. Nine bottles of paraffin oil were given in turn with breathing exercises. The cousin stayed conscious, vomited for hours and was discharged stable. Dostor (24 May 2025, 22:40) reports the post and the reply of Dr Alaa Attia, dean of the Faculty of Medicine at Assiut University. He said precise medical examinations showed no signs or symptoms of swallowing the pill. The youth denied taking it and admitted throwing it into a water basin. The dean added that the hospital is researching paraffin oil with acetylcysteine as an adjunct. The protocol is experimental and not officially adopted. Masrawy (25 May 2025) quotes the dean that the youth, about 17, showed no clinical symptoms of phosphine poisoning. The team questioned him and he categorically denied swallowing the pill. He cited a family dispute over travel to Italy. Dostor (25 May 2025) adds toxicologist Dr Mahmoud Amr, founder of the National Poison Center. He says oils are indeed a necessary first aid in grain-pill poisoning. Al Jazeera Mubasher (26 May 2025) quotes Amr that the claim that paraffin oil rescues such a case “has no scientific basis”. It quotes Cairo University toxicologist Dr Nermin Hamdi that Assiut University denied the case involved the grain pill. Hamdi adds that giving oil is first aid, not treatment. The Monitor lists 13 syndications. Al Arabiya's syndication was not available. The ingestion date is not fixed by any source.", "replication": { "produced_by": [ "A layperson prompting ChatGPT for first aid during a suspected aluminum phosphide ingestion", "Paraffin oil bought from a pharmacy and given before and, by the cousin's account, during hospital care" ], "must_hold": [ "That the pill was swallowed at all, which the treating dean says the patient denied", "Clinical confirmation of phosphine poisoning and of any effect of the oil. Assiut's paraffin-oil-plus-acetylcysteine protocol is described by the dean as experimental and not officially adopted." ], "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-0326", "case_url": "records/AIBD-0326.html", "reason": "Specific harm prevented.", "details": [ { "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": { "category": "Specific harm prevented", "comparison": "undocumented" }, "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-0371", "case_url": "records/AIBD-0371.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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", "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, 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.", "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": "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." } ] } }, "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": "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." } ] } }, "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": 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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. 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The page does not link a case paper, scan report, alert log or device specification." }, { "claim": "Background timing and neuron figures", "path": [ "https://mayomagazine.mayoclinic.org/2025/01/ai-in-stroke-care/", "42 Million Neurons Saved With AI Intervention infographic" ], "finding": "The 22-minute and 42-million-neuron figures concern an illustrative ischemic-stroke pathway. They are not patient-specific measurements and are not imported into this case." } ], "limitations": [ "The AI product and model version are unnamed.", "The contribution of AI to survival is not isolated from proximity to the hospital and clinical treatment.", "The DISCOVERY study mentioned in follow-up concerns later cognitive outcomes, not validation of the original AI-assisted rescue." ], "excluded_or_consolidated_sources": [], "review_scope": "Traced the claims in this case through accessible original publications and relevant embedded references, tables, appendices, corrections, and supplements. The stated access limits remain unresolved." } }, "provenance": { "kind": "source_review", "basis": "Read the available originals and relevant source chains, with unresolved access and evidentiary limits recorded explicitly.", "record_fields": [ "evidence" ], "source_refs": [ "AIBD-0581-source-0001" ], "reviewed_on": "2026-10-24" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Mayo Clinic’s magazine interviews Chan, her husband, and her care team. It links no independent clinical report of the episode.", "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": "Sophia Chan survived a ruptured aneurysm after AI-assisted diagnosis and emergency treatment at Mayo Clinic.", "account": "In February 2022, Sophia Chan became unresponsive after an aneurysm ruptured. At Mayo Clinic in Jacksonville, AI helped clinicians locate it within minutes. She underwent surgery, although her husband was initially told that she was unlikely to survive. After weeks of stabilization, she recovered. Mayo reports that she had resumed an active family life two years later." } }, "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 analysis of brain CT images" } }, "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-assisted aneurysm detection supports Sophia Chan's emergency treatment and recovery at Mayo Clinic", "headline": "AI helps locate Sophia Chan's aneurysm before emergency treatment" } }, "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": [ "1350009d81d5e22362617e6836f10066be92ecee61e8f6f5f2d5167587e9ad40" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Updated the fingerprint after this targeted 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": "In February 2022, Sophia Chan became unresponsive after an aneurysm ruptured. At Mayo Clinic in Jacksonville, AI helped clinicians locate it within minutes. She underwent surgery, although her husband was initially told that she was unlikely to survive. After weeks of stabilization, she recovered. Mayo reports that she had resumed an active family life two years later.", "replication": { "produced_by": [ "AI-assisted imaging, surgery, and intensive care." ], "must_hold": [ "Timely imaging, clinical review, and access to emergency treatment." ], "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": 6, "record_mapped": 24, "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-0187", "case_url": "records/AIBD-0187.html", "reason": "Specific harm prevented.", "details": [ { "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." }, { "label": "Comparison", "value": "the amount Medicare was scheduled to pay on the fraudulent claims had the billing gone undetected", "url": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146" }, { "label": "Before", "value": "the approximately $4.45 billion that was scheduled to be paid by Medicare" }, { "label": "After", "value": "all but approximately $41 million" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "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. It does not establish that AI was used in the Gold Rush detection.", "category": "harm prevented", "unexpected": false, "purpose": "Detect, investigate and prosecute complex health care fraud schemes.", "system": "DOJ Health Care Fraud Unit proactive billing analytics", "model_version": null, "developer": "U.S. Department of Justice, Criminal Division Fraud Section, Health Care Fraud Unit Data Analytics Team", "deployer": "U.S. Department of Justice with HHS-OIG and CMS", "location": null, "domain": "Health care fraud and public insurance", "segment": "Medicare durable medical equipment claims", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "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", "depth": null, "reason": "The release describes over one million stolen identities but documents no outcome for the individual beneficiaries." } ], "ai_role": { "status": "unassessed", "statement": "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." }, "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", "quote": null }, { "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", "quote": null }, { "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", "quote": null } ], "replication": { "produced_by": [ "Analysts detected anomalous Medicare billing.", "HHS-OIG and CMS suspended scheduled payments." ], "must_hold": [ "Access to billing data before payment.", "Legal authority and operational capacity to suspend payments." ], "echoes": [] }, "paired_incident": null, "headline": "Medicare withholds $4.41 billion in suspected fraudulent payments", "metric": "HHS-OIG and CMS prevented all but $41 million of $4.45 billion", "comparator": { "against": "the amount Medicare was scheduled to pay on the fraudulent claims had the billing gone undetected", "before": "the approximately $4.45 billion that was scheduled to be paid by Medicare", "after": "all but approximately $41 million", "basis": "measured", "quote": "The Health Care Fraud Unit's Data Analytics Team and its partners detected the anomalous billing through proactive data analytics, and HHS-OIG and CMS successfully prevented the organization from receiving all but approximately $41 million of the approximately $4.45 billion that was scheduled to be paid by Medicare.", "url": "https://www.justice.gov/opa/pr/national-health-care-fraud-takedown-results-324-defendants-charged-connection-over-146" }, "later": { "status": "litigated", "date": "2026-08-13", "note": "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.", "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-0114", "case_url": "records/AIBD-0114.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The platform analyzed a bacterial mass spectrum to predict resistance and suggest an alternative antibiotic. Clinicians selected and administered treatment. The hospital attributes earlier selection to the system, but the available account cannot isolate its effect on survival." }, { "label": "Comparison", "value": "The two to three days that hospital clinicians said conventional susceptibility testing required", "url": "https://health.ltn.com.tw/article/breakingnews/4102412" }, { "label": "Before", "value": "Two to three days" }, { "label": "After", "value": "A prediction within one hour" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Specific harm prevented", "comparison": "assumed" }, "record": { "id": "AIBD-0114", "title": "An early resistance warning helped doctors change a woman’s antibiotic", "summary": "An early resistance warning from CMUH’s Intelligent Anti-Microbial System helped doctors change a 78-year-old woman’s antibiotic before she recovered.", "date_observed": "2022-07-01", "date_basis": "event", "date_precision": "month", "benefit_status": "reported", "source_collection": null, "source_entry": null, "account": "A 78-year-old woman at China Medical University Hospital developed severe pneumonia during a prolonged admission. The hospital’s Intelligent Anti-Microbial System predicted within an hour that her Stenotrophomonas maltophilia isolate resisted ceftazidime and suggested levofloxacin. Clinicians changed the antibiotic. The hospital reported that she recovered and went home in late July 2022. The account concerns one patient. More than 100,000 platform uses and projected changes in hospital mortality concern the wider service, not 100,000 demonstrated recoveries.", "category": "harm prevented", "unexpected": false, "purpose": "Help clinicians select an effective antibiotic sooner by predicting resistance from routine mass spectra.", "system": "智抗菌平台 / Intelligent Anti-Microbial System (i.A.M.S)", "model_version": null, "developer": "China Medical University Hospital, Taichung (team of superintendent Der-Yang Cho 周德陽, Po-Ren Hsueh 薛博仁 of the Department of Laboratory Medicine, and Jiaxin Yu 游家鑫)", "deployer": "China Medical University Hospital, Taichung", "location": "Taichung, Taiwan", "domain": "Healthcare - clinical microbiology and antibiotic therapy", "segment": "Hospital laboratory AI (antibiotic-resistance prediction from MALDI-TOF spectra)", "benefit_type": "Improved Physical Health", "taxonomy_note": "The AI's part was turnaround time, a resistance prediction in one hour instead of days.", "registry_ids": { "oecd_aim": "2022-10-25-1879" }, "ratings": [ { "group": "personal", "depth": 4, "reason": "The treating hospital described timely treatment of a severe infection, followed by recovery and discharge. The account does not establish a counterfactual life saved." } ], "ai_role": { "status": "assessed", "statement": "The platform analyzed a bacterial mass spectrum to predict resistance and suggest an alternative antibiotic. Clinicians selected and administered treatment. The hospital attributes earlier selection to the system, but the available account cannot isolate its effect on survival." }, "evidence": [ { "kind": "press release", "citation": "China Medical University Hospital, “78歲婦人感染超級細菌昏迷 ! AI質譜儀1小時找出有效抗生素 《智抗菌》平臺累積十萬人以上使用 刊登頂尖醫學期刊《Journal of Infection》,” October 31, 2022. Original treating-hospital account, patient narrative and figure captions.", "title": "78歲婦人感染超級細菌昏迷 ! AI質譜儀1小時找出有效抗生素", "url": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8151", "date": "2022-10-31", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "中央社 (Central News Agency), 記者郝雪卿 (2022). 78歲婦人感染超級細菌命危 AI質譜儀輔助治療. CNA, Taichung, 26 October 2022, reporting the hospital's press conference and quoting superintendent 周德陽 and vice-superintendent 薛博仁.", "title": "78歲婦人感染超級細菌命危 AI質譜儀輔助治療", "url": "https://www.cna.com.tw/news/ahel/202210260324.aspx", "date": "2022-10-26", "written_by": "deployer", "quote": null }, { "kind": "news report", "citation": "自由健康網 (Liberty Times), 記者蔡淑媛 (2022). 7旬嬤感染超級細菌命危 AI預測有抗藥性趕緊換藥救命. Liberty Times, Taichung, 26 October 2022, quoting 周德陽 and 薛博仁.", "title": "7旬嬤感染超級細菌命危 AI預測有抗藥性趕緊換藥救命", "url": "https://health.ltn.com.tw/article/breakingnews/4102412", "date": "2022-10-26", "written_by": "deployer", "quote": null }, { "kind": "peer-reviewed study", "citation": "Yu J, Lin HH, Tseng KH, Tien N, Hsueh PR, Cho DY (2023). Direct prediction of ceftazidime-resistant Stenotrophomonas maltophilia from routine MALDI-TOF mass spectra using machine learning. Journal of Infection 86(2): e58-e60, letter, online 9 September 2022, doi:10.1016/j.jinf.2022.09.005. All authors at China Medical University Hospital; the letter has no abstract, so its keywords are quoted.", "title": "Direct prediction of ceftazidime-resistant Stenotrophomonas maltophilia from routine MALDI-TOF mass spectra using machine learning", "url": "https://doi.org/10.1016/j.jinf.2022.09.005", "date": "2022-09-09", "written_by": "makers", "quote": null }, { "kind": "report", "citation": "China Medical University Hospital, 2022 Corporate Sustainability Report, 37, “預測抗藥細菌，醫療數位轉型.” The report identifies i.A.M.S and compares inpatient drug spending with the corresponding period of 2021.", "title": "中國醫藥大學附設醫院 2022 永續報告書", "url": "https://www.cmuh.org.tw/FileUploads/FileUpload/2022%E4%B8%AD%E5%9C%8B%E9%86%ABCSR%28ESG%29_PDF.pdf", "date": null, "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "The hospital's own laboratory identifies isolates by MALDI-TOF, so a spectrum exists as soon as a culture grows", "A machine-learning classifier trained on the hospital's spectra and susceptibility records for Stenotrophomonas maltophilia and ceftazidime, one of nine species the platform covers", "The platform returns the resistance prediction and a suggested alternative to the treating team within an hour" ], "must_hold": [ "The organism and antibiotic are ones the model was trained for", "Clinicians act on the prediction before the conventional susceptibility result arrives", "An effective alternative antibiotic is available and tolerated by the patient" ], "echoes": [] }, "paired_incident": null, "headline": "An early resistance warning helped doctors change a woman’s antibiotic", "metric": "flagged ceftazidime resistance in 1 hour", "comparator": { "against": "The two to three days that hospital clinicians said conventional susceptibility testing required", "before": "Two to three days", "after": "A prediction within one hour", "basis": "assumed", "quote": "中國附醫驗醫學中心副院長薛博仁說，由於菌嗜麥芽窄食單胞菌抗藥性強，一般臨床檢驗抗藥性要2、3天，透過AI大數據分析細菌質譜儀圖譜，1小時能快速預測有抗藥性，準確率達92%，醫療團隊發現原先用藥有抗藥性，立刻換上無抗藥性的抗生素，1週後就肺部感染大幅好轉，之後順利拔管出院返家。", "url": "https://health.ltn.com.tw/article/breakingnews/4102412" }, "later": { "status": "persisted", "date": "2022-10-31", "note": "The hospital reported more than 100,000 platform uses since rollout. Its five-year mortality and medication-error figures were projections, not observed results.", "quote": null, "url": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8151" }, "value": null, "developer_country": "Taiwan", "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/2022-10-25-1879", "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://health.ltn.com.tw/article/breakingnews/4102412; https://www.top1health.com/Article/240/90334", "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/China_Medical_University_(Taiwan); https://en.wikipedia.org/wiki/China_Medical_University_(Taiwan)", "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": "2026-10-25", "change": "Reviewed the original evidence and supporting material; updated system details, methods, outcomes, limitations, costs, and source lineage.", "source": "https://www.cmuh.cmu.edu.tw/NewsInfo/NewsArticle?no=8151", "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the deployer's report, two news reports from the deployer and the deployer's press release. 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The source does not quantify this woman’s financial benefit." } }, "claims[].proposition": { "state": "value_present", "value": { "kind": "text", "text": "The platform predicted antibiotic resistance before clinicians changed treatment, after which the patient recovered." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The platform predicted antibiotic resistance before clinicians changed treatment, after which the patient recovered." } }, "cross_border_flows[]": { "state": "not_reported", "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "reason": "The reviewed sources do not document international transfers of patient data or quantify cross-border financial flows.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reviewed sources do not document international transfers of patient data or quantify cross-border financial flows." } }, "decisions[].action_outcome": { "state": "value_present", "value": { "kind": "text", "text": "The hospital introduced the platform in 2022 and reported more than 100,000 uses by October. 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Its publications differ on whether rollout began in January or February." } }, "decisions[].grounds": { "state": "value_present", "value": { "kind": "text", "text": "Hospital leaders described delayed susceptibility results and the consequences of inappropriate treatment." }, "provenance": { "kind": "source_review", "basis": "Hospital rollout and projection paragraphs; 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Hospital leaders described delayed susceptibility results and the consequences of inappropriate treatment." } }, "decisions[].institution_task": { "state": "value_present", "value": { "kind": "text", "text": "China Medical University Hospital sought faster resistance predictions and earlier selection of antibiotics." }, "provenance": { "kind": "source_review", "basis": "Hospital rollout and projection paragraphs; 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "China Medical University Hospital sought faster resistance predictions and earlier selection of antibiotics." } }, "decisions[].success_criterion": { "state": "value_present", "value": { "kind": "text", "text": "The hospital described faster laboratory results and reduced drug spending. Its five-year mortality and medication-error figures were forecasts." }, "provenance": { "kind": "source_review", "basis": "Hospital rollout and projection paragraphs; 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital described faster laboratory results and reduced drug spending. Its five-year mortality and medication-error figures were forecasts." } }, "deployment_stage": { "state": "value_present", "value": { "kind": "text", "text": "Clinical use at China Medical University Hospital in 2022." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Clinical use at China Medical University Hospital in 2022." } }, "economics[].amount": { "state": "value_present", "value": { "kind": "structured", "data": { "hospital_report": { "inpatient_drug_spending_reduction_percent": [ 30, 40 ], "comparison": "Corresponding period of 2021" }, "press_quote": { "annual_antibiotic_spending_reduction_raw": "千萬", "numeric_reading": 10000000, "currency_explicitly_stated": false, "realization_uncertain": true }, "scope": "Entire platform, not this patient", "fructus_status_recommendation": "context or review queue pending monetary base, currency, and attribution review" } }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital reports 30–40% lower inpatient drug spending. A news report quotes annual savings of “10 million,” without stating a currency or calculation." } }, "economics[].costs[]": { "state": "value_present", "value": { "kind": "text", "text": "Implementation and operating costs are not reported alongside the savings." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Implementation and operating costs are not reported alongside the savings." } }, "economics[].counterfactual": { "state": "value_present", "value": { "kind": "text", "text": "The hospital report uses the corresponding period of 2021. It does not describe adjustment for changes in patients or treatment." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital report uses the corresponding period of 2021. It does not describe adjustment for changes in patients or treatment." } }, "economics[].currency_price_basis": { "state": "unresolved", "provenance": { "kind": "source_review", "basis": "Liberty Times, final paragraph; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "reason": "The annual figure does not state its currency or price year. The hospital report gives only percentages.", "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The annual figure does not state its currency or price year. The hospital report gives only percentages.", "editor_note": "New Taiwan dollars are a plausible reading because the report concerns a Taiwanese hospital’s domestic drug spending. The cited sentence does not name the currency, so no conversion has been applied." } }, "economics[].gross_net": { "state": "value_present", "value": { "kind": "text", "text": "The figures concern drug spending. They do not subtract development, equipment, staffing, or operating costs." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The figures concern drug spending. They do not subtract development, equipment, staffing, or operating costs." } }, "economics[].measure_type": { "state": "value_present", "value": { "kind": "text", "text": "Reported reductions in drug spending across the hospital’s platform." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Reported reductions in drug spending across the hospital’s platform." } }, "economics[].period_unit": { "state": "value_present", "value": { "kind": "text", "text": "The hospital compared the period after rollout in 2022 with the corresponding period in 2021. A separate news account gives an annual figure." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital compared the period after rollout in 2022 with the corresponding period in 2021. A separate news account gives an annual figure." } }, "economics[].realization": { "state": "value_present", "value": { "kind": "text", "text": "The sustainability report presents the spending reduction as observed. The annual figure’s calculation and period of observation are unclear." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The sustainability report presents the spending reduction as observed. The annual figure’s calculation and period of observation are unclear." } }, "economics[].recipient_payer": { "state": "value_present", "value": { "kind": "text", "text": "The hospital describes lower inpatient drug spending. It does not allocate the saving between the insurer, hospital, and patients." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital describes lower inpatient drug spending. It does not allocate the saving between the insurer, hospital, and patients." } }, "economics[].return_payback": { "state": "value_present", "value": { "kind": "text", "text": "The sources do not calculate net return or a payback period." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The sources do not calculate net return or a payback period." } }, "economics[].valuation_method": { "state": "value_present", "value": { "kind": "text", "text": "The report states a percentage reduction without publishing the underlying spending amounts, denominators, or calculation." }, "provenance": { "kind": "source_review", "basis": "CMUH 2022 sustainability report, p. 37; Liberty Times, final platform paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0003", "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The report states a percentage reduction without publishing the underlying spending amounts, denominators, or calculation." } }, "evaluations[].ai_uplift": { "state": "value_present", "value": { "kind": "text", "text": "The evidence supports the system’s role in selecting treatment earlier. It does not establish what would have happened without that warning." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The evidence supports the system’s role in selecting treatment earlier. It does not establish what would have happened without that warning." } }, "evaluations[].comparator": { "state": "value_present", "value": { "kind": "text", "text": "The hospital’s stated turnaround for conventional susceptibility testing. There was no controlled comparison of this patient’s recovery." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital’s stated turnaround for conventional susceptibility testing. There was no controlled comparison of this patient’s recovery." } }, "evaluations[].design": { "state": "value_present", "value": { "kind": "text", "text": "A single-patient clinical account without a control. The hospital also reported separate, aggregate observations about its platform." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "A single-patient clinical account without a control. The hospital also reported separate, aggregate observations about its platform." } }, "evaluations[].effect": { "state": "value_present", "value": { "kind": "text", "text": "The resistance prediction was available within an hour. Clinicians changed the antibiotic, and the patient later recovered and was discharged." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The resistance prediction was available within an hour. Clinicians changed the antibiotic, and the patient later recovered and was discharged." } }, "evaluations[].exposure": { "state": "value_present", "value": { "kind": "text", "text": "The admission began in April 2022, pneumonia developed in late May, and discharge occurred in late July. The exact date of the AI warning is not established." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The admission began in April 2022, pneumonia developed in late May, and discharge occurred in late July. The exact date of the AI warning is not established." } }, "evaluations[].id": { "state": "value_present", "value": { "kind": "text", "text": "CMUH clinical account, NewsArticle 8151" }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "CMUH clinical account, NewsArticle 8151" } }, "evaluations[].measurement_method": { "state": "value_present", "value": { "kind": "text", "text": "The hospital described the laboratory warning, antibiotic change, improvement, extubation, and discharge. Public reports do not include the full medical record." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital described the laboratory warning, antibiotic change, improvement, extubation, and discharge. Public reports do not include the full medical record." } }, "evaluations[].metric": { "state": "value_present", "value": { "kind": "text", "text": "Time to a resistance prediction and the patient’s subsequent clinical recovery." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Time to a resistance prediction and the patient’s subsequent clinical recovery." } }, "evaluations[].reproduction_materials": { "state": "value_present", "value": { "kind": "text", "text": "The hospital reproduces a figure from its resistance-model letter. The reviewed public materials do not provide model code, weights, or patient-level data." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital reproduces a figure from its resistance-model letter. The reviewed public materials do not provide model code, weights, or patient-level data." } }, "evaluations[].setting_sample": { "state": "value_present", "value": { "kind": "text", "text": "One patient at China Medical University Hospital. The hospital separately reported more than 100,000 uses of its platform." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "One patient at China Medical University Hospital. The hospital separately reported more than 100,000 uses of its platform." } }, "evaluations[].uncertainty_limitations": { "state": "value_present", "value": { "kind": "text", "text": "The clinical evidence comes from the treating hospital. News reports repeat its account. The original resistance-model letter could be identified but not read in full." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The clinical evidence comes from the treating hospital. News reports repeat its account. The original resistance-model letter could be identified but not read in full." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "registry_ids": { "oecd_aim": "2022-10-25-1879" } } }, "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": "text", "text": "The hospital’s 2022 report describes lower inpatient drug spending across the platform. It provides no later follow-up on this woman." }, "provenance": { "kind": "source_review", "basis": "2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0005" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital’s 2022 report describes lower inpatient drug spending across the platform. It provides no later follow-up on this woman." } }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Laboratory staff prepared and identified the isolate. Physicians interpreted the warning and made the treatment decision." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Laboratory staff prepared and identified the isolate. Physicians interpreted the warning and made the treatment decision." } }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0114" } }, "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 service depended on a microbiology laboratory, MALDI-TOF mass spectrometry, susceptibility records, clinical staff, and an available alternative antibiotic." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The service depended on a microbiology laboratory, MALDI-TOF mass spectrometry, susceptibility records, clinical staff, and an available alternative antibiotic." } }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "One 78-year-old woman with severe pneumonia, diabetes, hypertension, and chronic kidney disease." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "One 78-year-old woman with severe pneumonia, diabetes, hypertension, and chronic kidney disease." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "structured", "data": { "documented_patients": 1, "platform_uses_lower_bound": 100000, "uses_are_not_unique_beneficiaries": true, "patient_name": "Pseudonym in the source; no legal name recorded." } }, "provenance": { "kind": "source_review", "basis": "Hospital patient narrative and rollout paragraph.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "One reported recovery. The hospital’s figure of more than 100,000 platform uses is not a count of distinct beneficiaries." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health", "taxonomy_note": "The AI's part was turnaround time, a resistance prediction in one hour instead of days." } }, "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": "text", "text": "The hospital reported that an early resistance warning helped clinicians change the antibiotic before the woman recovered." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The hospital reported that an early resistance warning helped clinicians change the antibiotic before the woman recovered." } }, "outcomes[].distribution": { "state": "value_present", "value": { "kind": "text", "text": "The reported clinical result concerns one patient at one hospital. Platform-wide usage does not count confirmed individual benefits." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The reported clinical result concerns one patient at one hospital. Platform-wide usage does not count confirmed individual benefits." } }, "outcomes[].duration": { "state": "value_present", "value": { "kind": "text", "text": "The woman went home in late July 2022. The sources do not report her subsequent health." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "The woman went home in late July 2022. The sources do not report her subsequent health." } }, "outcomes[].id": { "state": "value_present", "value": { "kind": "text", "text": "AIBD-0114-outcome-01" }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "AIBD-0114-outcome-01" } }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "structured", "data": { "groups": [ "personal" ] } }, "provenance": { "kind": "curatorial", "basis": "The beneficiary groups correspond to the reviewed depth assessments.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "People." } }, "outcomes[].status": { "state": "value_present", "value": { "kind": "text", "text": "Recovery was reported by the treating hospital. The platform’s five-year mortality forecasts were intended benefits." }, "provenance": { "kind": "source_review", "basis": "CMUH, hospital account of 31 October 2022, patient narrative and figure captions; CNA and Liberty Times, 26 October 2022; CMUH 2022 sustainability report, p. 37.", "record_fields": [], "source_refs": [ "AIBD-0114-source-0001" ], "reviewed_on": "2026-10-25" }, "review_state": "reviewed", "coverage": "complete", "display": { "answer": "Recovery was reported by the treating hospital. 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Direct prediction of ceftazidime-resistant Stenotrophomonas maltophilia from routine MALDI-TOF mass spectra using machine learning. Journal of Infection 86(2): e58-e60, letter, online 9 September 2022, doi:10.1016/j.jinf.2022.09.005. 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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": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0088", "catalog_version": "1.0.0", "source_fingerprint": "7494545dd8aeba975dc7387c52bc23ab138bc6a416517d50ebb709bf803d7efa", "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-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" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "NHC-Melissa-AL132025-forecast-verification" ], "role": "Forecast verification for Hurricane Melissa" }, "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", "record_ids": [ "NHC-Melissa-AL132025-forecast-verification" ] }, "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": "DeepMind", "deployer": "NOAA National Hurricane 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": "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. 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It does not isolate GDMI’s effect on the official forecast." } }, "evaluations[].comparator": { "state": "value_present", "value": { "kind": "text", "text": "NHC’s official forecasts, conventional model guidance, and climatology-persistence forecasts for the same storm." }, "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 official forecasts, conventional model guidance, and climatology-persistence forecasts for the same storm." } }, "evaluations[].design": { "state": "value_present", "value": { "kind": "text", "text": "Retrospective verification of operational forecasts against the storm’s observed track and intensity." }, "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": "Retrospective verification of operational forecasts against the storm’s observed track and intensity." } }, "evaluations[].effect": { "state": "value_present", "value": { "kind": "text", "text": "GDMI’s track forecasts were comparable to NHC’s official forecasts through 96 hours. NHC performed slightly better at 120 hours and had lower intensity errors than every guidance model through 96 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": "GDMI’s track forecasts were comparable to NHC’s official forecasts through 96 hours. NHC performed slightly better at 120 hours and had lower intensity errors than every guidance model through 96 hours." } }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "evaluations[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "NHC-Melissa-AL132025-forecast-verification" ], "role": "Forecast verification for Hurricane Melissa" }, "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 forecast verification for Hurricane Melissa (AL132025)." } }, "evaluations[].measurement_method": { "state": "value_present", "value": { "kind": "text", "text": "NHC compared forecasts at matching lead times with its final estimates of the storm’s position and intensity. It compared official forecasts with model guidance on a common sample." }, "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 compared forecasts at matching lead times with its final estimates of the storm’s position and intensity. 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. 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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. 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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-0064", "case_url": "records/AIBD-0064.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The AI-supported scan read identified the major stroke immediately on arrival, enabling a 25-minute door-in-door-out. The treatment itself (thrombectomy) was delivered by the Oxford team. The stroke would very likely have been diagnosed without AI. So the AI's contribution is the speed of decision and transfer. The Lancet Digital Health study (which has Brainomix co-authors alongside NHS lead authors) supports that at population level." }, { "label": "Comparison", "value": "thrombectomy rate at the same 26 hospitals before the software was implemented, with the 81 non-implementing hospitals as the background trend", "url": "https://europepmc.org/abstract/MED/41339157" }, { "label": "Before", "value": "2·3% (376 of 15 969 patients)" }, { "label": "After", "value": "4·6% (751 of 15 428 patients), a relative increase of 100%" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0064", "title": "Brainomix 360 Stroke AI supports immediate stroke identification at Royal Berkshire Hospital; 83-year-old transferred for thrombectomy within 25 minutes", "summary": "AI-supported scan identified Jean Hines's major stroke on arrival; transferred in 25 minutes for successful thrombectomy; good recovery.", "date_observed": "2025-08-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Jean Hines, 83, collapsed at home and was taken to the Royal Berkshire Hospital, Reading, England, on a weekend in August. National Health Service (NHS) England's December 2025 statement gives no year, so 2025 is inferred. A scan carried out immediately on arrival, supported by the Brainomix AI imaging tool, identified a major stroke needing urgent specialist treatment. Within 25 minutes of reaching the emergency department she was on her way by ambulance to the John Radcliffe Hospital, Oxford. There she had a successful mechanical thrombectomy. She describes a very good physical recovery with no serious disabilities. NHS England states the Brainomix 360 Stroke tool is used in over 70 hospitals. It associates the tool with a 64-minute door-in-door-out reduction at primary stroke centers.", "category": "protective response", "unexpected": false, "purpose": "Brainomix 360 Stroke is built to analyze stroke imaging and support rapid treatment and transfer decisions, including selection for thrombectomy. This is its intended function.", "system": "Brainomix 360 Stroke", "model_version": null, "developer": "Brainomix", "deployer": "NHS England / Royal Berkshire Hospital", "location": "Reading, United Kingdom", "domain": "Healthcare - acute stroke care", "segment": "Hospital stroke imaging decision support", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Rapid transfer enabled thrombectomy for a major stroke and she reports no serious disabilities. The AI's contribution was the time saved." }, { "group": "organizational", "depth": 2, "reason": "NHS England reports a 25-minute door-in-door-out for this patient against a 64-minute average reduction at primary stroke centers. It also reports a doubling of thrombectomy rates (2.3% to 4.6%) at participating sites." } ], "ai_role": { "status": "assessed", "statement": "The AI-supported scan read identified the major stroke immediately on arrival, enabling a 25-minute door-in-door-out. The treatment itself (thrombectomy) was delivered by the Oxford team. The stroke would very likely have been diagnosed without AI. So the AI's contribution is the speed of decision and transfer. The Lancet Digital Health study (which has Brainomix co-authors alongside NHS lead authors) supports that at population level." }, "evidence": [ { "kind": "press release", "citation": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance. NHS England news, 2 December 2025.", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "A scan carried out immediately on arrival — supported by the Brainomix AI imaging tool — identified a major stroke and the need for urgent specialist treatment." }, { "kind": "press release", "citation": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance (second quotation).", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "Within just 25 minutes of reaching the emergency department, Jean was on her way by blue-light ambulance to the John Radcliffe Hospital in Oxford" }, { "kind": "peer-reviewed study", "citation": "Nagaratnam K, Neuhaus AA, Fensome L, et al. (2025). Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study. The Lancet Digital Health 7(12):100927. Three Brainomix employees and the corresponding author (dual NHS/Brainomix affiliation) are among 21 authors; lead authors are NHS and University of Oxford.", "title": "Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study", "url": "https://europepmc.org/abstract/MED/41339157", "date": "2025-12-02", "written_by": "makers", "quote": "Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS." } ], "replication": { "produced_by": [ "AI stroke imaging with results available to the admitting team immediately after the scan", "A regional transfer pathway from primary stroke center to a thrombectomy center" ], "must_hold": [ "Rapid ambulance transfer and an available thrombectomy team", "Clinicians act on the AI-supported read without waiting for a separate specialist report" ], "echoes": [ { "date": "2025-12-02", "setting": "107 NHS hospitals in England, 452,952 stroke patients 2019–2023: thrombectomy rates doubled (2.3% to 4.6%) at AI evaluation sites (population-level)", "model_version": null, "outcome": "recurred", "source": "Nagaratnam K et al. (2025) Lancet Digital Health 7(12):100927" } ] }, "paired_incident": null, "headline": "Brainomix flags Jean Hines's stroke; transferred in 25 minutes", "metric": "transferred in 25 minutes for successful thrombectomy", "comparator": { "against": "thrombectomy rate at the same 26 hospitals before the software was implemented, with the 81 non-implementing hospitals as the background trend", "before": "2·3% (376 of 15 969 patients)", "after": "4·6% (751 of 15 428 patients), a relative increase of 100%", "basis": "measured", "quote": "For evaluation sites, the pre-implementation endovascular thrombectomy rate was 2·3% (376 of 15 969 patients) and the post-implementation rate was 4·6% (751 of 15 428 patients), a relative increase of 100%. For non-evaluation sites, the pre-implementation rate was 1·6% (1431 of 88 712 patients) and the post-implementation rate was 2·6% (2410 of 89 900 patients), a relative increase of 62·5% (odds ratio [OR] for the interaction between site and time period 1·24 [95% CI 1·08-1·43]; p=0·0026).", "url": "https://europepmc.org/abstract/MED/41339157" }, "later": { "status": "scaled", "date": "2025-12-02", "note": "Four months after the inferred date of Jean Hines's stroke, the Lancet Digital Health study reported the same software systematically implemented in 26 NHS hospitals and associated with doubled thrombectomy rates.", "quote": "Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS. These results support the routine use of AI imaging software in the management of patients with stroke.", "url": "https://europepmc.org/abstract/MED/41339157" }, "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": "2026-10-20", "change": "NHS England item dated 2 December 2025 and the Lancet Digital Health item and echo dated 2025-12-02 from the Wayback Machine capture and the Europe PMC record, and category moved from specific harm prevented to protective response with prevention unproven with the taxonomy note rewritten, because no source shows that the AI changed this patient's outcome rather than the pathway timing.", "source": "NHS England news, 2 December 2025 (Wayback Machine capture of 9 December 2025 of https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/); Europe PMC record MED:41339157 (firstPublicationDate 2025-12-02)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://europepmc.org/abstract/MED/41339157; https://europepmc.org/abstract/MED/41339157", "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.brainomix.com/contact/; https://en.wikipedia.org/wiki/Royal_Berkshire_Hospital", "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": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study and the deployer's press release. 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So the AI's contribution is the speed of decision and transfer. The Lancet Digital Health study (which has Brainomix co-authors alongside NHS lead authors) supports that at population level." } } }, "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": "Rapid transfer enabled thrombectomy for a major stroke and she reports no serious disabilities. 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thrombectomy rates across the English NHS. 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National Health Service (NHS) England's December 2025 statement gives no year, so 2025 is inferred. A scan carried out immediately on arrival, supported by the Brainomix AI imaging tool, identified a major stroke needing urgent specialist treatment. Within 25 minutes of reaching the emergency department she was on her way by ambulance to the John Radcliffe Hospital, Oxford. There she had a successful mechanical thrombectomy. She describes a very good physical recovery with no serious disabilities. NHS England states the Brainomix 360 Stroke tool is used in over 70 hospitals. It associates the tool with a 64-minute door-in-door-out reduction at primary stroke centers.", "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": 3, "reason": "Rapid transfer enabled thrombectomy for a major stroke and she reports no serious disabilities. The AI's contribution was the time saved." }, { "group": "organizational", "depth": 2, "reason": "NHS England reports a 25-minute door-in-door-out for this patient against a 64-minute average reduction at primary stroke centers. It also reports a doubling of thrombectomy rates (2.3% to 4.6%) at participating sites." } ] } }, "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": "Brainomix 360 Stroke is built to analyze stroke imaging and support rapid treatment and transfer decisions, including selection for thrombectomy. This is its 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": "Reading, United Kingdom", "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": "Brainomix 360 Stroke is built to analyze stroke imaging and support rapid treatment and transfer decisions, including selection for thrombectomy. This is its 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": "2025-08-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "NHS England item dated 2 December 2025 and the Lancet Digital Health item and echo dated 2025-12-02 from the Wayback Machine capture and the Europe PMC record, and category moved from specific harm prevented to protective response with prevention unproven with the taxonomy note rewritten, because no source shows that the AI changed this patient's outcome rather than the pathway timing.", "source": "NHS England news, 2 December 2025 (Wayback Machine capture of 9 December 2025 of https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/); Europe PMC record MED:41339157 (firstPublicationDate 2025-12-02)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://europepmc.org/abstract/MED/41339157; https://europepmc.org/abstract/MED/41339157", "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.brainomix.com/contact/; https://en.wikipedia.org/wiki/Royal_Berkshire_Hospital", "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": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "AI stroke imaging with results available to the admitting team immediately after the scan", "A regional transfer pathway from primary stroke center to a thrombectomy center" ], "must_hold": [ "Rapid ambulance transfer and an available thrombectomy team", "Clinicians act on the AI-supported read without waiting for a separate specialist report" ], "echoes": [ { "date": "2025-12-02", "setting": "107 NHS hospitals in England, 452,952 stroke patients 2019–2023: thrombectomy rates doubled (2.3% to 4.6%) at AI evaluation sites (population-level)", "outcome": "recurred", "source": "Nagaratnam K et al. (2025) Lancet Digital Health 7(12):100927" } ] } } }, "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": [ "AI stroke imaging with results available to the admitting team immediately after the scan", "A regional transfer pathway from primary stroke center to a thrombectomy center" ], "must_hold": [ "Rapid ambulance transfer and an available thrombectomy team", "Clinicians act on the AI-supported read without waiting for a separate specialist report" ], "echoes": [ { "date": "2025-12-02", "setting": "107 NHS hospitals in England, 452,952 stroke patients 2019–2023: thrombectomy rates doubled (2.3% to 4.6%) at AI evaluation sites (population-level)", "outcome": "recurred", "source": "Nagaratnam K et al. (2025) Lancet Digital Health 7(12):100927" } ] } } }, "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": "NHS England item dated 2 December 2025 and the Lancet Digital Health item and echo dated 2025-12-02 from the Wayback Machine capture and the Europe PMC record, and category moved from specific harm prevented to protective response with prevention unproven with the taxonomy note rewritten, because no source shows that the AI changed this patient's outcome rather than the pathway timing.", "source": "NHS England news, 2 December 2025 (Wayback Machine capture of 9 December 2025 of https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/); Europe PMC record MED:41339157 (firstPublicationDate 2025-12-02)", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://europepmc.org/abstract/MED/41339157; https://europepmc.org/abstract/MED/41339157", "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.brainomix.com/contact/; https://en.wikipedia.org/wiki/Royal_Berkshire_Hospital", "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": "Healthcare - acute stroke care", "segment": "Hospital stroke imaging decision support" } }, "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": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance. NHS England news, 2 December 2025.", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "A scan carried out immediately on arrival — supported by the Brainomix AI imaging tool — identified a major stroke and the need for urgent specialist treatment." }, { "kind": "press release", "citation": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance (second quotation).", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "Within just 25 minutes of reaching the emergency department, Jean was on her way by blue-light ambulance to the John Radcliffe Hospital in Oxford" }, { "kind": "peer-reviewed study", "citation": "Nagaratnam K, Neuhaus AA, Fensome L, et al. (2025). Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study. The Lancet Digital Health 7(12):100927. Three Brainomix employees and the corresponding author (dual NHS/Brainomix affiliation) are among 21 authors; lead authors are NHS and University of Oxford.", "title": "Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study", "url": "https://europepmc.org/abstract/MED/41339157", "date": "2025-12-02", "written_by": "makers", "quote": "Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS." } ] } }, "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-0064-source-0001", "AIBD-0064-source-0002", "AIBD-0064-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": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance. NHS England news, 2 December 2025.", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "A scan carried out immediately on arrival — supported by the Brainomix AI imaging tool — identified a major stroke and the need for urgent specialist treatment." }, { "kind": "press release", "citation": "NHS England (2025). 'Life-changing' AI support helping stroke patients get a second chance (second quotation).", "title": "'Life-changing' AI support helping stroke patients get a second chance", "url": "https://www.england.nhs.uk/2025/12/life-changing-ai-support-helping-stroke-patients-get-a-second-chance/", "date": "2025-12-02", "written_by": "deployer", "quote": "Within just 25 minutes of reaching the emergency department, Jean was on her way by blue-light ambulance to the John Radcliffe Hospital in Oxford" }, { "kind": "peer-reviewed study", "citation": "Nagaratnam K, Neuhaus AA, Fensome L, et al. (2025). Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study. The Lancet Digital Health 7(12):100927. Three Brainomix employees and the corresponding author (dual NHS/Brainomix affiliation) are among 21 authors; lead authors are NHS and University of Oxford.", "title": "Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study", "url": "https://europepmc.org/abstract/MED/41339157", "date": "2025-12-02", "written_by": "makers", "quote": "Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS." } ] } }, "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-supported scan identified Jean Hines's major stroke on arrival; transferred in 25 minutes for successful thrombectomy; good recovery.", "account": "Jean Hines, 83, collapsed at home and was taken to the Royal Berkshire Hospital, Reading, England, on a weekend in August. National Health Service (NHS) England's December 2025 statement gives no year, so 2025 is inferred. A scan carried out immediately on arrival, supported by the Brainomix AI imaging tool, identified a major stroke needing urgent specialist treatment. Within 25 minutes of reaching the emergency department she was on her way by ambulance to the John Radcliffe Hospital, Oxford. There she had a successful mechanical thrombectomy. She describes a very good physical recovery with no serious disabilities. NHS England states the Brainomix 360 Stroke tool is used in over 70 hospitals. It associates the tool with a 64-minute door-in-door-out reduction at primary stroke 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": "Brainomix 360 Stroke" } }, "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": "Brainomix" } }, "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": "Brainomix 360 Stroke AI supports immediate stroke identification at Royal Berkshire Hospital; 83-year-old transferred for thrombectomy within 25 minutes", "headline": "Brainomix flags Jean Hines's stroke; transferred in 25 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": [ "a9d99f6616f7021e77b998362e17b8b0156769d248596b153f6ac094aba09b20" ], "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": "Jean Hines, 83, collapsed at home and was taken to the Royal Berkshire Hospital, Reading, England, on a weekend in August. National Health Service (NHS) England's December 2025 statement gives no year, so 2025 is inferred. A scan carried out immediately on arrival, supported by the Brainomix AI imaging tool, identified a major stroke needing urgent specialist treatment. Within 25 minutes of reaching the emergency department she was on her way by ambulance to the John Radcliffe Hospital, Oxford. There she had a successful mechanical thrombectomy. She describes a very good physical recovery with no serious disabilities. NHS England states the Brainomix 360 Stroke tool is used in over 70 hospitals. It associates the tool with a 64-minute door-in-door-out reduction at primary stroke centers.", "replication": { "produced_by": [ "AI stroke imaging with results available to the admitting team immediately after the scan", "A regional transfer pathway from primary stroke center to a thrombectomy center" ], "must_hold": [ "Rapid ambulance transfer and an available thrombectomy team", "Clinicians act on the AI-supported read without waiting for a separate specialist report" ], "echoes": [ { "date": "2025-12-02", "setting": "107 NHS hospitals in England, 452,952 stroke patients 2019–2023: thrombectomy rates doubled (2.3% to 4.6%) at AI evaluation sites (population-level)", "outcome": "recurred", "source": "Nagaratnam K et al. (2025) Lancet Digital Health 7(12):100927" } ] } } }, "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-0078", "case_url": "records/AIBD-0078.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The software detected the haematoma and auto-alerted the receiving team, which coordinated stabilization advice and transfer. A CT ordered for severe headache would have shown the bleed regardless, so the AI's contribution is the speed of notification and transfer. Dr Kellner discloses research grant support from Viz.AI among other companies. No author is a Viz.ai employee." }, { "label": "Comparison", "value": "the average interhospital transfer time for similar intracerebral hemorrhage cases, taken from a published New York City hospital-system cohort", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12006056/" }, { "label": "Before", "value": "199.7 minutes" }, { "label": "After", "value": "101 minutes" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "record": { "id": "AIBD-0078", "title": "Viz ICH Plus detects a parieto-occipital hemorrhage and auto-alerts the transfer team; 47-year-old moved between hospitals of a large urban health system in 101 minutes, mRS 1 at one year", "summary": "Viz ICH Plus flagged a 47-year-old's haematoma and alerted the transfer team; transfer in 101 min vs 199.7 average; mRS 1 at one year.", "date_observed": "2025-03-18", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A 47-year-old man came to the transferring hospital of the authors' large urban health system. He had severe headache, nausea and vomiting but no speech or motor deficits. The paper names neither the hospitals nor the city. All authors are at the Icahn School of Medicine at Mount Sinai, New York. Viz ICH Plus (Viz.ai) analyzed his computed tomography (CT), revealing a substantial right parieto-occipital haematoma with ventricular extension. It auto-alerted the Neuroemergencies Management and Transfer (NEMAT) team at the receiving specialized stroke center. With an ICH score of 2 he was transferred within 101 minutes across 13.7 miles, against an average interhospital transfer time of 199.7 minutes. He had supratentorial craniotomy with endoscopic haematoma evacuation and ventriculoperitoneal shunt placement. His scores were mRS 4 and NIHSS 6 at one month, 3 and 5 at three months. At one year: mRS 1 and NIHSS 2.", "category": "harm prevented", "unexpected": false, "purpose": "Viz ICH Plus is built to detect intracranial hemorrhage on non-contrast CT and automatically notify care and transfer teams. Faster interhospital transfer is its intended function.", "system": "Viz ICH Plus", "model_version": null, "developer": "Viz.ai", "deployer": "Large urban healthcare system, not named in the report; the city is not stated, and all authors are affiliated with Neurosurgery, Icahn School of Medicine at Mount Sinai, New York", "location": null, "domain": "Healthcare - neurosurgery / intracerebral hemorrhage", "segment": "AI hemorrhage detection with automated transfer notification", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "After a hemorrhage with ventricular extension the patient improved from mRS 4 at one month to mRS 1 at one year. The AI's share is the time saved in transfer, the surgery being the treatment." }, { "group": "organizational", "depth": 2, "reason": "Interhospital transfer took 101 minutes against the system's 199.7-minute average, with the receiving team engaged before the patient left the first hospital." } ], "ai_role": { "status": "assessed", "statement": "The software detected the haematoma and auto-alerted the receiving team, which coordinated stabilization advice and transfer. A CT ordered for severe headache would have shown the bleed regardless, so the AI's contribution is the speed of notification and transfer. Dr Kellner discloses research grant support from Viz.AI among other companies. No author is a Viz.ai employee." }, "evidence": [ { "kind": "case report", "citation": "Afreen R, Ezzat B, Kalagara R, Dangayach NS, Kellner CP (2025). Optimizing Intracerebral Hemorrhage Management and Interhospital Transfer With Viz ICH Plus AI Technology. Cureus 17(3):e80790.", "title": "Optimizing Intracerebral Hemorrhage Management and Interhospital Transfer With Viz ICH Plus AI Technology", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12006056/", "date": "2025-03-18", "written_by": "deployer", "quote": "Viz ICH Plus analyzed the patient's CT, revealing a substantial right parieto-occipital hematoma with ventricular extension, and automatically alerted the Neuroemergencies Management and Transfer (NEMAT) team" }, { "kind": "case report", "citation": "Afreen R et al. (2025). Cureus 17(3):e80790 (second quotation).", "title": "Optimizing Intracerebral Hemorrhage Management and Interhospital Transfer With Viz ICH Plus AI Technology", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12006056/", "date": "2025-03-18", "written_by": "deployer", "quote": "With an ICH score of 2, the patient was transferred within 101 minutes across a 13.7-mile distance - much faster than the average IHT time of 199.7 minutes" }, { "kind": "case report", "citation": "Afreen R et al. (2025). 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Under the Memorial stroke protocol EMS had already pre-notified the hospital and the neurointerventional team was ready before imaging. The occlusion would have been identified on the CT scan regardless. So the AI's contribution is speed at the point of care, and its share of the time saved cannot be separated from the pre-notification. The only counterfactual offered is Dr Mehta's general description of the pre-RapidAI workflow." }, { "label": "Comparison", "value": "the hospital's workflow before RapidAI, with scans viewed at workstations", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients" }, { "label": "Before", "value": "tethered to our workstations and had to take multiple steps to open the software to view the scans" }, { "label": "After", "value": "RapidAI alert on his smartphone and smartwatch; artery opened in under 60 minutes from arrival" }, { "label": "Comparison basis", "value": "Assumed" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "assumed" }, "record": { "id": "AIBD-0079", "title": "RapidAI automated LVO detection alerts Memorial Neuroscience Institute's stroke team; 29-year-old firefighter's artery reopened within 60 minutes of arrival", "summary": "RapidAI's automated LVO alert mobilized the stroke team for Eric, 29; thrombectomy restored flow under 60 minutes from arrival; no deficits.", "date_observed": "2022-05-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Eric, a 29-year-old firefighter and paramedic, felt unwell after a workout. 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Hospital news release, 12 May 2022 (second quotation).", "title": "Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "date": "2022-05-12", "written_by": "deployer", "quote": "the blocked artery was fully opened, and blood flow restored after the thrombectomy procedure in under 60 minutes from his arrival" }, { "kind": "press release", "citation": "Memorial Healthcare System (2022). Hospital news release, 12 May 2022 (quotation from Dr Brijesh P. 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Under the Memorial stroke protocol EMS had already pre-notified the hospital and the neurointerventional team was ready before imaging. The occlusion would have been identified on the CT scan regardless. So the AI's contribution is speed at the point of care, and its share of the time saved cannot be separated from the pre-notification. 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He tried to send a text and could not move his left thumb, then his left leg went numb. His trainer noticed he was slurring and called 9-1-1. Emergency medical services (EMS) took him to Memorial Hospital West's Comprehensive Stroke Center. Under the Memorial stroke protocol the paramedics had already alerted the hospital. The neurointerventional surgery team was ready to do a computed tomography (CT) scan and treat in the catheterization laboratory. As soon as scan results were available, Dr Brijesh Mehta received the RapidAI alert on his smartphone and smartwatch. It showed the software's automated detection of a large vessel occlusion (LVO). That prompted him to mobilize the cath lab team and fast-track Eric for mechanical thrombectomy. The blocked artery was fully opened and blood flow restored under 60 minutes from his arrival. Eric described himself as alive with no deficits. The hospital published the account on 12 May 2022; the stroke date and its city are not given.", "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": 3, "reason": "A 29-year-old had his occluded artery reopened within 60 minutes of arrival and reports being alive with no deficits. The AI's share is the time saved in fast-tracking the cath lab team after EMS had already pre-notified the hospital and the team was ready. The thrombectomy was the treatment." }, { "group": "organizational", "depth": 2, "reason": "Dr Mehta states that the platform streamlines workflow and cuts time compared with the previous need to open scans at workstations." } ] } }, "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": "RapidAI's platform is built to detect large vessel occlusion on imaging and push alerts to the stroke team so that thrombectomy can start sooner. This is its 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": { "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": "RapidAI's platform is built to detect large vessel occlusion on imaging and push alerts to the stroke team so that thrombectomy can start sooner. This is its 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": "2022-05-12", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected Eric's symptoms to the source's wording (could not move his left thumb, then his left leg went numb, trainer noticed slurring), added that EMS had pre-notified the hospital and the team was ready before imaging so that the alert's role is fast-tracking the cath lab team, changed “CT angiogram” to “CT scan”, quoted Dr Mehta's sentence in full, and moved the category from specific harm prevented to protective response with prevention unproven because the source offers no individual counterfactual for the AI's share of the time saved.", "source": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; 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Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients. Hospital news release, 12 May 2022, quoting Dr Brijesh P. Mehta, medical director of the Comprehensive Stroke Program and Neurointerventional Surgery at Memorial Neuroscience Institute, and the patient.", "title": "Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "date": "2022-05-12", "written_by": "deployer", "quote": "He received the RapidAI alert on his smartphone and smartwatch, showing the software's automated detection of LVO (large vessel occlusion)" }, { "kind": "press release", "citation": "Memorial Healthcare System (2022). Hospital news release, 12 May 2022 (second quotation).", "title": "Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "date": "2022-05-12", "written_by": "deployer", "quote": "the blocked artery was fully opened, and blood flow restored after the thrombectomy procedure in under 60 minutes from his arrival" }, { "kind": "press release", "citation": "Memorial Healthcare System (2022). Hospital news release, 12 May 2022 (quotation from Dr Brijesh P. 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Hospital news release, 12 May 2022 (second quotation).", "title": "Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "date": "2022-05-12", "written_by": "deployer", "quote": "the blocked artery was fully opened, and blood flow restored after the thrombectomy procedure in under 60 minutes from his arrival" }, { "kind": "press release", "citation": "Memorial Healthcare System (2022). Hospital news release, 12 May 2022 (quotation from Dr Brijesh P. Mehta).", "title": "Memorial Neuroscience Institute Uses AI to Optimize Care and Positive Outcomes for Stroke Patients", "url": "https://www.mhs.net/news/2022/05/memorial-neuroscience-institute-uses-ai-to-optimize-care-and-positive-outcomes-for-stroke-patients", "date": "2022-05-12", "written_by": "deployer", "quote": "The value of artificial intelligence is that it streamlines our workflow, cuts down time and allows us to make decisions quickly and effectively at the point of care to benefit our patients." } ] } }, "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": "RapidAI's automated LVO alert mobilized the stroke team for Eric, 29; thrombectomy restored flow under 60 minutes from arrival; no deficits.", "account": "Eric, a 29-year-old firefighter and paramedic, felt unwell after a workout. He tried to send a text and could not move his left thumb, then his left leg went numb. His trainer noticed he was slurring and called 9-1-1. Emergency medical services (EMS) took him to Memorial Hospital West's Comprehensive Stroke Center. Under the Memorial stroke protocol the paramedics had already alerted the hospital. The neurointerventional surgery team was ready to do a computed tomography (CT) scan and treat in the catheterization laboratory. As soon as scan results were available, Dr Brijesh Mehta received the RapidAI alert on his smartphone and smartwatch. It showed the software's automated detection of a large vessel occlusion (LVO). That prompted him to mobilize the cath lab team and fast-track Eric for mechanical thrombectomy. The blocked artery was fully opened and blood flow restored under 60 minutes from his arrival. Eric described himself as alive with no deficits. The hospital published the account on 12 May 2022; the stroke date and its city are 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": "RapidAI imaging platform (automated LVO 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": "RapidAI" } }, "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": "RapidAI automated LVO detection alerts Memorial Neuroscience Institute's stroke team; 29-year-old firefighter's artery reopened within 60 minutes of arrival", "headline": "RapidAI LVO alert gets Eric's artery reopened within 60 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": [ "1067971966d0f02e27b9747935cc762ce4a7715429d236e1b8344bb89c6ee12b" ], "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": "Eric, a 29-year-old firefighter and paramedic, felt unwell after a workout. He tried to send a text and could not move his left thumb, then his left leg went numb. His trainer noticed he was slurring and called 9-1-1. Emergency medical services (EMS) took him to Memorial Hospital West's Comprehensive Stroke Center. Under the Memorial stroke protocol the paramedics had already alerted the hospital. The neurointerventional surgery team was ready to do a computed tomography (CT) scan and treat in the catheterization laboratory. As soon as scan results were available, Dr Brijesh Mehta received the RapidAI alert on his smartphone and smartwatch. It showed the software's automated detection of a large vessel occlusion (LVO). That prompted him to mobilize the cath lab team and fast-track Eric for mechanical thrombectomy. The blocked artery was fully opened and blood flow restored under 60 minutes from his arrival. Eric described himself as alive with no deficits. The hospital published the account on 12 May 2022; the stroke date and its city are not given.", "replication": { "produced_by": [ "RapidAI LVO detection pushing alerts to the on-call neurointerventionalist's phone and watch", "A comprehensive stroke center with an in-house thrombectomy team" ], "must_hold": [ "Rapid recognition of stroke signs and EMS transport to a thrombectomy-capable center", "Team mobilization on the AI alert rather than after a formal radiology read" ], "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-0565", "case_url": "records/AIBD-0565.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Computer vision supplies near-miss observations and visual evidence that practitioners used to select road-layout interventions. The source reports implemented measures, but does not establish that collisions or injuries were prevented." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0565", "title": "Transport for West Midlands: Near miss detection with VivaCity", "summary": "Transport for West Midlands reports 61,000 detected near misses and road-layout changes informed by AI. Avoided injuries are not established.", "date_observed": "2025-01-06", "date_basis": "source publication", "date_precision": "day", "benefit_status": "mixed", "source_collection": "LGA AI case-study bank", "source_entry": "LGA-029", "account": "Transport for West Midlands enabled VivaCity near-miss detection in 40 sensors and used the observations to select and implement road-layout changes. The source reports 61,000 detected near misses in six months and the installation of measures including central islands and narrower junction mouths. The interventions are documented, but the source does not establish a reduction in collisions or injuries.", "category": "protective response", "unexpected": false, "purpose": "Identify near-miss patterns and use them to support preventive road-safety interventions.", "system": "VivaCity AI near-miss detection sensors and analytics", "model_version": null, "developer": "VivaCity", "deployer": "Transport for West Midlands", "location": "United Kingdom", "domain": "Road safety and transport analytics", "segment": null, "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "New observations led to implemented interventions, supporting improved analytical and intervention capability." }, { "group": "societal", "depth": 2, "reason": "Central islands and narrower junction mouths were installed using the observations. Changes in collisions and injuries remain unmeasured." } ], "ai_role": { "status": "assessed", "statement": "Computer vision supplies near-miss observations and visual evidence that practitioners used to select road-layout interventions. The source reports implemented measures, but does not establish that collisions or injuries were prevented." }, "evidence": [ { "kind": "report", "citation": "Council or project account published in the Local Government Association AI case-study bank. The account provides no independent outcome evaluation.", "title": "Transport for West Midlands: Near miss detection with VivaCity", "url": "https://www.local.gov.uk/case-studies/transport-west-midlands-near-miss-detection-vivacity", "date": "2025-01-06", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "The January–August 2024 pilot enabled near-miss functions on 40 existing sensors, with later continuation funding.", "Practitioners used dashboards and video clips to identify hotspots and implement measures such as islands and narrower junction mouths." ], "must_hold": [ "The conditions needed to reproduce the benefit are unspecified." ], "echoes": [] }, "paired_incident": null, "headline": "West Midlands uses near-miss data to change road layouts", "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.local.gov.uk/case-studies/transport-west-midlands-near-miss-detection-vivacity", "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-0565", "catalog_version": "1.0.0", "source_fingerprint": "90d5011ce5211de1c3b069ec2521e6fa815560b55fd8424ada688eb818e5bdc2", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0565-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": "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": "VivaCity", "deployer": "Transport for West Midlands" } }, "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": "Computer vision supplies near-miss observations and visual evidence that practitioners used to select road-layout interventions. 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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": { "source_collection": "LGA AI case-study bank", "source_entry": "LGA-029" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "source_collection", "source_entry" ], "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-0565" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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West Midlands enabled VivaCity near-miss detection in 40 sensors and used the observations to select and implement road-layout changes. The source reports 61,000 detected near misses in six months and the installation of measures including central islands and narrower junction mouths. 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Avoided injuries are not established.", "account": "Transport for West Midlands enabled VivaCity near-miss detection in 40 sensors and used the observations to select and implement road-layout changes. The source reports 61,000 detected near misses in six months and the installation of measures including central islands and narrower junction mouths. The interventions are documented, but the source does not establish a reduction in collisions 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": "VivaCity AI near-miss detection sensors and 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": "VivaCity" } }, "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": "Transport for West Midlands: Near miss detection with VivaCity", "headline": "West Midlands uses near-miss data to change road layouts" } }, "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": [ "90d5011ce5211de1c3b069ec2521e6fa815560b55fd8424ada688eb818e5bdc2" ], "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": "Transport for West Midlands enabled VivaCity near-miss detection in 40 sensors and used the observations to select and implement road-layout changes. The source reports 61,000 detected near misses in six months and the installation of measures including central islands and narrower junction mouths. The interventions are documented, but the source does not establish a reduction in collisions or injuries.", "replication": { "produced_by": [ "The January–August 2024 pilot enabled near-miss functions on 40 existing sensors, with later continuation funding.", "Practitioners used dashboards and video clips to identify hotspots and implement measures such as islands and narrower junction mouths." ], "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-0036", "case_url": "records/AIBD-0036.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Automated classification produced the candidate detections. Analysts validated them on shore before NOAA acted. The resulting Slow Zone is voluntary." }, { "label": "Comparison", "value": "aerial surveillance", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, { "label": "After", "value": "more slowdowns than aerial surveillance by a factor of two to five during fall and summer" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "measured" }, "record": { "id": "AIBD-0036", "title": "Glider-borne automated whale-call classifier triggers a NOAA Right Whale Slow Zone east of Portland, Maine", "summary": "WHOI/NOAA glider's automated call classifier detected right whales on 27 December 2023; NOAA declared a Slow Zone east of Portland, ME.", "date_observed": "2023-12-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 27 December 2023 the Gulf of Maine Winter Slocum glider acoustically detected North Atlantic right whales east of Portland, Maine. Woods Hole Oceanographic Institution and NOAA's Northeast Fisheries Science Center operate it. Such gliders carry a DMON instrument running the Low-Frequency Detection and Classification System (LFDCS). It matches pitch-tracked calls against a call library using quadratic discriminant function analysis. Shore-based analysts validate detections. On 29 December 2023 NOAA Fisheries announced a voluntary Right Whale Slow Zone bounded by 43°49'N, 43°09'N, 67°02'W and 68°57'W. It ran 27 December 2023 through 11 January 2024. NOAA asked mariners to route around the area or transit at 10 knots or less.", "category": "protective response", "unexpected": false, "purpose": "LFDCS was built to detect and classify baleen whale calls in near real time for management use. The Slow Zone trigger was that purpose.", "system": "DMON instrument running the Low-Frequency Detection and Classification System (LFDCS) on a Slocum glider", "model_version": null, "developer": "Woods Hole Oceanographic Institution (Mark Baumgartner)", "deployer": "Woods Hole Oceanographic Institution and NOAA Northeast Fisheries Science Center; NOAA Fisheries issues Slow Zones", "location": "Gulf of Maine east of Portland, United States", "domain": "Marine conservation / shipping", "segment": "Endangered-species vessel-strike mitigation", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "societal", "depth": 2, "reason": "A protective speed-reduction request for an endangered species over a defined area for about 15 days. No evidence that a strike was averted or that specific vessels complied." } ], "ai_role": { "status": "assessed", "statement": "Automated classification produced the candidate detections. Analysts validated them on shore before NOAA acted. The resulting Slow Zone is voluntary." }, "evidence": [ { "kind": "regulatory document", "citation": "NOAA Fisheries (29 Dec 2023). New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11. GovDelivery bulletin.", "title": "New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11", "url": "https://content.govdelivery.com/accounts/USNOAAFISHERIES/bulletins/3824b02", "date": "2023-12-29", "written_by": "deployer", "quote": "detected the presence of right whales east of Portland, ME" }, { "kind": "peer-reviewed study", "citation": "Indeck KL, Baumgartner MF, Lecavalier L, et al. (2025). Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales. Oceanography 38(Suppl. 1):13-21.", "title": "Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales", "date": null, "written_by": "makers", "quote": "154 Slow Zones have been triggered or extended by acoustic detections of NARWs, and 51 (33%) of those Slow Zones were triggered or extended by gliders" }, { "kind": "other", "citation": "Baumgartner M (WHOI). Low-Frequency Detection and Classification System (LFDCS) user manual (developer documentation).", "title": "Low-Frequency Detection and Classification System (LFDCS)", "url": "https://dcs.whoi.edu/lfdcs_manual/lfdcs_manual.html", "date": null, "written_by": "makers", "quote": "The call library can contain hundreds of these known call types, allowing the LFDCS to efficiently detect and classify many different calls produced by numerous species." }, { "kind": "report", "citation": "Wilder J, Davis G, DeAngelis A, Van Parijs S, Baumgartner M (2023). Low-Frequency Detection and Classification System (LFDCS) Reference Guide. NOAA Institutional Repository.", "title": "Low-Frequency Detection and Classification System (LFDCS) Reference Guide", "url": "https://repository.library.noaa.gov/view/noaa/48671", "date": null, "written_by": "makers", "quote": "a software system created for automated detection and classification of low-frequency baleen whale vocalizations in archival and real-time acoustic data" }, { "kind": "regulatory document", "citation": "NOAA Fisheries. Reducing Vessel Strikes to North Atlantic Right Whales (Slow Zones policy page).", "title": "Reducing Vessel Strikes to North Atlantic Right Whales", "url": "https://www.fisheries.noaa.gov/national/endangered-species-conservation/reducing-vessel-strikes-north-atlantic-right-whales", "date": null, "written_by": "deployer", "quote": "For a period of 15 days after a whale is detected, mariners are encouraged to avoid these areas" } ], "replication": { "produced_by": [ "Glider or buoy carrying DMON/LFDCS in right whale habitat", "Shore-based analyst validation protocol", "NOAA Slow Zone program (established late 2020) accepting acoustic triggers" ], "must_hold": [ "Whales vocalize within detection range", "Mariners comply voluntarily for any protective effect to materialize" ], "echoes": [ { "date": "2024-01-20", "setting": "Southeast US waters; first definite glider-based acoustic detection there, which triggered a Southeast Early Warning System alert for vessel strike mitigation", "model_version": null, "outcome": "recurred", "source": "Indeck et al. (2025) Oceanography, https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, { "date": null, "setting": "US East Coast, four years of the Slow Zone program: 154 Slow Zones triggered or extended by acoustic detections, 51 by gliders", "model_version": null, "outcome": "recurred", "source": "Indeck et al. (2025) Oceanography" } ] }, "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": "Automated detection to protect an endangered species: the glider's whale-call classifier triggered a ship Slow Zone, while Ezemvelo KZN Wildlife's AI cameras failed to detect poachers and four rhinos were killed." }, "headline": "LFDCS glider detections trigger a NOAA Right Whale Slow Zone", "metric": "mariners route around the area or transit at 10 knots or less", "comparator": { "against": "aerial surveillance", "before": null, "after": "more slowdowns than aerial surveillance by a factor of two to five during fall and summer", "basis": "measured", "quote": "Gliders triggered more slowdowns than aerial surveillance by a factor of two to five during fall and summer but were less effective during spring, as whales migrating into the GoSL tend to call at lower rates and occur at lower densities than during other behavioral states (Parks et al., 2011; Matthews and Parks, 2021).", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, "later": { "status": "scaled", "date": "2025-01-21", "note": "By early 2025 gliders had triggered or extended 51 of the 154 acoustically triggered Slow Zones, and on 20 January 2024 a glider made the first definite glider-based acoustic detection in southeast US waters.", "quote": "In the four years since its inception, 154 Slow Zones have been triggered or extended by acoustic detections of NARWs, and 51 (33%) of those Slow Zones were triggered or extended by gliders operated by the Woods Hole Oceanographic Institution, Rutgers University, Stony Brook University, and The University of Maine during 57 separate glider missions.", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, "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 219 (medium 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://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales; https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales", "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/Woods_Hole_Oceanographic_Institution; https://en.wikipedia.org/wiki/Woods_Hole_Oceanographic_Institution", "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": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the makers' report, the makers' other and two regulatory documents from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0036", "catalog_version": "1.0.0", "source_fingerprint": "181041b30300fced8151c056ec9a4d5c663517c4a7701680671af7c2eeb1c947", "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" } ], "source_ids": [ "AIBD-0036-source-0001", "AIBD-0036-source-0002", "AIBD-0036-source-0003", "AIBD-0036-source-0004", "AIBD-0036-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": "Woods Hole Oceanographic Institution (Mark Baumgartner)", "deployer": "Woods Hole Oceanographic Institution and NOAA Northeast Fisheries Science Center; NOAA Fisheries issues Slow Zones" } }, "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": "Automated classification produced the candidate detections. 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(2025) Oceanography, https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, { "setting": "US East Coast, four years of the Slow Zone program: 154 Slow Zones triggered or extended by acoustic detections, 51 by gliders", "outcome": "recurred", "source": "Indeck et al. 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New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11. GovDelivery bulletin.", "title": "New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11", "url": "https://content.govdelivery.com/accounts/USNOAAFISHERIES/bulletins/3824b02", "date": "2023-12-29", "written_by": "deployer", "quote": "detected the presence of right whales east of Portland, ME" }, { "kind": "peer-reviewed study", "citation": "Indeck KL, Baumgartner MF, Lecavalier L, et al. (2025). Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales. Oceanography 38(Suppl. 1):13-21.", "title": "Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales", "written_by": "makers", "quote": "154 Slow Zones have been triggered or extended by acoustic detections of NARWs, and 51 (33%) of those Slow Zones were triggered or extended by gliders" }, { "kind": "other", "citation": "Baumgartner M (WHOI). Low-Frequency Detection and Classification System (LFDCS) user manual (developer documentation).", "title": "Low-Frequency Detection and Classification System (LFDCS)", "url": "https://dcs.whoi.edu/lfdcs_manual/lfdcs_manual.html", "written_by": "makers", "quote": "The call library can contain hundreds of these known call types, allowing the LFDCS to efficiently detect and classify many different calls produced by numerous species." }, { "kind": "report", "citation": "Wilder J, Davis G, DeAngelis A, Van Parijs S, Baumgartner M (2023). Low-Frequency Detection and Classification System (LFDCS) Reference Guide. NOAA Institutional Repository.", "title": "Low-Frequency Detection and Classification System (LFDCS) Reference Guide", "url": "https://repository.library.noaa.gov/view/noaa/48671", "written_by": "makers", "quote": "a software system created for automated detection and classification of low-frequency baleen whale vocalizations in archival and real-time acoustic data" }, { "kind": "regulatory document", "citation": "NOAA Fisheries. Reducing Vessel Strikes to North Atlantic Right Whales (Slow Zones policy page).", "title": "Reducing Vessel Strikes to North Atlantic Right Whales", "url": "https://www.fisheries.noaa.gov/national/endangered-species-conservation/reducing-vessel-strikes-north-atlantic-right-whales", "written_by": "deployer", "quote": "For a period of 15 days after a whale is detected, mariners are encouraged to avoid these areas" } ] } }, "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-0036-source-0001", "AIBD-0036-source-0002", "AIBD-0036-source-0003", "AIBD-0036-source-0004", "AIBD-0036-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": "regulatory document", "citation": "NOAA Fisheries (29 Dec 2023). New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11. GovDelivery bulletin.", "title": "New Right Whale Slow Zone: East of Portland, ME - Effective Through 1/11", "url": "https://content.govdelivery.com/accounts/USNOAAFISHERIES/bulletins/3824b02", "date": "2023-12-29", "written_by": "deployer", "quote": "detected the presence of right whales east of Portland, ME" }, { "kind": "peer-reviewed study", "citation": "Indeck KL, Baumgartner MF, Lecavalier L, et al. (2025). Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales. Oceanography 38(Suppl. 1):13-21.", "title": "Glider Surveillance for Near-Real-Time Detection and Spatial Management of North Atlantic Right Whales", "url": "https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales", "written_by": "makers", "quote": "154 Slow Zones have been triggered or extended by acoustic detections of NARWs, and 51 (33%) of those Slow Zones were triggered or extended by gliders" }, { "kind": "other", "citation": "Baumgartner M (WHOI). Low-Frequency Detection and Classification System (LFDCS) user manual (developer documentation).", "title": "Low-Frequency Detection and Classification System (LFDCS)", "url": "https://dcs.whoi.edu/lfdcs_manual/lfdcs_manual.html", "written_by": "makers", "quote": "The call library can contain hundreds of these known call types, allowing the LFDCS to efficiently detect and classify many different calls produced by numerous species." }, { "kind": "report", "citation": "Wilder J, Davis G, DeAngelis A, Van Parijs S, Baumgartner M (2023). Low-Frequency Detection and Classification System (LFDCS) Reference Guide. NOAA Institutional Repository.", "title": "Low-Frequency Detection and Classification System (LFDCS) Reference Guide", "url": "https://repository.library.noaa.gov/view/noaa/48671", "written_by": "makers", "quote": "a software system created for automated detection and classification of low-frequency baleen whale vocalizations in archival and real-time acoustic data" }, { "kind": "regulatory document", "citation": "NOAA Fisheries. Reducing Vessel Strikes to North Atlantic Right Whales (Slow Zones policy page).", "title": "Reducing Vessel Strikes to North Atlantic Right Whales", "url": "https://www.fisheries.noaa.gov/national/endangered-species-conservation/reducing-vessel-strikes-north-atlantic-right-whales", "written_by": "deployer", "quote": "For a period of 15 days after a whale is detected, mariners are encouraged to avoid these areas" } ] } }, "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": "WHOI/NOAA glider's automated call classifier detected right whales on 27 December 2023; NOAA declared a Slow Zone east of Portland, ME.", "account": "On 27 December 2023 the Gulf of Maine Winter Slocum glider acoustically detected North Atlantic right whales east of Portland, Maine. Woods Hole Oceanographic Institution and NOAA's Northeast Fisheries Science Center operate it. Such gliders carry a DMON instrument running the Low-Frequency Detection and Classification System (LFDCS). It matches pitch-tracked calls against a call library using quadratic discriminant function analysis. Shore-based analysts validate detections. On 29 December 2023 NOAA Fisheries announced a voluntary Right Whale Slow Zone bounded by 43°49'N, 43°09'N, 67°02'W and 68°57'W. It ran 27 December 2023 through 11 January 2024. NOAA asked mariners to route around the area or transit at 10 knots or less." } }, "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": "DMON instrument running the Low-Frequency Detection and Classification System (LFDCS) on a Slocum glider" } }, "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": "Woods Hole Oceanographic Institution (Mark Baumgartner)" } }, "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": "Glider-borne automated whale-call classifier triggers a NOAA Right Whale Slow Zone east of Portland, Maine", "headline": "LFDCS glider detections trigger a NOAA Right Whale Slow Zone" } }, "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": [ "181041b30300fced8151c056ec9a4d5c663517c4a7701680671af7c2eeb1c947" ], "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 27 December 2023 the Gulf of Maine Winter Slocum glider acoustically detected North Atlantic right whales east of Portland, Maine. Woods Hole Oceanographic Institution and NOAA's Northeast Fisheries Science Center operate it. Such gliders carry a DMON instrument running the Low-Frequency Detection and Classification System (LFDCS). It matches pitch-tracked calls against a call library using quadratic discriminant function analysis. Shore-based analysts validate detections. On 29 December 2023 NOAA Fisheries announced a voluntary Right Whale Slow Zone bounded by 43°49'N, 43°09'N, 67°02'W and 68°57'W. It ran 27 December 2023 through 11 January 2024. NOAA asked mariners to route around the area or transit at 10 knots or less.", "replication": { "produced_by": [ "Glider or buoy carrying DMON/LFDCS in right whale habitat", "Shore-based analyst validation protocol", "NOAA Slow Zone program (established late 2020) accepting acoustic triggers" ], "must_hold": [ "Whales vocalize within detection range", "Mariners comply voluntarily for any protective effect to materialize" ], "echoes": [ { "date": "2024-01-20", "setting": "Southeast US waters; first definite glider-based acoustic detection there, which triggered a Southeast Early Warning System alert for vessel strike mitigation", "outcome": "recurred", "source": "Indeck et al. (2025) Oceanography, https://tos.org/oceanography/article/glider-surveillance-for-near-real-time-detection-and-spatial-management-of-north-atlantic-right-whales" }, { "setting": "US East Coast, four years of the Slow Zone program: 154 Slow Zones triggered or extended by acoustic detections, 51 by gliders", "outcome": "recurred", "source": "Indeck et al. (2025) Oceanography" } ] } } }, "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-0332", "case_url": "records/AIBD-0332.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "Alice produced the alerts on the 2,297 tenders. TCE-MG analysts forwarded 60 alerts and the audited bodies made the corrections. The AI role was detection and triage. The only sources are the deployer's own news item (TCE-MG, 27 June 2023) and Atricon's reproduction of it the next day. No audit report, tender or court decision is among the sources. TCE-MG did not build the tool (CGU/TCU did), so the statement is not vendor-authored. It is the user describing its own results." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0332", "title": "TCE-MG's Alice audit of 2,297 August 2022 tenders detects 60 irregularities worth R$113.7 million; 53 alerts answered and notices worth R$91.7 million corrected", "summary": "TCE-MG audit with Alice of 2,297 Aug 2022 tenders: 60 irregularities (R$113,713,128.26); 53 alerts answered; notices corrected worth R$91,723,011.31 (TCE-MG, 27 June 2023).", "date_observed": "2022-12-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Tribunal de Contas do Estado de Minas Gerais (TCE-MG) is one of more than ten Brazilian courts of accounts that adopted Alice. Alice is the Análise de Licitações e Editais system created by the CGU and the TCU. In December 2022 it published an audit of 2,297 municipal and state tenders held in a single month, August 2022. The exact day is not given. The court's own news item of 27 June 2023, reproduced by Atricon on 28 June 2023, gives the results. The work detected 60 irregularities involving R$113,713,128.26. 53 of the 60 alerts sent to the audited bodies received answers that allowed control verification. Irregularities were fixed in republished notices or measures adopted for future tenders. The audit achieved corrected notices worth R$91,723,011.31. The figures are aggregates presented by analysts Maria Aparecida Aiko Ikemura and Leonardo Alves Mateus to about 60 people from courts of accounts across Brazil. No individual tender is named.", "category": "protective response", "unexpected": false, "purpose": "Automated identification of tender notices with possible inconsistencies or irregularities so that the court can oversee more tenders. This use was its intended purpose.", "system": "Alice (Análise de Licitações e Editais), the CGU/TCU tender-analysis system as adopted by TCE-MG", "model_version": null, "developer": "Controladoria-Geral da União (CGU) and Tribunal de Contas da União (TCU)", "deployer": "Tribunal de Contas do Estado de Minas Gerais (TCE-MG)", "location": "Minas Gerais, Brazil", "domain": "Public administration: state external audit and procurement control", "segment": "State court of accounts (anti-waste and anti-corruption control)", "benefit_type": "Reduced Risk", "taxonomy_note": "The value corrected is that of the notices, not money saved.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual beneficiary is identified. Beneficiaries are public bodies and bidders in corrected tenders." }, { "group": "organizational", "depth": 2, "reason": "Material to the audited municipal and state bodies: 53 of 60 alerts answered and notices worth R$91,723,011.31 corrected, per the court's own aggregate figures." }, { "group": "societal", "depth": 1, "reason": "One month of tenders in one state. The statement gives no estimate of money saved, only the value of notices corrected." } ], "ai_role": { "status": "assessed", "statement": "Alice produced the alerts on the 2,297 tenders. TCE-MG analysts forwarded 60 alerts and the audited bodies made the corrections. The AI role was detection and triage. The only sources are the deployer's own news item (TCE-MG, 27 June 2023) and Atricon's reproduction of it the next day. No audit report, tender or court decision is among the sources. TCE-MG did not build the tool (CGU/TCU did), so the statement is not vendor-authored. It is the user describing its own results." }, "evidence": [ { "kind": "press release", "citation": "Tribunal de Contas do Estado de Minas Gerais (2023). Tribunal mineiro apresenta economia feita com ajuda de Alice. TCE-MG Coordenadoria de Jornalismo e Redação (Fred La Rocca), 27 Jun 2023.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "O trabalho detectou 60 irregularidades, que envolveram o valor de R$ 113.713.128,26." }, { "kind": "press release", "citation": "Tribunal de Contas do Estado de Minas Gerais (2023). Same item, result of the alerts.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice (corrections)", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "Com isso, a auditoria conseguiu a correção dos editais no valor equivalente a R$ 91.723.011,31." }, { "kind": "press release", "citation": "Appel, V. (2023). TCE-MG apresenta economia feita com ajuda de robô Alice. Atricon (Associação dos Membros dos Tribunais de Contas do Brasil), 28 Jun 2023, source TCE-MG.", "title": "TCE-MG apresenta economia feita com ajuda de robô Alice", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/", "date": "2023-06-28", "written_by": "deployer", "quote": "Em dezembro de 2022, o TCE publicou o resultado de uma auditória feita em 2.297 licitações municipais e estaduais somente em agosto daquele ano." }, { "kind": "press release", "citation": "Appel V (2024). TCE-MG apresenta robôs Alice e Solaris para o TCE-ES. Atricon, 17 January 2024.", "title": "TCE-MG apresenta robôs Alice e Solaris para o TCE-ES", "url": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "date": "2024-01-17", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Adoption of the CGU/TCU Alice system by a state court of accounts, with its own data acquisition and infrastructure", "Selection of a typology of irregularities and a designed oversight process", "Alerts sent to the audited bodies with a request for response", "Audited bodies republishing corrected notices or adopting measures for future tenders" ], "must_hold": [ "Access to state and municipal tender notices in machine-readable form", "Internal acceptance of the tool by auditors (the court cites internal resistance as a challenge)", "Audited bodies answering alerts (53 of 60 did)", "Typologies kept current so that alerts remain relevant" ], "echoes": [ { "date": "2017-09-27", "setting": "TCU (Goiás unit) use of Alice on DNIT BR-153 notices, tenders blocked by acórdão", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" }, { "date": "2024-01-17", "setting": "TCE-MG presented Alice and Solaris to TCE-ES (Espírito Santo) for adoption; no outcome reported", "model_version": null, "outcome": "unknown", "source": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" } ] }, "paired_incident": null, "headline": "Alice audit at TCE-MG corrects notices worth R$91.7 million", "metric": "the audit achieved corrections of notices worth R$91,723,011.31", "comparator": null, "later": { "status": "persisted", "date": "2024-01-17", "note": "In January 2024 TCE-MG was still running Alice and presented its methodology and results to a TCE-ES auditor who had asked to learn about the robots; adoption by TCE-ES is not reported.", "quote": "Conhecer a metodologia e resultados do TCEMG com a utilização dos robôs Solaris e Alice, bem como as ações implementadas pela Corte de Contas mineira com os dados e informações obtidos com as fiscalizações feitas pelos robôs. Com esses objetivos, o auditor Bruno Fardin Faé, do Tribunal de Contas do Espírito Santos (TCE-ES), solicitou ao TCE-MG uma reunião virtual com a equipe do Suricato, que ocorreu na manhã desta quarta-feira (16).", "url": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" }, "value": { "amount": "R$ 91.723.011,31", "what": "value of the tender notices corrected after Alice's alerts in the audit of August 2022 tenders", "who": "the audited state and municipal bodies in Minas Gerais, which republished corrected notices; the page's title calls the result a saving without saying who saved", "estimate": false, "quote": "Dos 60 alertas encaminhados aos jurisdicionados, 53 apresentaram respostas que permitiram a verificação do controle, alcançando a correção das irregularidades na republicação dos editais ou adoção de providências em futuros procedimentos licitatórios. Com isso, a auditoria conseguiu a correção dos editais equivalente a R$ 91.723.011,31.", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" }, "developer_country": "Brazil", "deployer_country": "Brazil", "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://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_do_Estado_de_Minas_Gerais", "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": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: 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-0332", "catalog_version": "1.0.0", "source_fingerprint": "9a17eefb59731218d03d721f97d64c7857a0731029460a856d0ad980c6dd8260", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0332-source-0001", "AIBD-0332-source-0002", "AIBD-0332-source-0003", "AIBD-0332-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-0332-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": "Controladoria-Geral da União (CGU) and Tribunal de Contas da União (TCU)", "deployer": "Tribunal de Contas do Estado de Minas Gerais (TCE-MG)" } }, "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": "Alice produced the alerts on the 2,297 tenders. TCE-MG analysts forwarded 60 alerts and the audited bodies made the corrections. The AI role was detection and triage. The only sources are the deployer's own news item (TCE-MG, 27 June 2023) and Atricon's reproduction of it the next day. No audit report, tender or court decision is among the sources. TCE-MG did not build the tool (CGU/TCU did), so the statement is not vendor-authored. It is the user describing its own results." } } }, "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": "Alice produced the alerts on the 2,297 tenders. TCE-MG analysts forwarded 60 alerts and the audited bodies made the corrections. The AI role was detection and triage. The only sources are the deployer's own news item (TCE-MG, 27 June 2023) and Atricon's reproduction of it the next day. No audit report, tender or court decision is among the sources. TCE-MG did not build the tool (CGU/TCU did), so the statement is not vendor-authored. It is the user describing its own results." } } }, "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. Beneficiaries are public bodies and bidders in corrected tenders." }, { "group": "organizational", "depth": 2, "reason": "Material to the audited municipal and state bodies: 53 of 60 alerts answered and notices worth R$91,723,011.31 corrected, per the court's own aggregate figures." }, { "group": "societal", "depth": 1, "reason": "One month of tenders in one state. The statement gives no estimate of money saved, only the value of notices corrected." } ] } }, "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: three press releases 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", "reason": "No individual beneficiary is identified. Beneficiaries are public bodies and bidders in corrected tenders." }, { "group": "organizational", "depth": 2, "reason": "Material to the audited municipal and state bodies: 53 of 60 alerts answered and notices worth R$91,723,011.31 corrected, per the court's own aggregate figures." }, { "group": "societal", "depth": 1, "reason": "One month of tenders in one state. The statement gives no estimate of money saved, only the value of notices corrected." } ] } }, "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. Beneficiaries are public bodies and bidders in corrected tenders." }, { "group": "organizational", "depth": 2, "reason": "Material to the audited municipal and state bodies: 53 of 60 alerts answered and notices worth R$91,723,011.31 corrected, per the court's own aggregate figures." }, { "group": "societal", "depth": 1, "reason": "One month of tenders in one state. The statement gives no estimate of money saved, only the value of notices corrected." } ] } }, "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": "R$ 91.723.011,31", "what": "value of the tender notices corrected after Alice's alerts in the audit of August 2022 tenders", "who": "the audited state and municipal bodies in Minas Gerais, which republished corrected notices; the page's title calls the result a saving without saying who saved", "estimate": false, "quote": "Dos 60 alertas encaminhados aos jurisdicionados, 53 apresentaram respostas que permitiram a verificação do controle, alcançando a correção das irregularidades na republicação dos editais ou adoção de providências em futuros procedimentos licitatórios. Com isso, a auditoria conseguiu a correção dos editais equivalente a R$ 91.723.011,31.", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" } } }, "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": "R$ 91.723.011,31", "what": "value of the tender notices corrected after Alice's alerts in the audit of August 2022 tenders", "who": "the audited state and municipal bodies in Minas Gerais, which republished corrected notices; the page's title calls the result a saving without saying who saved", "estimate": false, "quote": "Dos 60 alertas encaminhados aos jurisdicionados, 53 apresentaram respostas que permitiram a verificação do controle, alcançando a correção das irregularidades na republicação dos editais ou adoção de providências em futuros procedimentos licitatórios. Com isso, a auditoria conseguiu a correção dos editais equivalente a R$ 91.723.011,31.", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" } } }, "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": "R$ 91.723.011,31", "what": "value of the tender notices corrected after Alice's alerts in the audit of August 2022 tenders", "who": "the audited state and municipal bodies in Minas Gerais, which republished corrected notices; the page's title calls the result a saving without saying who saved", "estimate": false, "quote": "Dos 60 alertas encaminhados aos jurisdicionados, 53 apresentaram respostas que permitiram a verificação do controle, alcançando a correção das irregularidades na republicação dos editais ou adoção de providências em futuros procedimentos licitatórios. Com isso, a auditoria conseguiu a correção dos editais equivalente a R$ 91.723.011,31.", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/" } } }, "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": "the audit achieved corrections of notices worth R$91,723,011.31", "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": "2024-01-17", "note": "In January 2024 TCE-MG was still running Alice and presented its methodology and results to a TCE-ES auditor who had asked to learn about the robots; adoption by TCE-ES is not reported.", "quote": "Conhecer a metodologia e resultados do TCEMG com a utilização dos robôs Solaris e Alice, bem como as ações implementadas pela Corte de Contas mineira com os dados e informações obtidos com as fiscalizações feitas pelos robôs. Com esses objetivos, o auditor Bruno Fardin Faé, do Tribunal de Contas do Espírito Santos (TCE-ES), solicitou ao TCE-MG uma reunião virtual com a equipe do Suricato, que ocorreu na manhã desta quarta-feira (16).", "url": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" } } }, "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-0332" } }, "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 value corrected is that of the notices, not money saved." } }, "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 Tribunal de Contas do Estado de Minas Gerais (TCE-MG) is one of more than ten Brazilian courts of accounts that adopted Alice. Alice is the Análise de Licitações e Editais system created by the CGU and the TCU. In December 2022 it published an audit of 2,297 municipal and state tenders held in a single month, August 2022. The exact day is not given. The court's own news item of 27 June 2023, reproduced by Atricon on 28 June 2023, gives the results. The work detected 60 irregularities involving R$113,713,128.26. 53 of the 60 alerts sent to the audited bodies received answers that allowed control verification. Irregularities were fixed in republished notices or measures adopted for future tenders. The audit achieved corrected notices worth R$91,723,011.31. The figures are aggregates presented by analysts Maria Aparecida Aiko Ikemura and Leonardo Alves Mateus to about 60 people from courts of accounts across Brazil. No individual tender is named.", "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": "No individual beneficiary is identified. Beneficiaries are public bodies and bidders in corrected tenders." }, { "group": "organizational", "depth": 2, "reason": "Material to the audited municipal and state bodies: 53 of 60 alerts answered and notices worth R$91,723,011.31 corrected, per the court's own aggregate figures." }, { "group": "societal", "depth": 1, "reason": "One month of tenders in one state. The statement gives no estimate of money saved, only the value of notices corrected." } ] } }, "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 identification of tender notices with possible inconsistencies or irregularities so that the court can oversee more tenders. This use was its 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": "Minas Gerais, Brazil", "developer_country": "Brazil", "deployer_country": "Brazil" } }, "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 identification of tender notices with possible inconsistencies or irregularities so that the court can oversee more tenders. This use was its 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": "2022-12-01", "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://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_do_Estado_de_Minas_Gerais", "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": [ "Adoption of the CGU/TCU Alice system by a state court of accounts, with its own data acquisition and infrastructure", "Selection of a typology of irregularities and a designed oversight process", "Alerts sent to the audited bodies with a request for response", "Audited bodies republishing corrected notices or adopting measures for future tenders" ], "must_hold": [ "Access to state and municipal tender notices in machine-readable form", "Internal acceptance of the tool by auditors (the court cites internal resistance as a challenge)", "Audited bodies answering alerts (53 of 60 did)", "Typologies kept current so that alerts remain relevant" ], "echoes": [ { "date": "2017-09-27", "setting": "TCU (Goiás unit) use of Alice on DNIT BR-153 notices, tenders blocked by acórdão", "outcome": "recurred", "source": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" }, { "date": "2024-01-17", "setting": "TCE-MG presented Alice and Solaris to TCE-ES (Espírito Santo) for adoption; no outcome reported", "outcome": "unknown", "source": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" } ] } } }, "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": [ "Adoption of the CGU/TCU Alice system by a state court of accounts, with its own data acquisition and infrastructure", "Selection of a typology of irregularities and a designed oversight process", "Alerts sent to the audited bodies with a request for response", "Audited bodies republishing corrected notices or adopting measures for future tenders" ], "must_hold": [ "Access to state and municipal tender notices in machine-readable form", "Internal acceptance of the tool by auditors (the court cites internal resistance as a challenge)", "Audited bodies answering alerts (53 of 60 did)", "Typologies kept current so that alerts remain relevant" ], "echoes": [ { "date": "2017-09-27", "setting": "TCU (Goiás unit) use of Alice on DNIT BR-153 notices, tenders blocked by acórdão", "outcome": "recurred", "source": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" }, { "date": "2024-01-17", "setting": "TCE-MG presented Alice and Solaris to TCE-ES (Espírito Santo) for adoption; no outcome reported", "outcome": "unknown", "source": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" } ] } } }, "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://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Tribunal_de_Contas_do_Estado_de_Minas_Gerais", "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 administration: state external audit and procurement control", "segment": "State court of accounts (anti-waste and anti-corruption control)" } }, "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": "Tribunal de Contas do Estado de Minas Gerais (2023). Tribunal mineiro apresenta economia feita com ajuda de Alice. TCE-MG Coordenadoria de Jornalismo e Redação (Fred La Rocca), 27 Jun 2023.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "O trabalho detectou 60 irregularidades, que envolveram o valor de R$ 113.713.128,26." }, { "kind": "press release", "citation": "Tribunal de Contas do Estado de Minas Gerais (2023). Same item, result of the alerts.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice (corrections)", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "Com isso, a auditoria conseguiu a correção dos editais no valor equivalente a R$ 91.723.011,31." }, { "kind": "press release", "citation": "Appel, V. (2023). TCE-MG apresenta economia feita com ajuda de robô Alice. Atricon (Associação dos Membros dos Tribunais de Contas do Brasil), 28 Jun 2023, source TCE-MG.", "title": "TCE-MG apresenta economia feita com ajuda de robô Alice", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/", "date": "2023-06-28", "written_by": "deployer", "quote": "Em dezembro de 2022, o TCE publicou o resultado de uma auditória feita em 2.297 licitações municipais e estaduais somente em agosto daquele ano." }, { "kind": "press release", "citation": "Appel V (2024). TCE-MG apresenta robôs Alice e Solaris para o TCE-ES. Atricon, 17 January 2024.", "title": "TCE-MG apresenta robôs Alice e Solaris para o TCE-ES", "url": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "date": "2024-01-17", "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-0332-source-0001", "AIBD-0332-source-0002", "AIBD-0332-source-0003", "AIBD-0332-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": "Tribunal de Contas do Estado de Minas Gerais (2023). Tribunal mineiro apresenta economia feita com ajuda de Alice. TCE-MG Coordenadoria de Jornalismo e Redação (Fred La Rocca), 27 Jun 2023.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "O trabalho detectou 60 irregularidades, que envolveram o valor de R$ 113.713.128,26." }, { "kind": "press release", "citation": "Tribunal de Contas do Estado de Minas Gerais (2023). Same item, result of the alerts.", "title": "Tribunal mineiro apresenta economia feita com ajuda de Alice (corrections)", "url": "https://www.tce.mg.gov.br/Tribunal-mineiro-apresenta-economia-feita-com-ajuda-de-Alice.html/Noticia/1111626457", "date": "2023-06-27", "written_by": "deployer", "quote": "Com isso, a auditoria conseguiu a correção dos editais no valor equivalente a R$ 91.723.011,31." }, { "kind": "press release", "citation": "Appel, V. (2023). TCE-MG apresenta economia feita com ajuda de robô Alice. Atricon (Associação dos Membros dos Tribunais de Contas do Brasil), 28 Jun 2023, source TCE-MG.", "title": "TCE-MG apresenta economia feita com ajuda de robô Alice", "url": "https://atricon.org.br/blog/tce-mg-apresenta-economia-feita-com-ajuda-de-robo-alice/", "date": "2023-06-28", "written_by": "deployer", "quote": "Em dezembro de 2022, o TCE publicou o resultado de uma auditória feita em 2.297 licitações municipais e estaduais somente em agosto daquele ano." }, { "kind": "press release", "citation": "Appel V (2024). TCE-MG apresenta robôs Alice e Solaris para o TCE-ES. Atricon, 17 January 2024.", "title": "TCE-MG apresenta robôs Alice e Solaris para o TCE-ES", "url": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/", "date": "2024-01-17", "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": "TCE-MG audit with Alice of 2,297 Aug 2022 tenders: 60 irregularities (R$113,713,128.26); 53 alerts answered; notices corrected worth R$91,723,011.31 (TCE-MG, 27 June 2023).", "account": "The Tribunal de Contas do Estado de Minas Gerais (TCE-MG) is one of more than ten Brazilian courts of accounts that adopted Alice. Alice is the Análise de Licitações e Editais system created by the CGU and the TCU. In December 2022 it published an audit of 2,297 municipal and state tenders held in a single month, August 2022. The exact day is not given. The court's own news item of 27 June 2023, reproduced by Atricon on 28 June 2023, gives the results. The work detected 60 irregularities involving R$113,713,128.26. 53 of the 60 alerts sent to the audited bodies received answers that allowed control verification. Irregularities were fixed in republished notices or measures adopted for future tenders. The audit achieved corrected notices worth R$91,723,011.31. The figures are aggregates presented by analysts Maria Aparecida Aiko Ikemura and Leonardo Alves Mateus to about 60 people from courts of accounts across Brazil. No individual tender is named." } }, "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": "Alice (Análise de Licitações e Editais), the CGU/TCU tender-analysis system as adopted by TCE-MG" } }, "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": "Controladoria-Geral da União (CGU) and Tribunal de Contas da União (TCU)" } }, "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": "TCE-MG's Alice audit of 2,297 August 2022 tenders detects 60 irregularities worth R$113.7 million; 53 alerts answered and notices worth R$91.7 million corrected", "headline": "Alice audit at TCE-MG corrects notices worth R$91.7 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": [ "9a17eefb59731218d03d721f97d64c7857a0731029460a856d0ad980c6dd8260" ], "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 Tribunal de Contas do Estado de Minas Gerais (TCE-MG) is one of more than ten Brazilian courts of accounts that adopted Alice. Alice is the Análise de Licitações e Editais system created by the CGU and the TCU. In December 2022 it published an audit of 2,297 municipal and state tenders held in a single month, August 2022. The exact day is not given. The court's own news item of 27 June 2023, reproduced by Atricon on 28 June 2023, gives the results. The work detected 60 irregularities involving R$113,713,128.26. 53 of the 60 alerts sent to the audited bodies received answers that allowed control verification. Irregularities were fixed in republished notices or measures adopted for future tenders. The audit achieved corrected notices worth R$91,723,011.31. The figures are aggregates presented by analysts Maria Aparecida Aiko Ikemura and Leonardo Alves Mateus to about 60 people from courts of accounts across Brazil. No individual tender is named.", "replication": { "produced_by": [ "Adoption of the CGU/TCU Alice system by a state court of accounts, with its own data acquisition and infrastructure", "Selection of a typology of irregularities and a designed oversight process", "Alerts sent to the audited bodies with a request for response", "Audited bodies republishing corrected notices or adopting measures for future tenders" ], "must_hold": [ "Access to state and municipal tender notices in machine-readable form", "Internal acceptance of the tool by auditors (the court cites internal resistance as a challenge)", "Audited bodies answering alerts (53 of 60 did)", "Typologies kept current so that alerts remain relevant" ], "echoes": [ { "date": "2017-09-27", "setting": "TCU (Goiás unit) use of Alice on DNIT BR-153 notices, tenders blocked by acórdão", "outcome": "recurred", "source": "https://web.archive.org/web/20240224093756/https://inforchannel.com.br/2017/09/27/tcu-cria-robo-para-varredura-diaria-de-irregularidades-em-editais/" }, { "date": "2024-01-17", "setting": "TCE-MG presented Alice and Solaris to TCE-ES (Espírito Santo) for adoption; no outcome reported", "outcome": "unknown", "source": "https://atricon.org.br/blog/tce-mg-apresenta-robos-alice-e-solaris-para-o-tce-es/" } ] } } }, "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-0098", "case_url": "records/AIBD-0098.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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"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. 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" }, "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 typhoon housing damage per municipality to trigger pre-agreed early actions before landfall." } }, "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": "Dipaculao, Aurora Province, Philippines", "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 typhoon housing damage per municipality to trigger pre-agreed early actions before landfall." } }, "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": "2022-10-27", "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 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.", "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": [ "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": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "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": [ "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 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.", "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 and forecast-based financing", "segment": "Red Cross National Society" } }, "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": "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" } ] } }, "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). 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" } ] } }, "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-0082", "case_url": "records/AIBD-0082.html", "reason": "Specific harm prevented.", "details": [ { "label": "AI contribution", "value": "The CGU states the audits were done “com apoio da ferramenta automatizada ALICE”. At INCA, it says, the overpricing risk was identified “com a ajuda do ALICE”. For the Ipanema tender, suspension followed a CGU recommendation after analysis of the price survey. ALICE's documented role is alerting and supporting the audit; auditors and managers decided. Developer and deployer are the same public body. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, { "label": "Comparison", "value": "the original estimated value of Pregão 15/2021, which reused the price survey of Pregão 05/2021", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" }, { "label": "Before", "value": "valor estimado de R$ 34 mi" }, { "label": "After", "value": "reduziu o valor total do pregão em R$ 2.381.663,00; all items bought for R$ 14.315.793,42 on 21 July 2021" }, { "label": "Comparison basis", "value": "Measured" } ], "filters": { "category": "Specific harm prevented", "comparison": "measured" }, "record": { "id": "AIBD-0082", "title": "CGU, supported by ALICE, cuts R$2.38 million from a 2021 Hospital Federal de Ipanema glove tender and R$5.95 million from an undated INCA glove tender", "summary": "With ALICE support, CGU cut R$2.38m from a 2021 Ipanema hospital glove tender and R$5.95m from an INCA tender; R$8.33m avoided", "date_observed": "2021-07-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A CGU statement of 4 October 2023 describes five preventive audits of tenders by federal hospitals and institutes in Rio de Janeiro. The automated tool ALICE supported them. The CGU says this prevented at least R$8.33 million of overpriced purchases. At the Instituto Nacional de Câncer (INCA), a glove auction estimated at R$11 million was re-estimated at R$5.1 million after ALICE indicated prices above market. That avoided about R$5.95 million. At the Hospital Federal de Ipanema, Pregão 15/2021 for gloves (estimated R$34 million) had reused the price survey of Pregão 05/2021. On CGU's recommendation it was suspended and re-estimated, cutting R$2,381,663.00. The auction held on 21 July 2021 bought every item at or below the new estimates, totaling R$14,315,793.42.", "category": "harm prevented", "unexpected": false, "purpose": "Preventive audit of federal procurement notices using automated risk alerts to stop overpriced purchases before award", "system": "ALICE (Analisador de Licitações, Contratos e Editais), CGU automated audit-trail and AI alert tool", "model_version": null, "developer": "Controladoria-Geral da União (CGU), in-house (launched 2015 per the release)", "deployer": "Controladoria-Geral da União (CGU)", "location": "Rio de Janeiro, Brazil", "domain": "Public administration: government audit and procurement control", "segment": "Federal internal control of health-sector purchasing", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No identifiable individual beneficiary; patients benefit only indirectly through preserved hospital budgets." }, { "group": "organizational", "depth": 2, "reason": "Material to the hospitals and the federal budget: R$2,381,663.00 cut from one auction and about R$5.95 million from another. R$8.33 million avoided across the five audits per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale relative to federal purchasing of about R$119 billion a year." } ], "ai_role": { "status": "assessed", "statement": "The CGU states the audits were done “com apoio da ferramenta automatizada ALICE”. At INCA, it says, the overpricing risk was identified “com a ajuda do ALICE”. For the Ipanema tender, suspension followed a CGU recommendation after analysis of the price survey. ALICE's documented role is alerting and supporting the audit; auditors and managers decided. Developer and deployer are the same public body. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." }, "evidence": [ { "kind": "press release", "citation": "Controladoria-Geral da União (2023). CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi. gov.br/cgu, 4 Oct 2023.", "title": "CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "por recomendação da CGU, o certame foi suspenso e uma nova estimativa de preços foi realizada. A nova análise reduziu o valor total do pregão em R$ 2.381.663,00." }, { "kind": "press release", "citation": "Controladoria-Geral da União (2023). Same release, 'ALICE' section with 2023 aggregates.", "title": "CGU atua em editais de licitação em hospitais federais (ALICE section)", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "a partir de 16.743 compras analisadas no âmbito nacional, o ALICE emitiu alertas que resultaram na suspensão de 59 compras no valor aproximado de R$ 3.1 bilhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal.", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE, da Controladoria-Geral da União. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "ALICE alerts on price surveys inconsistent with prices practiced in comparable purchases in the same region", "CGU preventive audits completed before the auction, with recommendations to re-estimate prices", "Hospital managers suspending and re-running the price estimate" ], "must_hold": [ "Tender notices published on the federal purchasing portal where ALICE reads them daily", "Time between publication and auction sufficient for audit and correction", "Availability of market price references" ], "echoes": [ { "date": "2025-01-14", "setting": "Three 2024 Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2024-12-30", "setting": "2024 aggregate: 206 preventive audits opened, R$257.25 million saved (CGU, 30 December 2024)", "model_version": null, "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "101", "title": "Dutch Families Wrongfully Accused of Tax Fraud Due to Discriminatory Algorithm", "url": "https://incidentdatabase.ai/cite/101", "severity": null, "group": "organizational", "basis": "Dutch Tax Authority's fraud-risk algorithm, a government audit-targeting tool like ALICE, wrongly accused thousands of families of fraud and damaged the authority itself." }, "headline": "CGU, with ALICE support, avoids R$8.33m in overpriced purchases", "metric": "prevented at least R$8.33 million of overpriced purchases", "comparator": { "against": "the original estimated value of Pregão 15/2021, which reused the price survey of Pregão 05/2021", "before": "valor estimado de R$ 34 mi", "after": "reduziu o valor total do pregão em R$ 2.381.663,00; all items bought for R$ 14.315.793,42 on 21 July 2021", "basis": "measured", "quote": "Observou-se, no entanto, que foi utilizada a mesma pesquisa de preço do pregão de n. º 05/2021 e por recomendação da CGU, o certame foi suspenso e uma nova estimativa de preços foi realizada. A nova análise reduziu o valor total do pregão em R$ 2.381.663,00. Em 21 de julho de 2021, o pregão foi realizado e todos os itens foram adquiridos por valores unitários iguais ou inferiores aos estimados pela unidade, totalizando o montante de R$ 14.315.793,42.", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" }, "later": { "status": "scaled", "date": "2024-12-30", "note": "By the end of 2024 the CGU reported 126 bodies registered to receive ALICE alerts, including 94 state, district and municipal units, and R$257.25 million saved in concluded 2024 audits.", "quote": "Em 2024, 126 entes foram cadastrados para recebimento de alertas do robô ALICE, sendo 32 órgãos federais, 33 unidades da esfera estadual e distrital e 61 unidades da esfera municipal. Para os próximos anos, A Controladoria-Geral da União tem a perspectiva de ampliar o uso do robô ALICE para mais gestores federais, estaduais e municipais.", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" }, "value": { "amount": "R$ 8,33 milhões", "what": "overpriced hospital purchases avoided across five preventive audits", "who": "the federal government (hospitals and institutes in Rio de Janeiro)", "estimate": false, "quote": "A Controladoria-Geral da União (CGU) realizou, preventivamente, cinco auditorias em editais de contratações de hospitais federais e institutos sediados no Rio de Janeiro/RJ. A ação evitou que, pelo menos, R$ 8,33 milhões fossem desperdiçados em aquisições de insumos e serviços hospitalares com sobrepreço.", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" }, "developer_country": "Brazil", "deployer_country": "Brazil", "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 101 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/101", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attached the 2021 date only to the Ipanema Pregão 15/2021 in the title because the CGU release gives no date or tender number for the INCA glove auction; dated the Revista da CGU article 20 December 2022 and re-described it as a practitioner report (relato) in the CGU's own journal; moved the record to “harm prevented” because the CGU documents overpriced purchases under way that its ALICE-supported audits stopped before award; and set the benefit type to Trustible's “Reduced Risk” from the published framework (entry quoted in the taxonomy note; data/taxonomy.json must carry it) so that the type names the benefit instead of a closest fit.", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao; https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' 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-0082", "catalog_version": "1.0.0", "source_fingerprint": "42ab95f8d05e6b0d3a12512a6105db2fa0170e9798a3b5b43802a768a9a3651c", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0082-source-0001", "AIBD-0082-source-0002", "AIBD-0082-source-0003", "AIBD-0082-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-0082-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": "Controladoria-Geral da União (CGU), in-house (launched 2015 per the release)", "deployer": "Controladoria-Geral da União (CGU)" } }, "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 CGU states the audits were done “com apoio da ferramenta automatizada ALICE”. At INCA, it says, the overpricing risk was identified “com a ajuda do ALICE”. For the Ipanema tender, suspension followed a CGU recommendation after analysis of the price survey. ALICE's documented role is alerting and supporting the audit; auditors and managers decided. Developer and deployer are the same public body. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." } } }, "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 CGU states the audits were done “com apoio da ferramenta automatizada ALICE”. At INCA, it says, the overpricing risk was identified “com a ajuda do ALICE”. For the Ipanema tender, suspension followed a CGU recommendation after analysis of the price survey. ALICE's documented role is alerting and supporting the audit; auditors and managers decided. Developer and deployer are the same public body. The CGU page is on record as an archived copy, because the live page was restricted during Brazil's 2026 electoral period." } } }, "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 individual beneficiary; patients benefit only indirectly through preserved hospital budgets." }, { "group": "organizational", "depth": 2, "reason": "Material to the hospitals and the federal budget: R$2,381,663.00 cut from one auction and about R$5.95 million from another. R$8.33 million avoided across the five audits per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale relative to federal purchasing of about R$119 billion 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": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: the makers' 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 individual beneficiary; patients benefit only indirectly through preserved hospital budgets." }, { "group": "organizational", "depth": 2, "reason": "Material to the hospitals and the federal budget: R$2,381,663.00 cut from one auction and about R$5.95 million from another. R$8.33 million avoided across the five audits per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale relative to federal purchasing of about R$119 billion 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[].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 identifiable individual beneficiary; patients benefit only indirectly through preserved hospital budgets." }, { "group": "organizational", "depth": 2, "reason": "Material to the hospitals and the federal budget: R$2,381,663.00 cut from one auction and about R$5.95 million from another. R$8.33 million avoided across the five audits per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale relative to federal purchasing of about R$119 billion 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" }, "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": "Dutch Tax Authority's fraud-risk algorithm, a government audit-targeting tool like ALICE, wrongly accused thousands of families of fraud and damaged the authority itself.", "links": [ { "database": "AI Incident Database", "identifier": "101", "title": "Dutch Families Wrongfully Accused of Tax Fraud Due to Discriminatory Algorithm", "url": "https://incidentdatabase.ai/cite/101", "group": "organizational", "basis": "Dutch Tax Authority's fraud-risk algorithm, a government audit-targeting tool like ALICE, wrongly accused thousands of families of fraud and damaged the authority itself.", "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" }, "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": "R$ 8,33 milhões", "what": "overpriced hospital purchases avoided across five preventive audits", "who": "the federal government (hospitals and institutes in Rio de Janeiro)", "estimate": false, "quote": "A Controladoria-Geral da União (CGU) realizou, preventivamente, cinco auditorias em editais de contratações de hospitais federais e institutos sediados no Rio de Janeiro/RJ. A ação evitou que, pelo menos, R$ 8,33 milhões fossem desperdiçados em aquisições de insumos e serviços hospitalares com sobrepreço.", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } } }, "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": "R$ 8,33 milhões", "what": "overpriced hospital purchases avoided across five preventive audits", "who": "the federal government (hospitals and institutes in Rio de Janeiro)", "estimate": false, "quote": "A Controladoria-Geral da União (CGU) realizou, preventivamente, cinco auditorias em editais de contratações de hospitais federais e institutos sediados no Rio de Janeiro/RJ. A ação evitou que, pelo menos, R$ 8,33 milhões fossem desperdiçados em aquisições de insumos e serviços hospitalares com sobrepreço.", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } } }, "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": "R$ 8,33 milhões", "what": "overpriced hospital purchases avoided across five preventive audits", "who": "the federal government (hospitals and institutes in Rio de Janeiro)", "estimate": false, "quote": "A Controladoria-Geral da União (CGU) realizou, preventivamente, cinco auditorias em editais de contratações de hospitais federais e institutos sediados no Rio de Janeiro/RJ. A ação evitou que, pelo menos, R$ 8,33 milhões fossem desperdiçados em aquisições de insumos e serviços hospitalares com sobrepreço.", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi" } } }, "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": "prevented at least R$8.33 million of overpriced purchases", "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": { "paired_incident": { "database": "AI Incident Database", "identifier": "101", "title": "Dutch Families Wrongfully Accused of Tax Fraud Due to Discriminatory Algorithm", "url": "https://incidentdatabase.ai/cite/101", "group": "organizational", "basis": "Dutch Tax Authority's fraud-risk algorithm, a government audit-targeting tool like ALICE, wrongly accused thousands of families of fraud and damaged the authority itself." } } }, "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": "2024-12-30", "note": "By the end of 2024 the CGU reported 126 bodies registered to receive ALICE alerts, including 94 state, district and municipal units, and R$257.25 million saved in concluded 2024 audits.", "quote": "Em 2024, 126 entes foram cadastrados para recebimento de alertas do robô ALICE, sendo 32 órgãos federais, 33 unidades da esfera estadual e distrital e 61 unidades da esfera municipal. Para os próximos anos, A Controladoria-Geral da União tem a perspectiva de ampliar o uso do robô ALICE para mais gestores federais, estaduais e municipais.", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" } } }, "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-0082" } }, "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": "A CGU statement of 4 October 2023 describes five preventive audits of tenders by federal hospitals and institutes in Rio de Janeiro. The automated tool ALICE supported them. The CGU says this prevented at least R$8.33 million of overpriced purchases. At the Instituto Nacional de Câncer (INCA), a glove auction estimated at R$11 million was re-estimated at R$5.1 million after ALICE indicated prices above market. That avoided about R$5.95 million. At the Hospital Federal de Ipanema, Pregão 15/2021 for gloves (estimated R$34 million) had reused the price survey of Pregão 05/2021. On CGU's recommendation it was suspended and re-estimated, cutting R$2,381,663.00. The auction held on 21 July 2021 bought every item at or below the new estimates, totaling R$14,315,793.42.", "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": "No identifiable individual beneficiary; patients benefit only indirectly through preserved hospital budgets." }, { "group": "organizational", "depth": 2, "reason": "Material to the hospitals and the federal budget: R$2,381,663.00 cut from one auction and about R$5.95 million from another. R$8.33 million avoided across the five audits per the CGU." }, { "group": "societal", "depth": 1, "reason": "Small at national scale relative to federal purchasing of about R$119 billion 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" }, "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": "Preventive audit of federal procurement notices using automated risk alerts to stop overpriced purchases before award" } }, "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": "Rio de Janeiro, Brazil", "developer_country": "Brazil", "deployer_country": "Brazil" } }, "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": "Preventive audit of federal procurement notices using automated risk alerts to stop overpriced purchases before award" } }, "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": "2021-07-21", "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 101 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/101", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attached the 2021 date only to the Ipanema Pregão 15/2021 in the title because the CGU release gives no date or tender number for the INCA glove auction; dated the Revista da CGU article 20 December 2022 and re-described it as a practitioner report (relato) in the CGU's own journal; moved the record to “harm prevented” because the CGU documents overpriced purchases under way that its ALICE-supported audits stopped before award; and set the benefit type to Trustible's “Reduced Risk” from the published framework (entry quoted in the taxonomy note; data/taxonomy.json must carry it) so that the type names the benefit instead of a closest fit.", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao; https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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": [ "ALICE alerts on price surveys inconsistent with prices practiced in comparable purchases in the same region", "CGU preventive audits completed before the auction, with recommendations to re-estimate prices", "Hospital managers suspending and re-running the price estimate" ], "must_hold": [ "Tender notices published on the federal purchasing portal where ALICE reads them daily", "Time between publication and auction sufficient for audit and correction", "Availability of market price references" ], "echoes": [ { "date": "2025-01-14", "setting": "Three 2024 Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2024-12-30", "setting": "2024 aggregate: 206 preventive audits opened, R$257.25 million saved (CGU, 30 December 2024)", "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" } ] } } }, "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": [ "ALICE alerts on price surveys inconsistent with prices practiced in comparable purchases in the same region", "CGU preventive audits completed before the auction, with recommendations to re-estimate prices", "Hospital managers suspending and re-running the price estimate" ], "must_hold": [ "Tender notices published on the federal purchasing portal where ALICE reads them daily", "Time between publication and auction sufficient for audit and correction", "Availability of market price references" ], "echoes": [ { "date": "2025-01-14", "setting": "Three 2024 Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2024-12-30", "setting": "2024 aggregate: 206 preventive audits opened, R$257.25 million saved (CGU, 30 December 2024)", "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" } ] } } }, "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 101 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/101", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Attached the 2021 date only to the Ipanema Pregão 15/2021 in the title because the CGU release gives no date or tender number for the INCA glove auction; dated the Revista da CGU article 20 December 2022 and re-described it as a practitioner report (relato) in the CGU's own journal; moved the record to “harm prevented” because the CGU documents overpriced purchases under way that its ALICE-supported audits stopped before award; and set the benefit type to Trustible's “Reduced Risk” from the published framework (entry quoted in the taxonomy note; data/taxonomy.json must carry it) so that the type names the benefit instead of a closest fit.", "source": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Benefit type kept within Trustible's six types (Improved Organizational Efficiency); the sweep's proposed “Reduced Risk” is not in the taxonomy.", "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://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi; https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao; https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "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://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o; https://pt.wikipedia.org/wiki/Controladoria-Geral_da_Uni%C3%A3o", "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": "Public administration: government audit and procurement control", "segment": "Federal internal control of health-sector purchasing" } }, "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": "Controladoria-Geral da União (2023). CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi. gov.br/cgu, 4 Oct 2023.", "title": "CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "por recomendação da CGU, o certame foi suspenso e uma nova estimativa de preços foi realizada. A nova análise reduziu o valor total do pregão em R$ 2.381.663,00." }, { "kind": "press release", "citation": "Controladoria-Geral da União (2023). Same release, 'ALICE' section with 2023 aggregates.", "title": "CGU atua em editais de licitação em hospitais federais (ALICE section)", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "a partir de 16.743 compras analisadas no âmbito nacional, o ALICE emitiu alertas que resultaram na suspensão de 59 compras no valor aproximado de R$ 3.1 bilhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal.", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE, da Controladoria-Geral da União. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "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-0082-source-0001", "AIBD-0082-source-0002", "AIBD-0082-source-0003", "AIBD-0082-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": "Controladoria-Geral da União (2023). CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi. gov.br/cgu, 4 Oct 2023.", "title": "CGU atua em editais de licitação em hospitais federais no Rio de Janeiro e evita desperdício de cerca de R$ 8,33 mi", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "por recomendação da CGU, o certame foi suspenso e uma nova estimativa de preços foi realizada. A nova análise reduziu o valor total do pregão em R$ 2.381.663,00." }, { "kind": "press release", "citation": "Controladoria-Geral da União (2023). Same release, 'ALICE' section with 2023 aggregates.", "title": "CGU atua em editais de licitação em hospitais federais (ALICE section)", "url": "https://web.archive.org/web/20260412063523id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2023/10/cgu-atua-em-editais-de-licitacao-em-hospitais-federais-no-rio-de-janeiro-e-evita-desperdicio-de-cerca-de-r-8-33-mi", "date": "2023-10-04", "written_by": "makers", "quote": "a partir de 16.743 compras analisadas no âmbito nacional, o ALICE emitiu alertas que resultaram na suspensão de 59 compras no valor aproximado de R$ 3.1 bilhões" }, { "kind": "report", "citation": "Oliveira, T. C., Rocha, A. L. M. & Rezende, M. S. (2022). Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua de compras públicas governamentais com uso de inteligência artificial. Revista da CGU 14(26), jul-dez/2022, published 20 Dec 2022; a practitioner report (relato) in the CGU's own journal.", "title": "Alice: Desafios, resultados e perspectivas da ferramenta de auditoria contínua", "url": "https://revista.cgu.gov.br/Revista_da_CGU/article/view/530", "date": "2022-12-20", "written_by": "makers", "quote": "Já são mais de R$ 9,7 bilhões em compras suspensas a partir de suas indicações, apenas pela CGU" }, { "kind": "press release", "citation": "Controladoria-Geral da União (2024). Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE, da Controladoria-Geral da União. gov.br/cgu, 30 Dec 2024.", "title": "Órgãos federais economizaram, em 2024, 257 milhões de reais com apoio do Robô ALICE", "url": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao", "date": "2024-12-30", "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": "With ALICE support, CGU cut R$2.38m from a 2021 Ipanema hospital glove tender and R$5.95m from an INCA tender; R$8.33m avoided", "account": "A CGU statement of 4 October 2023 describes five preventive audits of tenders by federal hospitals and institutes in Rio de Janeiro. The automated tool ALICE supported them. The CGU says this prevented at least R$8.33 million of overpriced purchases. At the Instituto Nacional de Câncer (INCA), a glove auction estimated at R$11 million was re-estimated at R$5.1 million after ALICE indicated prices above market. That avoided about R$5.95 million. At the Hospital Federal de Ipanema, Pregão 15/2021 for gloves (estimated R$34 million) had reused the price survey of Pregão 05/2021. On CGU's recommendation it was suspended and re-estimated, cutting R$2,381,663.00. The auction held on 21 July 2021 bought every item at or below the new estimates, totaling R$14,315,793.42." } }, "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": "ALICE (Analisador de Licitações, Contratos e Editais), CGU automated audit-trail and AI alert tool" } }, "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": "Controladoria-Geral da União (CGU), in-house (launched 2015 per the release)" } }, "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": "CGU, supported by ALICE, cuts R$2.38 million from a 2021 Hospital Federal de Ipanema glove tender and R$5.95 million from an undated INCA glove tender", "headline": "CGU, with ALICE support, avoids R$8.33m in overpriced purchases" } }, "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": [ "42ab95f8d05e6b0d3a12512a6105db2fa0170e9798a3b5b43802a768a9a3651c" ], "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 CGU statement of 4 October 2023 describes five preventive audits of tenders by federal hospitals and institutes in Rio de Janeiro. The automated tool ALICE supported them. The CGU says this prevented at least R$8.33 million of overpriced purchases. At the Instituto Nacional de Câncer (INCA), a glove auction estimated at R$11 million was re-estimated at R$5.1 million after ALICE indicated prices above market. That avoided about R$5.95 million. At the Hospital Federal de Ipanema, Pregão 15/2021 for gloves (estimated R$34 million) had reused the price survey of Pregão 05/2021. On CGU's recommendation it was suspended and re-estimated, cutting R$2,381,663.00. The auction held on 21 July 2021 bought every item at or below the new estimates, totaling R$14,315,793.42.", "replication": { "produced_by": [ "ALICE alerts on price surveys inconsistent with prices practiced in comparable purchases in the same region", "CGU preventive audits completed before the auction, with recommendations to re-estimate prices", "Hospital managers suspending and re-running the price estimate" ], "must_hold": [ "Tender notices published on the federal purchasing portal where ALICE reads them daily", "Time between publication and auction sufficient for audit and correction", "Availability of market price references" ], "echoes": [ { "date": "2025-01-14", "setting": "Three 2024 Rio federal hospital tenders suspended, revoked or re-estimated after ALICE alerts (CGU, 14 January 2025)", "outcome": "recurred", "source": "https://web.archive.org/web/20250114172015id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/atuacao-do-robo-alice-segue-auxiliando-auditores-a-realizar-trabalhos-preventivos-que-ajudam-a-evitar-desperdicios-e-irregularidades/" }, { "date": "2024-12-30", "setting": "2024 aggregate: 206 preventive audits opened, R$257.25 million saved (CGU, 30 December 2024)", "outcome": "recurred", "source": "https://web.archive.org/web/20260207114437id_/https://www.gov.br/cgu/pt-br/assuntos/noticias/2024/12/orgaos-federais-economizaram-em-2024-257-milhoes-de-reais-com-apoio-do-robo-alice-da-controladoria-geral-da-uniao" } ] } } }, "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": 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-0103", "case_url": "records/AIBD-0103.html", "reason": "Protective response, prevention unproven.", "details": [ { "label": "AI contribution", "value": "The learned forecast model identifies the nights on which alerts are issued. The proclamation and alert program are documented by the operating partner, not the model's makers. The collision-reduction evidence comes from a separate Chicago study co-authored by BirdCast scientists and is not specific to Houston. The counterfactual for Houston is unproven." } ], "filters": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "record": { "id": "AIBD-0103", "title": "BirdCast forecast alerts underpin Houston's Lights Out Nights proclamation", "summary": "Houston Audubon issues Lights Out alerts from BirdCast forecasts; City of Houston proclaimed Lights Out Nights on 9 April 2021.", "date_observed": "2021-04-09", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "BirdCast is produced by the Cornell Lab of Ornithology with Colorado State University and now Purdue University and the University of Illinois. It forecasts nocturnal bird migration from a model trained on 23 years of weather-radar bird detections and reanalysis weather data. Houston Audubon states its staff use the BirdCast forecast maps to track migration predictions. They issue Lights Out Action Alerts when a chance of high migration coincides with weather events. Alerts ask residents and businesses to turn off nonessential lights from 11 p.m. to 6 a.m. during spring and fall migration. On 9 April 2021 the City of Houston proclaimed “Lights Out Nights” to help migrating birds. No Houston collision reduction is documented. A separate 21-year Chicago study co-authored by BirdCast scientists estimated halving lighted window area cuts collisions 11-fold in spring and 6-fold in fall.", "category": "protective response", "unexpected": false, "purpose": "Forecast nightly bird migration intensity from weather radar and weather models so that cities, building owners and residents can reduce lighting on high-migration nights.", "system": "BirdCast migration forecast model and Lights Out Action Alerts", "model_version": null, "developer": "Cornell Lab of Ornithology, Colorado State University (forecasts now produced with Purdue University and University of Illinois)", "deployer": "Houston Audubon (Birds Houston) and City of Houston", "location": "Houston, United States", "domain": "Conservation and biodiversity", "segment": "Migratory bird collision prevention / urban lighting policy", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Residents and building managers receive timed alerts, but no individual outcome is documented." }, { "group": "organizational", "depth": 1, "reason": "A municipal proclamation and partner alert program were adopted; the partner page reports no measured change in collisions at Houston buildings." }, { "group": "societal", "depth": null, "reason": "The collision evidence is a Chicago study co-authored by BirdCast scientists; Houston's own collision change is unmeasured." } ], "ai_role": { "status": "assessed", "statement": "The learned forecast model identifies the nights on which alerts are issued. The proclamation and alert program are documented by the operating partner, not the model's makers. The collision-reduction evidence comes from a separate Chicago study co-authored by BirdCast scientists and is not specific to Houston. The counterfactual for Houston is unproven." }, "evidence": [ { "kind": "press release", "citation": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "date": null, "written_by": "deployer", "quote": "April 9, 2021: City of Houston proclaims 'Lights Out Nights' in Houston to help migrating birds traveling through the region" }, { "kind": "press release", "citation": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "date": null, "written_by": "deployer", "quote": "our staff were able to track migration predictions for the region and issue alerts to the community when a chance of high migration coincided with weather events" }, { "kind": "press release", "citation": "BirdCast (n.d.). Migration forecast maps. birdcast.org.", "title": "Migration forecast maps", "url": "https://birdcast.org/migration-tools/migration-forecast-maps/", "date": null, "written_by": "makers", "quote": "By learning associations between weather variables and migration, we can use the model to predict a bird migration intensity given a particular weather forecast." }, { "kind": "peer-reviewed study", "citation": "Van Doren, B. M., Willard, D. E., Hennen, M., Horton, K. G., et al. (2021). Drivers of fatal bird collisions in an urban center. 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lights off 11 p.m.–6 a.m. on a seasonal schedule, BirdCast referenced as a resource", "outcome": "unknown", "source": "https://raleighnc.gov/climate-action-and-sustainability/news/raleigh-joins-lights-out-bird-migration" } ] } } }, "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": [ "Continental weather-radar archive to train the forecast", "A local partner that translates forecasts into alerts", "Municipal endorsement of lights-out practice" ], "must_hold": [ "Forecast skill on peak nights", "Building managers actually act on alerts", "Monitoring to measure collisions before and after" ], "echoes": [ { "date": "2026-08-27", "setting": "City of Raleigh joins Lights Out; lights off 11 p.m.–6 a.m. on a seasonal schedule, BirdCast referenced as a resource", "outcome": "unknown", "source": "https://raleighnc.gov/climate-action-and-sustainability/news/raleigh-joins-lights-out-bird-migration" } ] } } }, "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": "Reworded the account so that Houston Audubon's alerts go out when a chance of high migration coincides with weather events, as its page says, and described the Chicago collision study as co-authored by BirdCast scientists rather than independent, matching the PNAS author affiliations and the record's own authorship marking.", "source": "https://birdshouston.org/conservation/lights-out.html; https://pmc.ncbi.nlm.nih.gov/articles/PMC8214683/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since from the cited sources.", "source": "https://birdshouston.org/conservation/lights-out.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://en.wikipedia.org/wiki/Cornell_Lab_of_Ornithology; https://birdshouston.org/", "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": "Conservation and biodiversity", "segment": "Migratory bird collision prevention / urban lighting policy" } }, "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": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "written_by": "deployer", "quote": "April 9, 2021: City of Houston proclaims 'Lights Out Nights' in Houston to help migrating birds traveling through the region" }, { "kind": "press release", "citation": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "written_by": "deployer", "quote": "our staff were able to track migration predictions for the region and issue alerts to the community when a chance of high migration coincided with weather events" }, { "kind": "press release", "citation": "BirdCast (n.d.). Migration forecast maps. birdcast.org.", "title": "Migration forecast maps", "url": "https://birdcast.org/migration-tools/migration-forecast-maps/", "written_by": "makers", "quote": "By learning associations between weather variables and migration, we can use the model to predict a bird migration intensity given a particular weather forecast." }, { "kind": "peer-reviewed study", "citation": "Van Doren, B. M., Willard, D. E., Hennen, M., Horton, K. G., et al. (2021). Drivers of fatal bird collisions in an urban center. PNAS 118(24).", "title": "Drivers of fatal bird collisions in an urban center", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8214683/", "date": "2021-06-07", "written_by": "makers", "quote": "We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall." } ] } }, "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-0103-source-0001", "AIBD-0103-source-0002", "AIBD-0103-source-0003", "AIBD-0103-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": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "written_by": "deployer", "quote": "April 9, 2021: City of Houston proclaims 'Lights Out Nights' in Houston to help migrating birds traveling through the region" }, { "kind": "press release", "citation": "Houston Audubon / Birds Houston (n.d.). Lights Out for Birds. birdshouston.org.", "title": "Lights Out for Birds", "url": "https://birdshouston.org/conservation/lights-out.html", "written_by": "deployer", "quote": "our staff were able to track migration predictions for the region and issue alerts to the community when a chance of high migration coincided with weather events" }, { "kind": "press release", "citation": "BirdCast (n.d.). Migration forecast maps. birdcast.org.", "title": "Migration forecast maps", "url": "https://birdcast.org/migration-tools/migration-forecast-maps/", "written_by": "makers", "quote": "By learning associations between weather variables and migration, we can use the model to predict a bird migration intensity given a particular weather forecast." }, { "kind": "peer-reviewed study", "citation": "Van Doren, B. M., Willard, D. E., Hennen, M., Horton, K. G., et al. (2021). Drivers of fatal bird collisions in an urban center. PNAS 118(24).", "title": "Drivers of fatal bird collisions in an urban center", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8214683/", "date": "2021-06-07", "written_by": "makers", "quote": "We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall." } ] } }, "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": "Houston Audubon issues Lights Out alerts from BirdCast forecasts; City of Houston proclaimed Lights Out Nights on 9 April 2021.", "account": "BirdCast is produced by the Cornell Lab of Ornithology with Colorado State University and now Purdue University and the University of Illinois. It forecasts nocturnal bird migration from a model trained on 23 years of weather-radar bird detections and reanalysis weather data. Houston Audubon states its staff use the BirdCast forecast maps to track migration predictions. They issue Lights Out Action Alerts when a chance of high migration coincides with weather events. Alerts ask residents and businesses to turn off nonessential lights from 11 p.m. to 6 a.m. during spring and fall migration. On 9 April 2021 the City of Houston proclaimed “Lights Out Nights” to help migrating birds. No Houston collision reduction is documented. A separate 21-year Chicago study co-authored by BirdCast scientists estimated halving lighted window area cuts collisions 11-fold in spring and 6-fold in fall." } }, "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": "BirdCast migration forecast model and Lights Out Action 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": "Cornell Lab of Ornithology, Colorado State University (forecasts now produced with Purdue University and University of Illinois)" } }, "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": "BirdCast forecast alerts underpin Houston's Lights Out Nights proclamation", "headline": "BirdCast forecasts underpin Houston's Lights Out Nights" } }, "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": [ "64721b437e1ee54d480ed358ebeb277ab8816c11ea5d66523e905862833fdcff" ], "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": "BirdCast is produced by the Cornell Lab of Ornithology with Colorado State University and now Purdue University and the University of Illinois. It forecasts nocturnal bird migration from a model trained on 23 years of weather-radar bird detections and reanalysis weather data. Houston Audubon states its staff use the BirdCast forecast maps to track migration predictions. They issue Lights Out Action Alerts when a chance of high migration coincides with weather events. Alerts ask residents and businesses to turn off nonessential lights from 11 p.m. to 6 a.m. during spring and fall migration. On 9 April 2021 the City of Houston proclaimed “Lights Out Nights” to help migrating birds. No Houston collision reduction is documented. A separate 21-year Chicago study co-authored by BirdCast scientists estimated halving lighted window area cuts collisions 11-fold in spring and 6-fold in fall.", "replication": { "produced_by": [ "Continental weather-radar archive to train the forecast", "A local partner that translates forecasts into alerts", "Municipal endorsement of lights-out practice" ], "must_hold": [ "Forecast skill on peak nights", "Building managers actually act on alerts", "Monitoring to measure collisions before and after" ], "echoes": [ { "date": "2026-08-27", "setting": "City of Raleigh joins Lights Out; lights off 11 p.m.–6 a.m. on a seasonal schedule, BirdCast referenced as a resource", "outcome": "unknown", "source": "https://raleighnc.gov/climate-action-and-sustainability/news/raleigh-joins-lights-out-bird-migration" } ] } } }, "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-0347", "case_url": "records/AIBD-0347.html", "reason": "Protective response, prevention unproven.", "details": [ { "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": { "category": "Protective response, prevention unproven", "comparison": "undocumented" }, "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. 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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 } } } } } ] }